Clogging mechanisms in Managed Aquifer Recharge: a case ... · water scarcity increase pressure on future global water resources. While MAR has been While MAR has been primarily utilised

Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C

Lily Smith (21224043)

Coordinating Supervisor:

Associate Professor Ryan Vogwill, School of Earth and Environment, UWA

Co-supervisor:

Jed Youngs, Manager Hydrology, BHP Billiton West Australian Iron Ore

Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C SCIE9722 FNAS Master of Science Thesis

Lily Smith 208262

This thesis is submitted to fulfill the requirements for Master of Science (School of Earth and

Environment) by way of Thesis and Coursework

Faculty of Science

The University of Western Australia

May 2014

ii

Table of Contents

List of Figures .......................................................................................................................... vi

List of Tables ........................................................................................................................... ix

Abbreviations ........................................................................................................................... x

Abstract ................................................................................................................................... 11

1 Introduction .................................................................................................................... 12

1.1 Overview .................................................................................................................. 12

1.2 Aims ......................................................................................................................... 13

1.3 System Characterisation ........................................................................................... 13

1.3.1 Site Description .................................................................................................... 13

1.3.2 MAR System Characterisation ............................................................................. 14

1.3.3 Climate Characterisation ..................................................................................... 16

1.3.4 Hydrogeological Characterisation ...................................................................... 17

2 Literature Review .......................................................................................................... 21

2.1 Managed Aquifer Recharge ..................................................................................... 21

2.1.1 Definition ............................................................................................................. 21

2.1.2 Purpose ................................................................................................................ 21

2.1.3 Types .................................................................................................................... 22

2.1.4 Benefits ................................................................................................................. 22

2.2 Clogging ................................................................................................................... 22

2.2.1 Introduction .......................................................................................................... 23

2.2.2 Types and Causes ................................................................................................. 23

2.2.3 Management Options ........................................................................................... 25

2.2.4 Diagnostic Tools .................................................................................................. 25

2.3 Application of MAR in the Mining Industry ........................................................... 26

2.3.1 Mining Water Management ................................................................................. 26

2.3.2 MAR Schemes in the Pilbara Region ................................................................... 28

2.3.3 Mining Specific Operational Considerations ...................................................... 28

3 Materials and methods .................................................................................................. 29

3.1 Aquifer Response ..................................................................................................... 29

3.1.1 Hydrographs ........................................................................................................ 29

iii

3.1.2 Mounding .............................................................................................................. 29

3.2 Operational Performance .......................................................................................... 29

3.2.1 Aquifer vs In-Well Groundwater Level ................................................................. 29

3.2.2 Specific Injectivity................................................................................................. 30

3.2.3 Well Efficiency ...................................................................................................... 32

3.3 Clogging Diagnostics ............................................................................................... 34

3.3.1 Graphical Tool ..................................................................................................... 34

3.3.2 Water Quality Analysis ......................................................................................... 34

3.3.3 Bore Casing Image Analysis ................................................................................ 34

3.3.4 Saturation Index ................................................................................................... 35

4 Results .............................................................................................................................. 37


4.1.1 Hydrographs ......................................................................................................... 37

4.1.2 Mounding .............................................................................................................. 38


4.2.1 Aquifer vs In-Well Groundwater Level ................................................................. 40

4.2.2 Specific Injectivity................................................................................................. 41

4.2.3 Well Efficiency ...................................................................................................... 44


4.3.1 Graphical Tool ..................................................................................................... 46

4.3.2 Water Quality Analysis ......................................................................................... 46



5 Discussion ........................................................................................................................ 51




6 Conclusions ..................................................................................................................... 56

7 References........................................................................................................................ 58

Appendix A – Introduction .................................................................................................. A.1

Appendix B - Literature Review ......................................................................................... B.1

Appendix C – Materials and Methods ................................................................................ C.1

Appendix D - Results ............................................................................................................ D.1

iv

Appendix E – Research Proposal ........................................................................................ E.1

v

Acknowledgements

I would like to thank my supervisors Dr Ryan Vogwill (UWA) and Jed Youngs (BHPB) for

their time and effort in guiding me through this thesis; their ongoing support and advice were

very much appreciated and the project would not have come to fruition without it.

Many thanks to Pierre Rousseau from WAFE Pty Ltd for his technical guidance and support

with the hydrogeochemistry modeling software PHREEQC.

The support of BHP Billiton West Australian Iron Ore for making the project data available

for this research is much appreciated.

vi

List of Figures

Figure 1 Mining Area C location and site layout, showing the components of the MAR

system....................................................................................................................................... 14

Figure 2 MAR schematic diagram showing the seven system components of an MAR project.

.................................................................................................................................................. 16

Figure 3 Historic monthly rainfall record at Flat Rocks gauge, 20km north of MAC............. 17

Figure 4 Geological cross section for HGA0001P looking east along 709317E (Central

Pilbara Grid), unit codes from Table 2 .................................................................................... 18


Pilbara Grid) , unit codes from Table 2 ................................................................................... 18

Figure 6 Geological cross section for HGA0002P looking east along 709770 (Central Pilbara

Grid), , unit codes from Table 2 ............................................................................................... 19

Figure 7 A graphical diagnostic tool for determining different mechanisms of clogging ....... 24

Figure 8 Hydrograph at observation bore GWB0012M showing a declining regional

groundwater trend prior to injection (1997 – 2012) ................................................................. 32

Figure 9 An example of a 1m section showing both the raw and enhanced OTV image ........ 35

Figure 10 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0001P 37



Figure 13 Pre-injection groundwater surface in mRL from the 09/02/2012 (0.1m contour

intervals)................................................................................................................................... 39

Figure 14 Post-injection groundwater surface in mRL from the 11/4/2013 (0.1m contour

intervals)................................................................................................................................... 39

Figure 15 Groundwater mounding in m following 12-months of injection (0.1m contour

intervals)................................................................................................................................... 40

vii

Figure 16 Plot of the difference between the injection bore and the closest observation bores

groundwater levels .................................................................................................................... 40

Figure 17 Long-term specific Injectivity, SiL over time for each injection bore ..................... 42

Figure 18 Short-term specific injectivity, SIS over time for each injection bore ..................... 43

Figure 19 The cumulative deviation from mean rainfall plot showing a dry period prior to the

commencement of the groundwater injection trial ................................................................... 44

Figure 20 Matching theCDFM plot with the GWB0012M hydrograph to determine a

relationship ............................................................................................................................... 44

Figure 21 Well efficiency (%) showing results for both pre-injection (dark) and post-injection

(light) datasets for HGA0001P (diamonds), HGA0002) (squares) and HGA0003P (triangles).

.................................................................................................................................................. 46

Figure 22 Flow resistance in terms of in-well mounding overlain with the standard curves. .. 47

Figure 23 Field measured turbidity for HGA0001P and HGA0002P (HGA0003P not

available) showing the target of <5 NTU ................................................................................. 48

Figure 24 Bar chart showing the percentage of biofouling on the slotted PVC screens from the

image analysis for the pre-injection, post-injection and post-test pumping scenarios ............. 48

Figure 25 Percentage of biofouling with depth for HGA0001P, HGA0002P and HGA0003P

showing the slotted PVC and stainless steel wire wound screen sections................................ 49

Figure 26 Saturation Index (SI) versus time for carbonate minerals (calcite and dolomite) .... 50

Figure 27 Saturation Index (SI) versus time for sulphate minerals (bartite, gypsum and

anhydrite) .................................................................................................................................. 51

Figure A-1 Location of Flat Rocks rainfall gauge (20km north of Mining Area C), DoW site

505011 .................................................................................................................................... A.2

Figure A-2 Stratigraphic Units of the Hamersley Region. ................................................ A.3

Figure A-3 Construction bore log for HGA0001 (TP4) ......................................................... A.4

Figure A-4 Construction bore log for HGA0002 (TP5) .................................................... A.5

viii

Figure A-5 Construction bore log for HGA0003P (TP3) ...................................................... A.6

Figure A-6 Local 20k geology interpretation at the injection site, A Deposit, Mining Area C..

................................................................................................................................................ A.7

Figure A-7 MAR conceptual model....................................................................................... A.9

Figure B-1 Types of MAR schemes .................................................................................. B.3

Figure C-1 Casing Image Analysis: an example of the Image Histogram, which counts the

number of pixels of given colour (Index: Black = 0), to determine the percentage of

biofouling. ............................................................................................................................... C.2

Figure C-2 Saturation Index modeling: field-determined pH versus laboratory determined pH

................................................................................................................................................. C.3

Figure C-3 Saturation Index modeling: field determined pH versus time ............................. C.3

Figure D-1 The Hantush-Bierschenk method for determining ∆S ........................................ D.2

Figure D-2 The Hantush-Bierschenk method for determining parameters B and C ............. D.6

Figure D-3 The reduction in well efficiency (EW) between pre-injection and post-injection

data ......................................................................................................................................... D.9

Figure D-4 Results of the sensitivity analysis where the well efficiency at each step is

calculated using a range of P values .................................................................................... D.10

Figure D-5 Sensitivity Analysis for Saturation Index (SI) with respect to temperature for

carbonates and sulphates ...................................................................................................... D.17

ix

List of Tables

Table 1 MAR bore details (Refer to Table 2 for codes of screened units) ............................... 15

Table 2 Geological reference table for unit codes .................................................................... 20

Table 4 Static Water level for the injection bores recorded on 9/2/12 ..................................... 31

Table 5 Details of the step-drawdown tests .............................................................................. 32

Table 6 Static Water level for the injection bores recorded on 9/2/12 ..................................... 34

Table 7 Dissolution reactions and ion activity products (IAP) for minerals included in the

analysis ..................................................................................................................................... 36

Table 8 Jacobs equation coefficients determined using the Hantush-Bierschenk method ....... 45

Table 9 Assumptions of the Huntush-Bierschenk method ....................................................... 54

Table A-1 Name and description of geology codes used in. ................................................. A.8

Table B-1 Examples of MAR systems ................................................................................ B.2

Table C-1 Casing Image Analysis: The colour settings used in the downhole camera study for

each of the images. Note the HGA0003P pre-injection image quality was too poor to analyse.

................................................................................................................................................ C.2

Table D-1 The Hantush-Bierschenk method for determining Sw(n)/Qn .................................. D.5

Table D-2 Water quality sample results undertaken during injection with the ANZEC 2000

Guideline values; grey indicates that the analyte is below the detectible limit .................... D.12

Table D-3 Image quality estimates and comments on image reliability for the downhole

camera study for each 1m section of HGA0001P, HGA0002P and HGA0003P. ............... D.13

Table D-4 Key inputs and outputs for the PHREEQC modeling (red = average values used in

the absence of measured values) .......................................................................................... D.16

x

Abbreviations

AOC Assimilable Organic Carbon

ASR Aquifer Storage and Recovery

ASTR Aquifer Storage, Transport and Recovery

BIF Banded Iron Formation

BOM Bureau of Meterology

BWT Below Water Table

DEC Department of Conservation

DOC Dissolved Organic Carbon

DoW Department of Water

FMG Fortescue Metals Group

GDE Groundwater Dependent Ecosystems

IGRAC International Groundwater Resource Assessment Centre

MAC Mining Area C

MAR Managed Aquifer Recharge

mbgl meters below ground level

MFI Membrane Filtration Index

NTU Nephelometric Turbidity Units

NWQMS National Water Quality Management Strategy

ORP Oxidation-Reduction Potential

OSA Overburden Storage Area

PFI Parallel Filter Index

PHREEQC PH (pH), RE (redox), EQ (equilibrium), C (programming language)

PVC Polyvinyl chloride

SWL Static Water Level

TD Tertiary Detritals

TOC Total Organic Carbon

TSS Total Suspended Solids

VWP Vibrating Wire Piezometer

11

Abstract

Managed Aquifer Recharge or MAR is a well-established method of sustainable water

management. The changing climate, growing population, effects of urbanisation and surface

water scarcity increase pressure on future global water resources. While MAR has been

primarily utilised for potable and agricultural purposes in the past, it has future potential to

form an integral part of water management in the mining industry. It is an effective and

sustainable method for disposing of surplus water in an operational mining capacity and can

reduce the long term dewatering footprint of below water table deposits. Clogging of the

injection well and surrounding aquifer matrix is a common operational issue that results in

reduced permeability of injection surfaces. Clogging can occur via physical, chemical or

biological mechanisms or a combination thereof. The MAR scheme at BHP Billiton‟s Mining

Area C in the central Pilbara was used as a case study to investigate the potential for clogging

mechanisms to affect operational performance.

The MAR system at MAC injects surplus dewatering supply from mineralised iron-orebody

aquifers into the regional karstic dolomite aquifer. The trial commenced in April 2012 and

available data was analysed until April 2013. The regional aquifer showed a maximum 5m

increase in groundwater levels in response to the trial. The operational performance of the

injection bores decreased throughout the study period and one injection bore was shut down

prematurely due to poor performance. The well efficiency significantly decreased in two of

the three bores as a result of injection. A comparison of bore casing images pre- and post-

injection showed a significant increase in the clogging layer area. Test pumping had a minor

remediation effect, reducing the clogging layer by 10-15%. As such, clogging mechanisms

had a moderate impact of the operation of the MAR system at MAC.

Key words: Managed Aquifer Recharge, clogging, mining, Pilbara

12

1 Introduction

1.1 Overview

Managed Aquifer Recharge (MAR) is the process of enhancing natural rates of recharge to

groundwater systems and is becoming an increasingly important water management tool

globally. MAR schemes can take on a number of different forms, with the Aquifer Storage

and Recovery (ASR) and infiltration basin type being the most common. MAR schemes have

been successfully implemented in Australia and around the world for the purposes of

stormwater harvesting and wastewater recycling to secure additional water supplies and

maintain environmental outcomes. However, there is huge potential for MAR to be applied to

the mining industry to aid mine site water management.

Clogging at the injection surface poses one of the most challenging and persistent technical

issues for the operation of an MAR scheme (Dillon et al. 2001). Clogging can occur via

physical, chemical and biological processes and several diagnostic methods exist to predict

the clogging potential. An understanding of the clogging formation process and potential in

each MAR system enables efficient operation and maximises the longevity of MAR

infrastructure.

Using a case study at Mining Area C (MAC), 120km northeast of Newman, this thesis will

explore the role of MAR in mining and asses the injection performance of the system with

regards to potential clogging mechanisms. In November 2010, MAC commenced Below

Water Table (BWT) mining and associated dewatering activities. Groundwater abstracted

from in-pit and ex-pit borefields is used for dust suppression and ore handling and processing.

The mine has reached a positive water balance scenario, where dewatering supply is greater

than use. Management option to handle the surplus water generated by BWT mining

operations in the past have included storage in in-pit lakes, evaporation fans and infiltration

and evaporation with sediment ponds. MAR was identified as the preferred option and

manages the surplus water scenario in a sustainable and environmentally sensible manner.

MAR has the potential to minimise the mine site drawdown footprint by conserving the

groundwater resource for future direct use or targeted mitigation. In addition, it is in-line with

the DoW heirachy of responsible water use as outlined in the Pilbara water in mining

Guidelines (DoW(a), 2009).

13

The MAC MAR system was commissioned in April 2012 and continuous injection began in

August 2012. This thesis will review field data collected between April 2012 and April 2013.

Prior work identified clogging as a potential operational risk to the system; through the

precipitation of carbonates, the build-up of sediments or the growth of iron bacteria (BHP

Billiton, 2011).

1.2 Aims

This thesis seeks to achieve the following aims to investigate and understand the operational

risks in relation to the MAR system at MAC:

Describe the hydrological response to the injection trial in terms of aquifer

mounding, including both the spatial distribution of mounding observed in the

monitoring bores and the in-well response of the injection bores;

Analyse and quantify the operational performance of the injection system

throughout the duration of the trial;

Apply diagnostic tools to determine the clogging mechanism, if applicable, in the

event of decreasing operational performance.

1.3 System Characterisation

1.3.1 Site Description

Mining Area C (MAC) is a BHP Billiton owned and operated open-pit iron ore mine located

120km northeast of Newman in the Pilbara region of Western Australia. Mining activities are

conducted within tenement ML281SA. Figure 1 shows the site layout including key

components of the MAR scheme, final pit outlines of each deposit and the Overburden

Storage Areas (OSA).

14

Figure 1 Mining Area C location and site layout, showing the components of the MAR system (Figure created by

author)

1.3.2 MAR System Characterisation

Figure 2 shows the MAR design schematic with the seven components common to all MAR

projects as outlined by Dillon et al. (2009). Injection water is sourced from dewatering of the

mineralised Marra Mamba orebody aquifers at C and E Deposit (number of bores may vary

according to operational requirements). Water is transferred through a PVC pipe network to a

settling pond at A deposit, where a skid-mounted centrifugal pump drives the surplus water to

the injection bores. The infrastructure is designed to a 24ML/day injection capacity with an

expected annual discharge of 5.84 GL (BHP Billiton, 2011). Water is discharged into the

Paraburdoo Dolomite Member of the Wittenoom Formation via three 8” PVC injection bores

situated in A Deposit; HGA001P, HGA0002P and HGA0003P. The injection bores were

originally constructed and used as water supply bores and have been retrofitted with an orifice

plate connected to 75mm diameter layflat hose to facilitate injection. Table 1 shows that

HGA0001P is screened in the Wittenoom dolomite only, with HGA0002P and HGA0003P

screened in both the dolomite and the tertiary detritals and summarises information on the

adjacent observation bores. Construction logs are provided in Appendix A; Figure A-3,

Figure A-4 and Figure A-5 for further reference.

15

Table 1 MAR bore details (Refer to MAC is located in the Fortescue River basin within the

Weeli Wolli Creek system. The regional groundwater system flows to the east towards Weeli

Wolli Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to

555mRL at Weeli Wolli Spring (Golder, 2011). The convergence of groundwater flow with

outcropping basement rock in the east causes groundwater levels to rise and form the Weeli

Wolli Spring (RPS Aquaterra, 2008).

The groundwater at MAC is of potable quality and is calcium-magnesium bicarbonate rich.

Water quality analysis conducted by Golder (2011) indicates that the groundwater is weakly

acidic to weakly alkaline, with pH ranging from pH6.3 to pH 8.2. The salinity ranges from

258 to 642 mg/L with temperatures around 30C. Barber (2010) states that the level of

dissolved oxygen in both the source groundwater and the receiving groundwater indicates that

redox reactions are unlikely to take place due to the mixing of waters. However, there is some

potential for dolomite dissolution given the low levels of carbonate minerals (dolomite and

calcite) and sulphate minerals (anhydrite, gypsum and barite) in the source water.

16

Table 2 for codes of screened units)

Bore Depth (m)

Screens Date Drilled

Final Airlift Yield (L/s)

SWL (mbTOC) Unit Interval Depth Date

HGA0001P 118 OB 82-115 30/03/1998 16 45.27 18/04/1998 HGA0002P 136 OB/ TD 77.5-125.5 05/04/1998 >20 42.63 19/04/1998 HGA0003P 115 OB/TD 48-106 30/06/1997 N/A 42.66 05/10/1997 Closest adjacent observation bore to HGA0001P (10.8m) HGA0035M 66 TD 54-65 02/04/2012 N/A 52.35 04/04/2012 Closest adjacent observation bore to HGA0002P (15.3m) HGA0036M 66 OB/TD 44-65 03/04/2012 N/A 46.55 04/04/2012 Closest adjacent observation bore to HGA0003P (12.8m) GWB0012M 91 OB 77-91 25/05/1997 4 42.49 15/06/1997

Figure 2 MAR schematic diagram showing the seven system components of an MAR project outlined by Dillon et al.

(2009).

1.3.3 Climate Characterisation

The central Pilbara climatic setting is arid-tropical with high summer rainfall influenced by

tropical maritime and continental air masses. Historic monthly rainfall data was available

from the Flat Rocks rainfall gauge (DoW site 505011) from 1972 to 2013. The MAC region

receives an average annual rainfall of 404mm and the rainfall record is dominated by cyclonic

events as shown in Figure 3. A location map for the rainfall gauge is available in Figure A-1

in Appendix A.

17

Figure 3 Historic monthly rainfall record at Flat Rocks gauge, 20km north of MAC

1.3.4 Hydrogeological Characterisation

MAC is located in the Hamersley Basin on the northern flank of a large-scale regional

anticline which strikes east to west. The stratigraphic sequence, as provided in Figure A-2

Appendix A, dips northwards. The two dominant iron bearing stratigraphic units are the

mineralised Mount Newman Member of the Marra Mamba banded iron formation (BIF) and

the Brockman BIF which outcrops in the Packsaddle Ranges.

Complex faulting and folding is a dominant feature of the local geology at MAC. A low

permeability dolerite dyke runs through E Deposit which has implications on local

groundwater flow. The local 20,000km surface geology interpretation at the injection site is

provided in Appendix A, Figure A-6 with a Table A-1 explaining the geology codes.

The Paraburdoo dolomite Member of the Wittenoom Formation constitutes the regional

aquifer at MAC, along with saturated alluvial valley fill and scree detritals with calcrete

lenses. The hydraulic conductivity varies from high in significantly weathered regions to low

where fresh dolomite is predominant. Sufficiently mineralised zones of the Mount Newman

Member of the Marra Mamba Iron Formation form localized orebody aquifers. The West

Angela semi-permeable shale separates these aquifers to varying degrees along the strike of

the deposit.

0

50

100

150

200

250

300

350

1972 1976 1980 1984 1988 1992 1997 2001 2005 2009 2013

Tot

al M

onth

ly R

ainf

all (

mm

)

Historic Rainfall Record at Flat Rocks (505011)

Cyclone Joan - Dec 1975478.4mm

18

The conceptual model of the MAR system is shown in Figure A-7, Appendix A, which

highlights the major geological formations, conceptual abstraction and dewatering bores and

indicative pre- and post- injection water levels. Geological cross sections were prepared using

the 3-dimensional modeling software Leapfrog (version 2.1.1), showing the intersection with

the regional dolomite aquifer (OB = blue) in Figure 4 to Figure 6. The cross sections show

northward dipping stratigraphy overlain by 50 to 90m of tertiary sediment deposits.

Figure 4 Geological cross section for HGA0001P looking east along 709317E (Central Pilbara Grid), unit codes from

Table 2

Figure 5 Geological cross section for HGA0002P looking east along 709562E (Central Pilbara Grid) , unit codes from

Table 2

19

Figure 6 Geological cross section for HGA0002P looking east along 709770 (Central Pilbara Grid),, unit codes from

Table 2

MAC is located in the Fortescue River basin within the Weeli Wolli Creek system. The

regional groundwater system flows to the east towards Weeli Wolli Spring, Prior to mining,

groundwater levels at MAC were 662mRL and decreasing to 555mRL at Weeli Wolli Spring

(Golder, 2011). The convergence of groundwater flow with outcropping basement rock in the

east causes groundwater levels to rise and form the Weeli Wolli Spring (RPS Aquaterra,

2008).

The groundwater at MAC is of potable quality and is calcium-magnesium bicarbonate rich.

Water quality analysis conducted by Golder (2011) indicates that the groundwater is weakly

acidic to weakly alkaline, with pH ranging from pH6.3 to pH 8.2. The salinity ranges from

258 to 642 mg/L with temperatures around 30C. Barber (2010) states that the level of

dissolved oxygen in both the source groundwater and the receiving groundwater indicates that

redox reactions are unlikely to take place due to the mixing of waters. However, there is some

potential for dolomite dissolution given the low levels of carbonate minerals (dolomite and

calcite) and sulphate minerals (anhydrite, gypsum and barite) in the source water.

20

Table 2 Geological reference table for unit codes

Group Formation Member Code Description

Tertiary-age sediments

SZ Surface scree

TD3 Red tertiary detritals; transported material; pisolitic well-rounded gravels and magehite; typically low permeability

TD2 Yellow-brown Tertiary detritals; contains clay and calcrete lenses; typically low permeability

TD1

Red Ochre Detritals; fine-grained sediment deposits with large hematite clasts; formed from alluvial material from the Wittenoom and Marra Mamba Iron Formations; generally Fe>60%; typically low permeability

Ham

ersl

ey G

roup

Witt

enoo

m F

orm

atio

n

Paraburdoo OB Blue-grey dolomite; ranges from fresh to highly weathered; represents the regional aquifer in high permeability zones with dominant fracturing and weathering

West Angela WA2

Green-grey shale; low permeability with some fracturing allowing connection to the Marra Mamba formation

WA1 Cherty BIF followed by shale; can be enriched with Fe>50%

Mar

ra M

amba

Iron

Form

atio

n

Mount

Newman

N3 BIF with minor shale interbeds; geothetic mineralisation with carbonaceous shales; where mineralised (Fe>60% typically) this unit forms a localised aquifer associated with the ore deposit and enrichment favours the N3 unit

N2

N1

MacLeod MM Shaley, cherty BIF with several chert pod; horizons; potential enriched with Fe>30%; moderate permeability where mineralised

21

2 Literature Review

This literature review investigates the fundamentals of a MAR system, common operational

issues and the practicalities of MAR in the mining industry.

2.1 Managed Aquifer Recharge

2.1.1 Definition

Managed aquifer recharge is the “intentional recharge of water to aquifers for subsequent

recovery or environmental benefit” (NWQMS 2009 pg 13). The process has also been

referred to as enhanced recharge, water banking and sustainable underground storage in the

literature (Dillon 2005). The definition specifies „intentional‟ recharge to separate MAR from

incidental or unintended recharge processes such as the effects of land clearing, over

irrigation and increased runoff from urbanisation. Recently, the term “artificial recharge” has

been effectively replaced with MAR to avoid the negative connotations associated with the

perceived unnatural and non-sustainable process.

2.1.2 Purpose

MAR systems may be implemented for a number of purposes:

to harvest urban stormwater to supplement water resources (Page et al. 2011;

Vanderzalm et al. 2010; Dillon et al. 1999);

to reclaim wastewater to supplement water resources (Page et al. 2010; Bosher et al.

1998; Asano and Levine 1998);

to sustain environmental flows and phreatophytic vegetation (Naumburg et al. 2005);

and to act as a barrier to prevent saline intrusion (Daher et al. 2011; Shammas 2008);

to manage water generated from dewatering activities in open pit mining operations

(Youngs et al., 2010; Clarke, 1983).

The use of MAR to capture and store stormwater to reduce demand on conventional water

resources has become increasingly popular in Australia over the last decade. The largest

Australian MAR project is located in Queensland‟s Burdekin Delta, where recharge of

100GL/year via infiltrations ponds maintains sugar cane production. MAR initiatives have

been implemented with success worldwide; with India, USA, Sweden, Finland, New Zealand,

22

France and Germany leading the way with application of groundwater recharge management

schemes (IGRAC 2012).

2.1.3 Types

MAR encompasses a wide variety of water management systems, which vary with recharge

method, source of recharge water, end use of recovered water, scale and complexity. Aquifers

may be recharged by two methods: (a) the injection of source water directly into the target

aquifer through screened wells, or (b) the infiltration of source water through open basins,

galleries or channels. Recharge water may be sourced from drinking water treatment plants,

sewage treatment plants, harvested storm water, irrigation districts, ephemeral streams,

industrial specific sources (Bouwer 2002) or, as at MAC, excavation dewatering/

Table B-1 in Appendix B lists an example of each type of MAR system, with an associated

schematic from Dillon (2005) in Figure B-1. The ASR type of MAR scheme shall be the

focus of this literature review, as it is the model for the case study at Mining Area C.

2.1.4 Benefits

ASR has many benefits over surface water storages structures such as dams and reservoirs

making it an efficient option for long-term water storage. These include (NWQMS 2009;

Pyne 2006; Bouwer 2002; Kimrey 1989):

Low capital installation costs;

Low evapotraspiration loss from the aquifer;

Multi-purpose capacity for water quality treatment in addition to storage;

Reduced project area footprint;

Low potential for structural failure (i.e. dam wall failure);

Reduced potential for mosquito habitat;

Flexible system size to meet incremental growth in water demand;

Reduced potential for pollution or damage by sabotage or other hostile action;

Improved reliability of existing supplies

While the benefits of ASR have been widely publicised, the ongoing energy requirements and

operational cost of maintaining an ASR scheme is seldom touched on in the literature.

2.2 Clogging

23

2.2.1 Introduction

A common operational issue affecting ASR schemes is clogging of the recharge surface,

gravel pack or surrounding aquifer matrix that, in serious cases, can lead to the abandonment

of projects. Also known as aquifer plugging, the Australian MAR Guidelines define clogging

as “the reduction in permeability of a porous medium” (NWQMS 2009, p114). Clogging

leads to a reduction in flow rates, which limits the volume of water stored in the aquifer, or an

increase in head to maintain a constant recharge rate. It is important to understand the types

and causes of various forms of clogging and the associated management options, to define

source water treatments needs and maximize the operational life of the injection system.

2.2.2 Types and Causes

Clogging occurs due to reactions between the source water, target water and the aquifer

matrix as result of physical, chemical or biological mechanisms. Pyne (2005) identified that

following processes could be responsible for clogging:

Air entrapment;

Deposition of total suspended solids (TSS);

Biological growth;

Geochemical reactions;

Particle rearrangement in the aquifer materials.

Each process is discussed in detail below:

Air entrapment or gas binding is caused by the cascading of water inside the injection well

casing or air entering the recharge pipe network under negative pressure, producing air

bubbles that may block pore spaces in the aquifer matrix and screened casing. It is similar to

bubble lock which can occur during bore/well development. The entrained air increases the

oxidation-reduction potential (ORP), which promotes microbial activity and geochemical

reactions, leading to further clogging. Air entrapment can also occur due to the release of

dissolved gases through temperature or pressure changes or as a metabolic byproduct of

microbial activity (release of nitrogen or methane). Pyne (2005) suggests that clogging by air

entrapment is characterised by a rapid increase in flow resistance as shown in Figure 7.

24

Figure 7 A graphical diagnostic tool for determining different mechanisms of clogging (Pyne, 2005)

The accumulation of organic and inorganic suspended solids, such as clay and silt particles,

algae cells or their tests (diatoms), microorganism cells, can form a low permeability

clogging layer on injection surfaces (Bouwer 2002). Both Dillon et al. (2001) and Youngs et

al. (2010) assert that the deposition of suspended sediments is the most frequently reported

form of clogging.

Microbial clogging occurs through the growth of microorganisms and the production of

biofilms (extracellular polysaccharides). Pyne (2005) states that clogging due to biological

growth is not well understood. Recharge waters rich in organic carbon, nitrogen and

phosphorus, promote biological clogging and it is a commonly reported issue in recharge

basins (NWQMS 2009). Schuh (1990) identified that biological clogging in surface

infiltration systems can vary seasonally, in response to changes in water temperature and

viscosity.

Chemical clogging is the result of mineral precipitation affecting aquifer permeability.

Common geochemical reactions are the precipitation of calcium carbonate (calcite), gypsum,

phosphate, iron and manganese oxide hydrates (Bouwer 2002; Pyne 2005). Bacteria catalyse

many geochemical reactions therefore it can be difficult to separate chemical clogging from

biological clogging. These reactions occur due to the changes in redox conditions inherent in

injection of oxygenated water into typically reduced aquifers.

25

Particle rearrangement in the aquifer matrix, caused by repeated cycles of recharge and

recovery, can affect aquifer permeability (Olsthoorn 1982). Australian MAR Guidelines

(NWQMS 2009) fail to identify the particle rearrangement as a clogging mechanism however

Pyne (2005) states that particle rearrangement is not an important mechanism in aquifer

clogging but still must be considered.

2.2.3 Management Options

A well-designed and constructed system is critical to the effective operation of an MAR

scheme. Brown et al. (2000) suggested that air entrapment clogging issues could be

eliminated through the design phase in an ASR case study at Hamersley Iron‟s Nammuldi

iron ore mine in the Pilbara. Youngs et al. (2010) suggested that the rapidly constructed pipe

network, paired with poor scouring practices, promoted physical clogging of injection wells at

FMG‟s Cloudbreak operation.

Several options to manage injection well clogging are described in the literature:

Injection well redevelopment: The periodic redevelopment or backflushing of

injection wells by airlifting or pumping is the preferred method to manage clogging

according to Pyne (2005). The frequency of redevelopment depends on the rate of

clogging and can vary from daily to annually.

Pre-treatment of injection water: This is common for reinjection schemes where the

end use is for potable purposes or the quality of the source water is significantly lower

than the target aquifer. Bouwer (2002) indicates that in addition to reducing the effects

of clogging, pre-treatment of water enables the protection of the receiving

groundwater quality.

Alterations to MAR infrastructure: Youngs et al. (2010) remediated the physical

clogging of FMG‟s Cloudbreak operation by removing the slotted PVC casing, in

addition to well redevelopment.

Chemical treatment: Chlorine and chemical treatments such as mineral acids,

organic acids, biodispersants, surfactants and enzymes are utilised as a rehabilitation

procedure (Pyne 2005). It is effective against biological clogging but is limited in

mitigating physical clogging.

2.2.4 Diagnostic Tools

26

Various techniques have been reported on in the literature to predict clogging potential. These

include:

Membrane Filtration Index (MFI);

Water quality parameters;

Laboratory column experiments;

Numerical modeling;

Graphical techniques

The MFI method provides a relatively easy field assessment of physical clogging potential

(Dillon et al. 2001). Membrane filtration tests are used to develop an MFI. The test involves

passing recharge water through a membrane of fixed aperture at a constant pressure whilst

measuring the decline in flow rate. The MFI is then determined graphically from the slope of

the linear portion of the time/volume (t/V) vs. volume (V) plot. Dillon et al. (2001) states that

the greater the slope, the higher the MFI and the greater potential for physical clogging. As

the standardised membrane is unlikely to be representative of the pore spaces in the aquifer,

the test provides a guide only and cannot be relied upon as an absolute measure. Fracture-

dominated flow further reduces the relevance of MFI as a tool.

Water quality parameters can be useful indicators of clogging potential. Measurement of

turbidity and TSS indicate physical clogging while total organic carbon (TOC), dissolved

organic carbon (DOC) and assimilable organic carbon (AOC) indicate biological clogging

(NWQMS 2009). Laboratory column studies, also known as the parallel filter index (PFI), are

determined by passing recharge water through columns filled with aquifer material (Bouwer

2002; Wood et al. 2005; Rinck-Pfeiffer et al. 2000). Due to the small-scale nature of the PFI

test, this method is not usually representative of the field scale processes. Youngs et al. (2010)

applied the PHREEQC geochemical model to determine the potential of chemical clogging.

The model relies on water chemistry and hydrogeological data to predict the potential for

mineral precipitation. Simple graphical techniques to predict clogging have been proposed by

Pyne (2005), where the relationship between resistance to flow and time is compared with

standard curves for each clogging type (Figure 7).

2.3 Application of MAR in the Mining Industry

2.3.1 Mining Water Management

27

There is huge potential for MAR to be applied to the mining industry to aid mine site water

management, although only few studies investigate this prospect. The Australian MAR

Guidelines (NWQMS 2009) and investigations into legislation and policy governing MAR in

Australia (Ward and Dillon 2012) make no reference to the potential for MAR in a mining

context.

Dewatering of open pits and underground mining areas to enable safe conditions for below

water table (BWT) mining can result in large volumes of water abstracted from the orebody

aquifer. Current water management practices endeavor to use this supply for operational

requirements, which include dust suppression and ore processing. However, often the

dewatering abstraction volumes exceed mine site water demand. In such cases of water

surplus, the simplest approach is to discharge excess water to the surface water environment.

The water is lost as a resource for the mine, and most of it is is wasted as evapotranspiration..

Over the life of mine, water balances can fluctuate between water surplus and water deficit

depending on mine planning, pit sequencing of BWT deposits and climatic conditions. MAR

has the potential to buffer these fluctuations by banking water during periods of water surplus

to meet future water demand in a deficit scenario.

In addition to managing fluctuations in the mine site water balance, MAR has several benefits

over water management strategies currently applied in the industry. Firstly, the disposal of

excess dewatering volumes into ephemeral surface water system often leads to negative

ecological and cultural implications. Youngs et al. (2010) state that the constant discharge

provides a water source for ecosystems, which may then become dependent on mine site

operations, and acknowledged that surface discharge is discouraged by traditional landowners

in the Pilbara. Secondly, the reinjection of the dewatering surplus reduces the net groundwater

drawdown of the mine site operations. MAR can also be used to mitigate impacts to

groundwater dependant ecosystems (GDE) proximal to mine dewatering and to sustain

borefields, such as Ophthalmia Dam in the Pilbara (DoW, 2009).

Compared to the conventional MAR projects intended to secure potable supply, MAR

schemes in an operational mining environment are designed „fit for purpose‟. This means that

mining-related MAR schemes are typically designed and constructed rapidly on a larger scale,

operated over a shorter life-of-project duration with less emphasis on control of water quality.

This is particularly applicable to the Pilbara environment as the source water is typically of

28

high quality, similar to the receiving water, and the environmental water requirements are less

stringent the human health requirements.

2.3.2 MAR Schemes in the Pilbara Region

The potential for ASR as a mine site water management tool in the Pilbara mining region of

Western Australia has had limited attention in the scientific literature. Only few authors have

investigated this prospect (Windsor et al. 2011; Youngs et al. 2010; Brown et al. 2000; Clark

and Kneeshaw 1983). By examining two ASR case studies, Brown et al. (2009) contended

that in addition to meeting future water demand, MAR could reduce impact to the

surrounding surface water environment.

Reductions in impacts to GDEs are another potential benefit. The successful implementation

of a relatively large and complex MAR scheme at Fortescue Metals Group (FMG)‟s

Cloudbreak operation proves that MAR is a viable option for water managers in the mining

industry and is an „ideal‟ tool for the Pilbara region (Youngs et al. 2010). The scheme consists

of a saline and fresh water injection system and was implemented to bank fresh water for

future use and to maintain ecological water requirements of the nearby Fortescue Marsh

(Youngs et al. 2010).

2.3.3 Mining Specific Operational Considerations

Brown et al. (2000) identified three issues specific to the application of MAR in a mining

context:

1. The potential for injected water to re-circulate back into the dewatered orebody

aquifer, with implications for the efficiency of dewatering operations. The optimal

distance between dewatering operations and injection wells is dictated by the degree

of hydraulic connectivity between the orebody aquifer and the target injection aquifer.

Numerical modeling can be applied to predict the volumes of reticulated water


2. The practical requirement of periodic well redevelopment due to a reduction in

hydraulic performance from the effects of injection well and aquifer clogging

(discussed further in Section Clogging) and the implications for the active mining

operations.

3. The impact of injecting poor quality water from the active mining area into a high

quality natural groundwater environment.

29

It should be noted that (3.) is not an issues solely related to MAR in a mining environment.

Studies concerning the injection of treated wastewater to secure drinking water supply

(Bosher et al. 1998; Asano and Levine 1998; Pavelic and Dillon 1997) have also highlighted

the issue of managing the interaction of low quality source water with a higher quality target

aquifer. Issues not identified by Brown et al. (2002) include the complexity added mining

operation by the injection infrastructure and the effect of reinjection on mining operations

downstream of the MAR site with the implications on their water balance and dewatering

requirements.

3 Materials and methods

3.1 Aquifer Response

3.1.1 Hydrographs

The groundwater level data was plotted alongside injection volumes to review the aquifer

response to injection over time. Each plot shows the injection groundwater level within the

bore, the static water level within the bore for periods when the system was not operating and

the water level in the closest adjacent observation bore (as outlined in Table 1).

3.1.2 Mounding

Groundwater level data (in mRL) was analysed and contoured using Surfer (version 10.2.601)

to create groundwater surface maps of pre- and post-injection scenarios. The raw point data

was interpolated using the kriging method to create a data grid file (.grd). The aquifer

response to the 12 month injection period is summarised by subtracting the post-injection grid

(11/04/2013) from the pre-injection grid (09/02/2012). The „pre-injection‟ date of 09/02/2012

was selected as it best represented the groundwater levels prior to any feasibility, testing and

commissioning works. The pre-injection grid was generated from six available monitoring

bores at the time and the post injection grid from eighteen bores, as more were constructed

and data routinely collected.

3.2 Operational Performance

3.2.1 Aquifer vs In-Well Groundwater Level

30

Groundwater level data within the injection bore was compared to the closest observation

bore. Examining the temporal trends of the difference between the water levels gives an

indication of operation performance; if the difference increases over time, the injection

performance is decreasing. The difference metric, D, is calculated according to:

Where D = difference metric (m)

I = Water level in the injection bores (mRL)

O = water level in the observation bore (mRL)

Only groundwater level data recorded during periods of injection was utilised.

3.2.2 Specific Injectivity

Specific injectivity is a metric used to define the relative strength of reinjection bores and is of

similar nature to the specific capacity term used for abstraction bores (Miller, 2001). It is

defined by the ASCE (2001) as the rate of injection divided by the total drawup within the

well and has previously been used in analysis of mine-site reinjection systems at FMG‟s

Couldbreak project by Youngs et al. (2010). A decrease in specific injectivity over time

indicates poor injection performance and the potential presence of a clogging mechanism.

Two approaches were used to analyse specific injectivity; long-term specific injectivity, SIL,

and short-term specific injectivity, SIS, according to the following equations:

Where SIL = long-term specific injectivity (m3/d/m)

VT = total volume injected since 1/8/2012 (m3)

SWT = total drawup within the well since 1/8/2012 (m)

T = total time since 1/8/2012 (d)

Where SIS = short-term specific injectivity (m3/d/m)

V = volume injected since previous measurement (m3)

31

Sw = drawup within the well since previous measurement (m)

t = time since previous measurement (d)

The analysis was restricted to a period of relatively continuous injection from 1/8/2012 to

1/04/2013. The period of continuous injection for HGA0003P ended on the 12/11/2012 due to

a management decision regarding the injection bore‟s poor performance.

Table 3 outlines the initial groundwater levels used to calculate SWT for long-term specific

injectivity prior to continuous injection.

Table 3 Static Water level for the injection bores recorded on 9/2/12

Injection Bore HGA0001P HGA0002P HGA0003P SWL 9/2/12 577.16mRL 577.13mRL 577.01mRL

Both the water level and volume data were collected at weekly intervals. However due to

operational requirements on the mine site, water level data was not collected on the same day

as the volume data (often 2-3 days apart). To allow for specific injectivity calculations,

injection between volume measurements was assumed to be linear and used to estimate the

volume at the time of water level measurement.

However, historical data in Figure 8 shows a declining trend in regional groundwater levels

prior to injection in observation bore GWB0012M (adjacent to HGA0003P). Linear

regression was used to adjust the initial groundwater levels at a rate of -0.76m per year to

account for the regional trend.

A cumulative deviation from mean rainfall (CDFM) analysis was used to determine if the

regional decline in groundwater levels could be attributed to climatic variation.

Anthropogenic causes such as groundwater abstraction, land use changes, vegetation clearing

can also contribute to groundwater level decline. This method has been used in the literature

for similar groundwater investigations by Eakin (1964), Temperley (1980) and Boehmer

(1998). The CDFM is calculated on a monthly basis by subtracting the actual rainfall from the

long term average and plotting the cumulative difference over time. A decreasing trend in the

CDFM plot indicates a period of below average rainfall. The plot is matched to the GWB0012

hydrograph to determine a relationship between climate and groundwater levels.

32

Figure 8 Hydrograph at observation bore GWB0012M showing a declining regional groundwater trend prior to

injection (1997 – 2012)

3.2.3 Well Efficiency

Changes to the well efficiency following the injection trial were analysed to indicate relative

operational performance. Data was sourced from step-drawdown tests conducted by AquaGeo

(2013) and historic step-drawdown data from Woodward-Clyde in 1997-1998. The details of

the step-drawdown tests are summarised in Table 4 below which highlight the differences in

flow rate, duration and number of steps of the „before‟ (1997-1998) and „after‟ (2013) tests.

Table 4 Details of the step-drawdown tests conducted by Woodward-Clyde (1997), Woodward-Clyde (1998) and

AquaGeo (2013)

Bore ID Date Duration (mins)

Steps Discharge Rates (L/s)

Pump Inlet Depth (mbgl)

HGA0001P 18/04/1998 100 5 10, 13. 16, 19, 25 81 12/06/2013 60 4 6, 14, 21, 28 109

HGA0002P 19/04/1998 100 5 10, 13. 16, 19, 25 75 14/06/2013 60 4 8, 14, 21, 28 124

HGA0003P 12/07/1997 100 5 8, 12, 16, 18, 20 100 1/06/2013 60 4 6, 10, 15, 20 106

The step-drawdown test data was analysed using the Hantush-Bierschenk method to

determine the coefficients B and C of the Jacob‟s well equation (Kruseman and de Ridder,

1994):

where

572

574

576

578

580

582

584

586

1997 1999 2001 2003 2005 2007 2009 2011

Gro

undw

ater

Lev

el (m

RL

)Pre-Injection regional groundwater trend (GWB0012M)

33

Sw = drawdown in the production well (m)

Q = constant discharge (m3/d)

B = linear coefficient

C = non-linear coefficient

P = exponent, can vary from 1.5 to 3.5 with 2 widely utilised (Ramey, 1982)

Sw vs time was plotted on semi-log paper to determine ∆Sw(i), the increments of drawdown

between each step by the extrapolation of data. The total drawdown during the n-th step,

Sw(n), was calculated as the sum of the drawdown increments.

Using the discharge during the n-th step, Qn, the ratio of Sw(n)/Qn was calculated and plotted

vs the corresponding values of Qn for each step. The slope of the straight line fitted to the data

points is the coefficient C and the Sw(n)/Qn -axis intercept is the coefficient B.

The well efficiency, Ew, can be defined as the ratio of field specific capacity to theoretical

specific capacity and was calculated using the equation from Kasenow (2006):

As Table 4 shows, the step-drawdown tests conducted for HGA0003P pre- and post-injection

utilised different numbers of steps and different discharge rates per step. In order to achieve

direct comparison of the results, linear regression was applied to the post-injection data of

well efficiency vs discharge data to predict well efficiencies at the pre-injection dataset

discharge rates.

Sensitivity analysis

Ramey (1982) stated that the value of the Jacob‟s well equation exponent, P, can range

between 1.5 and 3.5. A sensitivity analysis was undertaken to determine whether changing the

value of P changed the interpretation of the well efficiency results. An interpretation of the

equation from Kasenow (2006) was used to re-calculate Ew(P), well efficiency as a function of

P:

34

Where P = {1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 3.5}

3.3 Clogging Diagnostics

3.3.1 Graphical Tool

The graphical diagnostic tool outlined in Pyne (2005) was applied to the injection trial data to

give an indication of the potential clogging mechanism if applicable. Figure 7 shows standard

curves for mechanical, biological and physical clogging which are distinctive in terms of the

resistance to flow over time. Flow resistance, or in-well mounding, was plotted over time and

compared to the standard curves. Table 5 show the static water level (SWL) used to calculate

in-well mounding, recorded on the 9/2/12 prior to the commencement of any injection

activities. Consideration for a declining trend in the regional groundwater table, as described

in section Methods: Specific Injectivity, was also included in the analysis.

Table 5 Static Water level for the injection bores recorded on 9/2/12

Injection Bore HGA0001P HGA0002P HGA0003P SWL 9/2/12 577.16mRL 577.13mRL 577.01mRL

3.3.2 Water Quality Analysis

Water quality samples during the injection period were analysed for a range of organic and

inorganic parameters, with focus on turbidity, suspended solids and dissolved iron as

indicators for clogging potential. Martin (2013) indicates that turbidity values less than 5

NTU is a practical target to identify physical clogging. However, this is highly dependent on

the scale of the controlling pores or voids in the aquifer.

3.3.3 Bore Casing Image Analysis

The downhole images of each injection bore‟s inner casing was quantitatively analysed prior

to injection (June 2011), post-injection (May 2013) and following aquifer testing (June 2013)

to determine the impact of the injecting and aquifer testing activities on the proportion of

biofilm on the screens.

The images were captured by Surtech Systems using an optical televiewer (OTV) instrument

which captures at 360° oriented image of the borehole using a digital charge-coupled device

(CCD) camera. The raw image was analysed using Irfan View image processing software

(Version 4.36) at 1m intervals. Firstly, the colour settings at each 1m section were enhanced

35

to identify the biofouling layer as black, an example is shown in Figure 9. The colour settings

used in the analysis varied given the different light settings in the raw images and are

provided in Table C-1, Appendix C. The percentage of biofouling for each 1m interval was

calculated using the red channel of the Image Histogram function, which counts the number

of pixels at a given colour or Index (in this case black = Index 0), as shown in Figure C-1 in

Appendix C.

Raw Image Enhanced Image

Figure 9 An example of a 1m section showing both the raw and enhanced OTV image

3.3.4 Saturation Index

Saturation indicies are used to predict whether a mineral is likely to dissolve or precipitate in

solution (Packhurst and Appelo, 1999). To determine the potential for chemical clogging,

trends in the saturation indices during injection were examined. Prior to the injection trial,

sulphate and carbonate minerals were identified by Barber (2010) as risks to the injection

system and thus have been selected for analysis.

The saturation index, SI, compares the ion activity product from water quality data at a certain

temperature to the equilibrium constant at that same temperature and is calculated according

to (Deutsch, 1997):

Where IAP = ion activity product, from water quality data

KMineral = equilibrium constant for mineral, temperature dependant

36

If SI = 0, the mineral is in equilibrium with solution. If the IAP is greater than the equilibrium

constant (SI>0), the groundwater is oversaturated and the mineral is likely to precipitate. If

the IAP is less than the equilibrium constant (SI < 0), the sample is undersaturated with

respect to the mineral likely to dissolve. However, Deutsch (1997) indicates that the practical

range for equilibrium in groundwater is SI = 0.5.

Table 6 lists the dissolution reactions and ion activity products for carbonates (calcite and

dolomite) and sulphates (gypsum, bartite and anhydrite) included in the analysis.

Table 6 Dissolution reactions and ion activity products (IAP) for minerals included in the analysis (adapted from

Deutsch, 1997)

Mineral Composition Dissolution Reaction IAP=

Calcite CaCO3 CaCO3 Ca2+ + CO32- (aCa2+)(aCO3 2-)

Dolomite CaMg(CO3)2 CaMg(CO3)2 Ca2+ + Mg2+ +

2CO32-

(aCa2+)(aMg2+)(aCO3 2-

)2

Gypsum CaSO4∙2H2O CaSO4∙2H2O Ca2+ + SO4

2- +

2H2O

(aCa2+)(aSO4 2-)

Bartite BaS O4 BaS O4 Ba2+ + SO42- (aBa2+)(aSO4 2-)

Anhydrite CaSO4 CaSO4 Ca2+ + SO42- (aCa2+)(aSO4 2-)

The geochemical modeling program PHREEQC Interactive (Version 3.1.2) with the minteq

thermodynamic database was used to determine the saturation index. Water quality data for

major, minor and trace inorganics was available for fourteen groundwater samples between

July 2010 and October 2013. Eight of these samples had associated field-determined pH and

temperature data. Figure C-2 in Appendix C shows that the laboratory-determined pH is

greater than the field-determined pH, indicating that the sample has reached equilibrium with

CO2 in the atmosphere and degassed prior to laboratory analysis. Therefore, the field-

determined pH better represented in situ conditions and used as the PHREEQC input. For the

eight samples where the field-determined temperature and pH were not available, an average

value was applied from field data collected during the injection period. Figure C-3 in

Appendix C shows there is no trend with field pH with time.

Sensitivity Analysis

37

A sensitivity analysis was undertaken across the temperature range of 25 - 35C at 0.25C

intervals to determine the impact of temperature uncertainty on the interpretation of saturation

index results.

4 Results


4.1.1 Hydrographs

Figure 10, Figure 11 and Figure 12 show the groundwater level and injection volume plots for

HGA0001P, HGA0002P and HGA0003P respectively. HGA0003P shows the least volume of

injected water (green bars) and the greatest water level response (purple line). Conversely,

HGA0001P and HGA0002) show a marginal response to a significant volume of water

injected.

Figure 10 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0001P

0

5000

10000

15000

20000

25000

30000

575

576

577

578

579

580

581

582

583

584

Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13

Vol

ume (

kL) -

Flow

Met

er

Wat

er L

evel

(mR

L) -

Man

ual D

ip

HGA0001P- Groundwater Levels and Injection Volumes

Injection Volum (kL per week) HGA0001P Injection Water Level (mRL)

HGA0001P Static Water Level (mRL) HGA0035M Water Level (mRL)

38



4.1.2 Mounding

The pre-injection groundwater surface in Figure 13 shows a shallow gradient trending to the

south-east with water levels ranging from 576.8 to 577.1mRL. Figure 14 shows that the post-

injection surface water table ranges from 581.3 to 582.3mRL with elevated water levels above

HGA0001P. The mounding map in Figure 15 shows there is 4.7-5.1m of mounding across the

0

5000

10000

15000

20000

25000

30000

576

578

580

582

584

586

588


Vol

ume (

kL) -

Flow

Met

er

Wat

er L

evel

(m

RL

) -M

anua

l Dip

HGA0002P - Groundwater Levels and Injection Volumes

Injection Volume (kL per week) HGA0002P Injection Water Level (mRL)

HGA0002P Static Water Level (mRL) HGA0036M Water Level (mRL)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

575

580

585

590

595

600

605

610

615


Vol

ume (

kL) -

Flow

Met

er

Wat

er L

evel

(m

RL

) -M

anua

l Dip

HGA0003P - Groundwater Levels and Injection Volumes

Injection Volume (kL per week) HGA0003P Injection Water Level (mRL)

HGA0003P Static Water Level (mRL) GWB0012M Water Level (mRL)

39

study area as a result of 12 months of injection with peak mounding around HGA0001P.

Figure 13 Pre-injection groundwater surface in mRL from the 09/02/2012 (0.1m contour intervals)

Figure 14 Post-injection groundwater surface in mRL from the 11/4/2013 (0.1m contour intervals)

40

Figure 15 Groundwater mounding in m following 12-months of injection (0.1m contour intervals)


4.2.1 Aquifer vs In-Well Groundwater Level

Figure 16 shows a rapid increase in the groundwater level difference for HGA0003P

indicating poor performance. In contrast, the difference at HGA0001P remains steady over

time and HGA0002P marginally increases over time, indicating performance is slowly

reducing.

Figure 16 Plot of the difference between the injection bore and the closest observation bores groundwater levels

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13

Diff

eren

ce (m

)

Aquifer vs In-Well Groundwater Level

HGA0001P vs HGA0035M HGA0002P vs HGA0036M HGA0003P vs GWB0012M

Continuous Injection

Testing and Commissioning

41

4.2.2 Specific Injectivity

Figure 17 shows that the long-term specific injectivity decreases over time during the period

of continuous injection. This trend is as expected for decreased operational performance,

suggesting the long term effects of persistent clogging. The results for HGA0001P range from

759.3 to 1667.2 m3/d/m which far exceed the values for HGA0002P (from 338.8 to 637.6

m3/d/m) and HGA0003P (from 20.3 to 49.9 m3/d/m). However, no clear trends are

discernable for the short-term specific injectivity where results span several orders of

magnitude.

The CDFM plot in Figure 19 shows a period of below average rainfall prior to the injection

trial. Figure 20 indicates rainfall has a moderate degree of influence over the regional decline

in groundwater level.

42

Figure 17 Long-term specific Injectivity, SiL over time for each injection bore

y = -3.4326x + 142757R² = 0.7104

0

400

800

1200

1600

2000

Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13

Spec

ific

Inje

ctiv

ity (m

3/d/

m)

HGA0001PLong Term Specific Injectivity (m3/d/m)

y = -0.9204x + 38391R² = 0.6881

0

100

200

300

400

500

600

700


Spec

ific

Inje

ctiv

ity (m

3/d/

m)


y = -0.3845x + 15866R² = 0.8208

0

10

20

30

40

50

60


Spec

ific

Inje

ctiv

ity (m

3/d/

m)


43

Figure 18 Short-term specific injectivity, SIS over time for each injection bore

0

40000

80000

120000

160000

200000


Spec

ific

Inje

ctiv

ity (m

3/d/

m)

HGA0001PShort Term Specific Injectivity (m3/d/m)

0

10000

20000

30000

40000

50000

60000


Spec

ific

Inje

ctiv

ity (m

3/d/

m)


0

100

200

300

400

500

600

700


Spec

ific

Inje

ctiv

ity (m

3/d/

m)


44

Figure 19 The cumulative deviation from mean rainfall plot showing a dry period prior to the commencement of the

groundwater injection trial

Figure 20 Matching theCDFM plot with the GWB0012M hydrograph to determine a relationship

4.2.3 Well Efficiency

The Jacob‟s equation coefficients calculated by the Hantush-Bierschenk method are provided

in Table 7 below. The interim steps of the Hantush-Bierschenk method, are provided in

Appendix C – Results.

575

576

577

578

579

580

581

582

583

584

585

-1200

-1000

-800

-600

-400

-200

0

200

400

600

1972 1977 1982 1988 1993 1999 2004 2010

Gro

undw

ater

Lev

el (m

RL

)

CD

FM (m

m)

Regional Groundwater Level and CDFM

CDFM (mm) GWB0012M Groundwater Level

Wet Period Dry Period Wet Period Dry Period

575

576

577

578

579

580

581

582

583

584

585

-350

-150

50

250

450

650

850

1997 1999 2002 2005 2007 2010

grou

ndw

ater

leve

l (m

RL

)

CD

FM (m

m)

Hydrograph and CDFM Matching

CDFM (mm) Water Level (mRL) - Manual Dip

Wet Period Dry Period

45

Table 7 Jacobs equation coefficients determined using the Hantush-Bierschenk method

Injection Bore Test Date B C HGA0001P

Nov 1998 4.35E-04 4.89E-08

June 2013 3.48E-04 1.39E-07

HGA0002P

Nov 1998 1.43E-04 9.30E-08

June 2013 1.61E-04 3.64E-07

HGA0003P

July 1997 3.67E-03 2.13E-06

June 2013 9.16E-03 5.83E-06

Figure 21 shows that the well efficiency is lower after injection and decreases as discharge

increases for all injection bores. HGA0002P was the least efficient performer with Ew ranging

from 64% -15.5%, while the well efficiencies from HGA0001P ranged from 82.2% - 50.5%.

HGA0001P and HGA0002P show a significant (15-30%) reduction well efficiency following

the injection trial in Figure D-3 in Appendix D. Contrastingly the results for HGA0003P show

only a minor change (.5-3.5%).

Figure D-4 in Appendix C shows that the well efficiency, Ew(P), is highly sensitive to changes

in the well equation exponent, P. However, regardless of the value of P, the analysis

systematically shows a decreasing trend in Ew(p) as discharge increases. At the extremes of the

range of P (P>3, P~1.5), the difference between well efficiency at each step becomes

increasingly small as Ew(P) approaches 0 and 100% respectively for al discharge rates.

46

Figure 21 Well efficiency (%) showing results for both pre-injection (dark) and post-injection (light) datasets for

HGA0001P (diamonds), HGA0002) (squares) and HGA0003P (triangles).


4.3.1 Graphical Tool

Figure 22 shows the mounding over time for the three injection bores. The linear rate of

increase in mounding for HGA0001P and HGA0002P could indicate a slow rate of physical

clogging via suspended solid buildup. The results for HGA0003P could indicate either a rapid

suspended solid buildup or mechanical clogging via gas bubbles. However, as injection

ceased in Nov 2012 it is difficult to interpret the results without further data.

4.3.2 Water Quality Analysis

The laboratory-analysed water quality sample results are shown in Table D-2 in Appendix D

with relation to the ANZECC (2000) guidelines for water quality. The water quality

parameters are all within the guideline values, with the exception of pH. As discussed in

Materials and Methods: Saturation Index, the laboratory-determined pH is shown to be

greater than the field-determined pH due to degassing of CO2. Figure C-3 shows that the field

determined pH is within the range outlined in ANZECC (2000). Both the suspended solids

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

500 700 900 1100 1300 1500 1700 1900 2100 2300 2500

Wel

l Eff

icie

ncy

(%)

Flow Rate (m3/d)

Comparison of Well Efficiency

HGA0001P: Pre-Injection (1998) HGA0001P: Post Injection (2013)

HGA0002P: Pre-Injection (1998) HGA0002P: Post Injection (2013)

HGA0003P: Pre-Injection (1997) HGA0003P: Post-Injection (2013)

47

and dissolved iron are below detectible limits, implying a low potential for physical clogging

via suspended sediment buildup and biological/chemical clogging by iron-reducing bacteria.

Figure 23 shows the turbidity results are within the target range (<5 NTU) for 93% of

HGA0001P samples and 85% of HGA0002P samples. This indicates a low to moderate

potential for physical clogging by suspended sediment buildup.

4.3.3 Bore Casing Image Analysis

Figure 24 shows that on average the biofouling layer increased by 48.0% in the slotted PVC

and 57.7% in the wire screen section due to the injection program. Aquifer testing carried out

in June 2013 reduced the biofouling layer by 15.0% and 9.6% respectively. These results are

as expected for decreased performance and are consistent with the results from the Specific

Injectivity and Aquifer Testing analysis. Figure 25 shows the change in biofouling with depth

and indicates the wire wound screen section has the greater tendency to develop a clogging

layer. The pre-injection image and a portion of the post-injection image for HGA0003P was

not able to be analysed due to very poor image quality. Refer to TableD-3 in Appendix D for

further details regarding image quality and thus reliability of biofouling estimates.

Figure 22 Flow resistance in terms of in-well mounding overlain with the standard curves from Pyne (2005).

0

5

10

15

20

25

30

35

40

Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13

Mou

ndin

g (m

)

Flow resistance (in-well mounding) for clogging diagnostics

HGA0001P HGA0002P HGA0003P

Testing and Commissioning Phase

Suspended Solids

Bacterial Growth(Abundant Food Supply)

Bacterial Growth(Limited Food Supply)

Gas bubbles

48

Figure 23 Field measured turbidity for HGA0001P and HGA0002P (HGA0003P not available) showing the target of

<5 NTU outlined by Martin (2013)

Figure 24 Bar chart showing the percentage of biofouling on the slotted PVC screens from the image analysis for the

pre-injection, post-injection and post-test pumping scenarios

0

2

4

6

8

10

12

Nov-12 Feb-13 May-13 Aug-13 Dec-13 Mar-14

Tur

bidi

ty (N

TU

)Field-Determined Turbidity

HGA0001P HGA0002P Target (<5 NTU)

0

10

20

30

40

50

60

70

80

90

100

Slotted Wire Slotted Wire Slotted Wire


Bio

fou

ling

(%)

Percentage of biofouling by screen type

Pre-Injection: June 2011 Post Injection: May 2013 Post-Pump Test: June 2013

49

Figure 25 Percentage of biofouling with depth for HGA0001P, HGA0002P and HGA0003P showing the slotted PVC and stainless steel wire wound screen sections.

82

84

86

88

90

92

94

96

98

100

102

104

106

108

110

0 25 50 75 100

Dep

th B

elow

Top

Of C

asin

g (m

)

% Biofouling

HGA0001P: Percentage of biofouling with depth

Post-Injection (May 2013)

Post-Test Pumping (June 2013)

Pre-Injection (July 2011)

Slotted PVC

Wire Screen

100

102

104

106

108

110

112

114

116

118

0 25 50 75 100

Dep

th B

elow

Top

Of C

asin

g (m

)

% Biofouling





Wire Screen

Slotted PVC

94

95

96

97

98

99

100

101

102

103

104

105

0 25 50 75 100

Dep

th B

elow

Top

Of C

asin

g (m

)

% Biofouling





Wire Screen

Slotted PVC

50

4.3.4 Saturation Index

The saturation index (SI) results for carbonate minerals shown in Figure 26 indicate the

groundwater in the injection bores are within the equilibrium range for calcite and slightly

saturated for dolomite. Figure 27 indicates that the groundwater is slightly undersaturated

with bartite and moderately undersaturated with anhydrite and gypsum. The sulphate minerals

show a minor decreasing trend as a result of injection. There is no significant change over

time with respect to the mixing of injection waters during the trial. The results are tabulated in

Table D-4 in Appendix D, showing distinction between field-measured pH and temperature

(black) and average pH and temperature (red).

Sensitivity Analysis

Figure D-5 in Appendix D shows that the SIDolomite calculations are most sensitive to changes

in temperature. SIAnhydrite and SIGypsum are robust with respect to temperature, SIBartite and

SICalcite changes by ~0.2 and SIDolomite changes by ~0.5 across the temperature range.

Figure 26 Saturation Index (SI) versus time for carbonate minerals (calcite and dolomite)

-0.5

-0.25

0

0.25

0.5

0.75

1

Jan-10 Jul-10 Feb-11 Aug-11 Mar-12 Sep-12 Apr-13 Nov-13 May-14

Satu

ratio

n In

dex,

SI

Saturation Index: Carbonates

HGA0001P - Calcite HGA0002P - Calcite HGA0003P - Calcite

HGA0001P - Dolomite HGA0002P - Dolomite HGA0003P - Dolomite

Injection Trial

51

Figure 27 Saturation Index (SI) versus time for sulphate minerals (bartite, gypsum and anhydrite)

5 Discussion


The three bores show different responses to the injection trial with respect to in-well water

levels. Marginal localised in-well mounding (~5m) was observed in HGA0001P. This could

be attributed to the cavity intersected by drilling between 115 and 118m (end of hole), the

highly fractured and karstic nature of the Wittenoom dolomite or the bore construction. 18m

of stainless steel wire wound screen was used over the permeable zone in HGA0001P, as

opposed to 12m in HGA0002P and 6m HGA0003P, making it relatively easier for the well to

transmit water into the aquifer. HGA0002P showed ~10m of in well mounding which

progressively accumulated throughout the injection trial. HGA0003 showed a rapid in-well

response to injection (~37m). The screened interval covers both the Wittenoom dolomite

(regional aquifer) and a lower permeability tertiary detrital unit containing high clay content.

The rapid onset of in-well mounding could be attributed to trapped air bubbles in the PVC

screens as a result of turbulent water injected during the testing and commissioning phase.

Alternatively, aquifer compaction due to drilling, poor well development, bore construction or

the high clay content recorded in the top 10m of the dolomite unit could also be contributing

-2.5

-2.25

-2

-1.75

-1.5

-1.25

-1

Jan-10 Jul-10 Feb-11 Aug-11 Mar-12 Sep-12 Apr-13 Nov-13 May-14

Satu

ratio

n In

dex,

SI

Saturation Index: Sulphates

HGA0001P - Gypsum HGA0002P - Gypsum HGA0003P - Gypsum

HGA0001P - Anhydrite HGA0002P - Anhydrite HGA0003P - Anhydrite

HGA0001P - Bartite HGA0002P - Bartite HGA0003P - Bartite

InjectionTrial

Bartite

Gypsum

Anhydrite

52

factors. The static water levels in Figure 10 - Figure 12 (red points), show that the

groundwater returns to pre-trial levels when the system is not operational.

It should be noted that due to their close proximity, the injection bores are not operating in

isolation. A degree of hydraulic connection exists between the injection bores and water level

responses are influenced by the operation of adjacent bores. For example, Figure 12 shows

that the in-well water level at HGA0003P continues to increase after injection in November

2012.

The contour map displaying aquifer mounding as a result of injection shows unlikely water

level peaks over observation bores (away from injection bores). The maps were created in

Surfer using the kriging interpolation method, which seeks to express trends in a dataset and

can interpolate values beyond the range in the raw data (Golden Software Inc, 2012). Despite

this, the use of the kriging method is widely accepted in the industry and in the literature

(Yang et al. 2007; Kambhammettu et al; 2011) and therefore gives a reasonable representation

of the aquifer response to injection.


The difference metric plot in Figure 16 further highlights the discrepancies between the

injection bores as discussed above. The large difference between HGA0003P and the adjacent

observational bore GWB0012M is consistent with rapid in-well mounding. The steady

increase in difference metric for HGA0002P could indicate a clogging layer buildup.

However, the adjacent observational bore HGA0036M is not screened in the Wittenoom

dolomite so the apparent reduction in performance could be attributed to a low vertical

connection between the tertiary detritals and the regional dolomite aquifer. Unexpectedly, the

difference metric for HGA0003P was slightly negative (-0.5 to -1m) indicating the water level

is higher in aquifer than inside the injection bore. The adjacent observational bore,

HGA00035M, is screened in the tertiary detrital unit not the Wittenoom dolomite. As this

difference is slight, it could be caused by a survey measurement error for the top of casing

elevation or by perched water hung up in the detrital unit. In order to accurately and

rigorously apply this method to determine bore performance, it is imperative the observational

bores are screened in the same unit as the injection bores

Similarly to specific capacity, the calculation of specific injectivity relies on the assumption

of continuous flow. The analysis was limited to a period of assumed continuous operation by

53

excluding shutdown periods due to the testing and commissioning phase and mechanical

pump failure (~months). However, during the analysis period the system was often

temporarily shutdown (~hours/days) and which has implications for the short-term specific

injectivity calculations. The MAR system is interlinked with mine‟s dust suppression system.

Water carts regularly take water from the same storage dam that supplies the MAR system for

dust suppression purposes, a key safety and environmental hazard on site. During particularly

hot and dusty periods, water managers turn off the MAR system to ensure adequate volumes

of water are available for dust suppression. Measured drawup between consecutive water

level measurements for the short-term specific injectivity calculations were sometimes very

small (<5cm), indicating the system could be in recovery. These small drawups are not

representative of the volume of water injected between measurements and result in extremely

large and unrealistic specific injectivity values.

Due to mine site reporting requirements, field measurements of volume and groundwater

levels were not recorded on the same day. In order to calculate specific injectivity, the volume

of water injected at the time of water level measurement was estimated based on a continuous

flow assumption. As discussed above, this introduces uncertainty into the calculated value.

However, the interpretation of the relative changes in specific injectivity and analysis of

temporal trends is still a valid approach. The long-term specific injectivity decreased during

the study period for all injection bores; HGA0001P by 47.9%, HGA0002P by 58.7% and

HGA0003P by 35.2% (prior to being shut down prematurely). This reduced performance

could be linked to a persistent clogging mechanism or the injection capacity limits of the

regional system. The rate of performance decrease can be used in a management capacity to

plan a tailored maintenance schedule for back flushing and rehabilitation. Based on a

nominated minimum performance threshold, for example 100m3/d/m, the rate of performance

decrease could be used to predict the time period until the threshold is breached for each bore

and thus requires backflushing.

The long-term specific injectivity analysis takes into consideration the regional declining

trend in groundwater levels. As Figure 20 shows, the „dry‟ period of below average rainfall

has a moderate influence over the decline in groundwater levels. However, the decline in

groundwater levels begins before the CDFM dry period, indicating an additional influence on

the regional groundwater system. Water supply from dolomite borefields, dewatering

activities at Rio Tinto‟s Hope Downs mine site from 2006 and at BHP Billiton‟s Mining Area

C from 2010 likely have an influence on groundwater levels.

54

The Hantush-Bierschenk method used to calculate well efficiency is applicable on the basis of

a number of assumptions and conditions. These are listed below in Table 8 with a comment

on their validity for this system. In addition, the method to determine parameters B and C is

based on the extrapolation of drawdown data, therefore introducing a source of uncertainty

(Kruseman and de Ridder, 1994). The sensitivity analysis further highlights the uncertainties

surrounding the well efficiency calculations as changes to the well equation coefficient P have

huge implications on the well efficiency results.

Table 8 Assumptions of the Huntush-Bierschenk method (Kruseman and de Ridder, 1994)

Assumption Remark The aquifer is confined, leaky or unconfined

The regional Wittenoom dolomite aquifer is leaky.

The aquifer has a seemingly infinite extent

The Wittenoom dolomite is restricted in the south due to the outcropping of the Brockman formation and has boundary conditions associated by mine site dewatering activities. However, given the duration of the injection trial this is unlikely to have an effect.

The aquifer is homogenous, isotropic and of uniform thickness

The aquifer is characterized by localised zones of fracturing and weathering. The Wittenoom dolomite pinches out in the south due to the northward dipping stratigraphy.

The piezometric surface is horizontal

The system was unlikely to have fully recovered from injection prior to test pumping so the piezometric surface may not have been horizontal

The aquifer is pumped at increasing discharge rates

The discharge rates increased for each step

The well fully penetrates the aquifer to ensure horizontal flow

The injection bores partially penetrate the Wittenoom dolomite, indicating a vertical flow component near the end of each the well. HGA0002P and HGA0003P are also screened in the tertiary detritals.

The well efficiency calculations in Figure 21 show that the well becomes less efficient at

higher discharge rates due to the turbulent water and the dominance of the well loss

component (CQ2). Compared to HGA0001P, the well efficiency for HGA0002P was

unexpectedly low and could be attributed to the limited use of stainless steel screens in the

bore construction or water inflowing from a narrow fracture zone resulting in high local

velocities. The results for HGA0003P indicate the bore is operating efficiently which is not

consistent with the operational performance analysis. The step-drawdown test for HGA0003P

could have been conducted over lower flow rates than experienced during the injection trial,

which would over estimate its efficiency with respect to injection. Relative to the changes

observed in HGA0001P and HGA0002P, a very small decrease in well efficiency is observed

as a result of injection. This could be attributed to the lower volume of water injected into

55

HGA0003P during the trial or the removal of the bubble lock due to pumping. The „pre-

injection‟ step tests were conducted at the time of drilling, 15 years before the injection trial.

Ideally, the pre-injection testing should be conducted immediately before the injection trial

commences and the effect of the time lag is difficult to separate from the effect of the

injection trial.


The application of the graphical tool used by Pyne (2005) to determine the clogging

mechanism is very limited and subjective. The graphical tool does not account for the

chemical clogging process where precipitation reactions obstruct well viability. However, its

simplicity makes it a quick, easy and practical tool for industry use and would be very

valuable for analysis of future MAR systems within the mining industry.

The clogging layer is easily observable in the bore casing images. Changes to the size of the

clogging layer by drivers such as injection and test pumping are well quantified by the image

analysis. However it is interesting to note that there was a significant portion (~25%) of

clogging layer observed before and unrelated to injection. The method is dependent on high

image quality and this was the limiting factor in analysis for HGA0002P and HGA0003P. The

original light settings on each raw image were different and therefore analysed using different

processing settings as appropriate to enhance the clogging features. This introduces

uncertainty into the analysis, as changes in area of biofilm could be the result of the subjective

change in contrast, brightness and gamma settings. Ideally, the raw images should have the

same light settings and filters in order to undergo the same processing method. Results for

HGA0001P were as expected with an increase the biofilm covering the slotted sections as a

result of injection and a smaller decrease in biofilm as a result of pump testing.

The PHREEQC geochemical modeling results for saturation index rely heavily on the choice

of thermodynamic database which contain equilibrium constant data from a variety of

literature sources (Packhurst and Appelo, 1999). The minteq.dat database was selected for use

as it best replicated previous PHREEQC modeling undertaken by Barber (2010). Todorov et

al. (2006) describe the mindteq.dat database as the “most complete literature database”

available for PHREEQC modeling. Results could vary with a different choice of database. In

addition, uncertainties are derived from the accuracy of chemical analysis in the laboratory,

equilibrium constant data and the method of calculating ion activity products (Deutsch, 1997).

56

Previous geochemical modeling undertaken by Barber (2010) identified the dissolution of

dolomite as a potential risk to the injection trial as a result of mixing source and receiving

waters. The dissolution risk was predicted to reduce if the source waters became partially

aerated prior to injection and reached equilibrium with atmospheric CO2, increasing the

saturation index for dolomite. The modeling results in Figure 26 validate these predictions

and show that SIDolomite is moderately saturated during the injection trial. The groundwater

samples from the injection bores were collected through a tap at the headworks, before the

water was injected into the receiving aquifer. Therefore, the samples represent an „altered‟

form of source water, which has been abstracted from dewatering activities in the open pit,

pumped into a storage dam then pumped again to the MAR system.

The sulphate minerals gypsum, anhydrite and bartite were moderately undersaturated which

theoretically indicates they are likely to dissolve in the aquifer matrix. However, Deutsch

(1997) asserts that an undersaturated mineral in an aquifer scenario can be interpreted as not

being present in the aquifer matrix or not reactive.

6 Conclusions

MAR is an important tool for water managers and, in addition to traditional applications of

water recycling and stormwater harvesting, has huge potential to aid water management in the

mining industry. The MAR system at Mining Area C manages surplus water from BWT

operations and the period from April 2012 to April 2013 was studied. The system returned

excess dewatering supply from localized orebody aquifers to the regional dolomite aquifer via

three injection bores. Clogging at the injection surface through the precipitation of carbonates,

sedimentation and the growth of iron bacteria were identified as risks to the system prior to

operation.

After 12 months of operation, an average 5m of mounding was observed across the study area

with groundwater levels increasing from 577 to 582 mRL. Water levels within the injections

bores exhibited different responses, from 35m maximum mounding in HGA0003P to 2m in

HGA0001P. The in-well water levels returned to static levels when the system was not

operating for brief periods.

The system operational performance was quantified by the difference metric, specific

injectivity and well efficiency and all were shown to decrease throughout the study period.

The specific injectivity decreased for all three bores at differing rates, potentially indicating

57

different flow resistance mechanisms. As expected, the well efficiency decreased for all bores

as a result of injection; a significant decrease at HGA0001P and HGA0002P (~∆25%) and a

minor decrease at HGA0003P (~∆2%). Factors other than clogging, such as well design, bore

construction and localised secondary porosity features could also contribute to the differences

in performance.

The graphical diagnostic tool indicates that HGA0003P is likely affected by rapidly-acting air

entrapment and ongoing sedimentation, while both HGA0001P and HGA0002P by slow

sedimentation or iron bacteria buildup. The growth of iron bacteria is unlikely as the water

chemistry analysis shows the dissolved iron is below the detection limit. The bore casing

image analysis shows that the well screen blockages increased by 48% on the PVC screens

and 58% on the stainless steel wire screens. The test pumping activities only had a minor

remediation effect, removing 9-15% of the clogging layer. The geochemical clogging

potential was investigated with PHREEQC to determine the saturation indices of sulphates

and carbonates. Dolomite was moderately saturated, indicating it has potential to precipitate

and calcite was within the equilibrium range. The groundwater was undersaturated with

respect to sulphates.

The following are recommendations based on learnings from the MAC MAR trial for future

systems in an operational mining environment:

Screen injection bores in only one hydrostratigraphic unit and screen the adjacent

observational bores in the same unit;

Sampling and laboratory analysis of the biofilm layer to determine its composition;

Apply the same step discharge rates for pre- and post aquifer pump testing to increase

confidence in well efficiency comparisons;

Isolate the operation of the MAR system from the dust suppression management

system to validate the assumption of continuous flow which will strengthen

confidence in specific injectivity calculations;

Conduct short-term specific injectivity tests periodically throughout the injection trial

to investigate persistent clogging. Once the system has been operating continuously,

shut down and allows 90% aquifer recovery. The test would measure the short term

response when the system restarts.

58

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A.1

Appendix A – Introduction

A.2

Figure A-1 Location of Flat Rocks rainfall gauge (20km north of Mining Area C), DoW site 505011 (RPS Aquaterra, 2014)

A.3

Figure A-2 Stratigraphic Units of the Hamersley Region (Kneeshaw 2008).

A.4

Figure A-3 Construction bore log for HGA0001 (TP4) from Woodward Clyde (1998)

A.5

Figure A-4 Construction bore log for HGA0002 (TP5) from Woodward Clyde (1998)

A.6

Figure A-5 Construction bore log for HGA0003P (TP3) from Woodward-Clyde (1997)

A.7

Figure A-6 Local 20k geology interpretation at the injection site, A Deposit, Mining Area C..

A.8

Table A-1 Name and description of geology codes used in.

Code Name Description

Cza Alluvium (consolidated) Alluvium - Consolidated & partly clay, silt, sand, gravel in low lying fan deltas and braided flood plains

Qa Alluvium (drainage channels)

Alluvium in drainage channels

Czcb Colluvium & alluvium (chert & BIF clasts)

Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size Chert & BIF clasts

Czc Colluvium & alluvium Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size clasts

PHbw Brockman - Mt Whaleback Shale

Mt Whaleback Shale Member - Ferruginous shale with chert beds. Central chert towards base

PHbd Brockman - Dales Gorge (BIF & Shale)

Dales Gorge Member - BIF and shale (DS 1-16) beds - D2/D3/D4 zones. Basal D1 zone of Chert, BIF and shale (CS 1-6)

AHdb Wittenoom - Bee Gorge Member

Wittenoom Form - Bee Gorge Member - Interbedded dolomite shale, chert and tuff. Turburditic in part

AHmm Marra Mamba - MacLeod Member

Interbedded Shale (MS 1-13) and ferruginous, with podded cherts, some ex carbonate

AHmn Marra Mamba - Mt Newman Member

Podded BIF - carbonate and silicate rich with interbedded shale and carbonate bands (NS 1-8)

AHmu Marra Mamba - Nammuldi Member

Yellow cherty BIF with thin shale interbeds (US 1-18). Carbonate rich in lower half

AHr Mt McRae Shale Mt McRae Shale - Pink buff weathering, pyritic black shale with massive yellow to grey chert interbeds

AHs Mt Sylvia Formation Mt Sylvia Formation - Pink weathering shale, siltstone and chert and 3 BIF markers. Block BIF (6m Brunos Band) at top.

Czcg Indurated colluvium (Canga)

Indurated Colluvium (Canga) consisting of iron ore pebbles in hard vitreous goethite or calcareous matrix

Czl Ferricrete (laterite) Ferricrete (laterite) Czco Colluvium & alluvium (ore

clasts) Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size clasts Ore Clasts

Czcm Colluvium & alluvium (ore, chert & BIF clasts)

Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size clasts Ore, Chert & BIF Clasts

H2 Martite goethitic supergene ore (ochereous)

Martite (Ochreous) goethite supergene iron ore preserving bedrock fabric. Mesozoic/Tertiary in age.

Tmco Alluvial Haematite (red ochre detritals)

Alluvial haematite siltstone/conglomerate (red ochre detritales) in paleochannels. Earl – mid miocene

A.9

Figure A-7 MAR conceptual model (BHP Billiton, 2011)

B.1

Appendix B - Literature Review

B.2

Table B-1 Examples of MAR systems adapted from NWQMS (2009)

MAR System

Type

Description Locality of example

Aquifer Storage and Recovery (ASR)

Injection into a well for storage and recovery from the same well to a confined or unconfined aquifer.

Grange, and Tea Tree Gulley, Adelaide, South Australia

Aquifer Storage, Recovery and Transport (ASTR)

Injection into a well for storage and recovery from a different well for water quality treatment purposes.

Salisbury, South Australia

Vadose Zone Wells

Injection into a dry well to allow infiltration to a deep unconfined aquifer.

Phoenix, United States

Percolation tanks and recharge weirs

Construction of a dam or weir in an ephemeral stream channel to allow infiltration to unconfined aquifers and subsequent recover downstream.

Callide Valley, Queensland

Rainwater harvesting

Diversion of roof runoff into a well or sump filled with sand or gravel.

Perth, Western Australia

Bank filtration Extraction from a well near or under a surface water body to induce infiltration.

Berlin, Germany

Infiltration galleries

Infiltration through geotechnically-stabilised buried trenches to an unconfined aquifer.

Floreat Park, Western Australia

Dune filtration Construction of a pond in a dune to allow infiltration for extraction at lower elevations.

Amsterdam, The Netherlands

Infiltration ponds Construction of a pond or channel off-stream to allow infiltration to an underlying unconfined aquifer;

Burdekin Delta, Queensland

Soil aquifer treatment

Diversion of treated sewage effluent to infiltration ponds.

Alice Springs, Northern Territory

Underground dams

Construction of a trench across an ephemeral streambed, backfilled with low permeability material for flood management purposes.

Northeast Brazil

Sand dams Construction of a sand dam on an ephemeral stream to create an artificial aquifer following periods of inundation.

Kitui, Kenya

Recharge releases Construction of a dam on an ephemeral stream, followed by the slow release of water to promote downstream infiltration.

Little Para River, South Australia

B.3

Figure B-1 Types of MAR schemes (Dillon, 2005)

C.1

Appendix C – Materials and Methods

C.2

Table C-1 Casing Image Analysis: The colour settings used in the downhole camera study for each of the images. Note

the HGA0003P pre-injection image quality was too poor to analyse.

Image Brightness Contrast Gamma HGA0001P: Pre-injection (July 2011) 76 127 0.45 HGA0001P: Post-injection (May 2013) -21 127 0.01 HGA0001P: Post-testing (June 2013) 18 127 0.01 HGA0002P: Pre-injection (July 2011) 81 127 5.39 HGA0002P: Post-injection (May 2013) -122 127 6.99 HGA0002P: Post-testing (June 2013) -122 127 6.99 HGA0003P: Post-injection (May 2013) -21 127 0.01 HGA0003P: Post-testing (June 2013) 18 127 0.01

Figure C-1 Casing Image Analysis: an example of the Image Histogram, which counts the number of pixels of given

colour (Index: Black = 0), to determine the percentage of biofouling.

C.3

Figure C-2 Saturation Index modeling: field-determined pH versus laboratory determined pH

Figure C-3 Saturation Index modeling: field determined pH versus time

7.0

7.5

8.0

8.5

9.0

7.0 7.5 8.0 8.5 9.0

Lab

orat

ory

dete

rmin

ed p

H

Field determined pH

Field pH vs Lab pH

HGA0002P

HGA0003P

HGA0001P

7.0

7.5

8.0

8.5

9.0

Apr-12 May-12 Jul-12 Aug-12 Oct-12 Dec-12 Jan-13 Mar-13

Fiel

d de

term

ined

pH

Field determined pH

D.1

Appendix D - Results

D.2

Figure D-1 The Hantush-Bierschenk method for determining ∆S

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

1.100

1.200

0.1 1.0 10.0 100.0

Dra

wdo

wn

(m)

Time (Minutes)

HGA0001P Pre-Injection:Hantush-Bierschenk Method for determining ∆s

∆Sw2 = 0.180

∆Sw1 = 0.395

∆Sw3 = 0.105

∆Sw4 = 0.64

∆Sw5 = 0.64

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

0 1 10 100

Dra

wdo

wn

(m)

Time (Minutes)

HGA0001P Post-Injection:Hantush-Bierschenk Method for determining ∆s

∆Sw2 = 0.41

∆Sw1 = 0.22

∆Sw3 = 0.42

∆Sw4 = 0.64

D.3

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.8000.1 1 10 100

Dra

wdo

wn

(m)

Time (Minutes)


∆Sw3 = 0.42

∆Sw1 = 0.21

∆Sw4 = 0.68

∆Sw5 = 1.2

∆Sw2= 0

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40 0.1 1 10 100

Dra

wdo

wn

(m)

Time (Minutes)

HGA0002P Post-Injection:Hantush-Bierschenk Method for determining ∆s

∆Sw2 = 0.42

∆Sw1 = 0.3

∆Sw3 = 0.68

∆Sw4 = 1.2

D.4

`

-

2

4

6

8

10

12

14

16 0.1 1 10 100

Dra

wdo

wn

(m)

Time (Minutes)


∆Sw2 = 2.35

∆Sw1 = 3.6

∆Sw3 = 3.2

∆Sw4 = 1.75

∆Sw5 = 2.0

-

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00 0.1 1 10 100

Dra

wdo

wn

(m)

Time (Minutes)

HGA0003P Post-Injectoin:Hantush-Bierschenk Method for determining ∆s

∆Sw2 = 6.2

∆Sw1 = 6.3

∆Sw3 = 8.6

∆Sw4 = 12.5

D.5

Table D-1 The Hantush-Bierschenk method for determining Sw(n)/Qn

Step Qn (L/s) Qn (m3/d) ∆Sw (m)

Sw(n) (m)

Sw(n)/Qn (d/m2)

HGA0001P Pre-Injection: ∆t = 100 min 1 10 864 0.40 0.40 0.000458333 2 13 1123 0.18 0.58 0.000512912 3 16 1382 0.11 0.68 0.000492764 4 19 1642 0.19 0.87 0.000530451 5 25 2160 0.28 1.15 0.000530556

HGA0001P Post-Injection: ∆t = 60 min

1 6 518.4 0.22 0.22 0.000424383 2 14 1209.6 0.41 0.63 0.000520833 3 21 1814.4 0.42 1.05 0.000578704 4 28 2419.2 0.64 1.69 0.000698578

HGA0002P Pre-Injection: ∆t = 100 min

1 10 864 0.21 0.21 0.000243056 2 13 1123 0.00 0.21 0.000186999 3 16 1382 0.20 0.41 0.000294501 4 19 1642 0.14 0.55 0.000332521 5 25 2160 0.16 0.70 0.000324537


1 8 691.2 0.3 0.3 0.000434028 2 14 1209.6 0.42 0.72 0.000595238 3 21 1814.4 0.68 1.4 0.000771605 4 28 2419.2 1.2 2.6 0.001074735

HGA0003P Pre-Injection: ∆t = 100 min

1 8 691.2 3.6 3.6 0.005208 2 12 1036.8 2.35 5.95 0.005739 3 16 1382.4 3.2 9.15 0.006619 4 18 1555.2 1.75 10.9 0.007009 5 20 1728.0 2 12.9 0.007465


1 6 518.4 6.3 6.3 0.012153 2 10 864.0 6.2 12.5 0.014468 3 15 1296.0 8.6 21.1 0.016281 4 20 1728.0 12.5 33.6 0.019444

D.6

Figure D-2 The Hantush-Bierschenk method for determining parameters B and C

4.5E-04

4.6E-04

4.7E-04

4.8E-04

4.9E-04

5.0E-04

5.1E-04

5.2E-04

5.3E-04

5.4E-04

5.5E-04

0 500 1000 1500 2000 2500

S w(n

)/Qn

(d/m

2 )

Q (m3/d)

HGA0001P Pre-Injection:Hantush-Bierschenk method for determining parameters B and C

C = gradient = 4.89 x 10-8

B= y-intercept = 4.35 x 10-4

0.0E+00

1.0E-04

2.0E-04

3.0E-04

4.0E-04

5.0E-04

6.0E-04

7.0E-04

8.0E-04

0 500 1000 1500 2000 2500 3000

S w(n

)/Qn

(d/m

2 )

Q (m3/d)

HGA0001P Post-Injection:Hantush-Bierschenk method for determining parameters B and C

C = gradient = 1.39 x 10-7


D.7

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

0 500 1000 1500 2000 2500

S w(n

)/Qn

(d/m

2 )

Q (m3/d)

HGA0002P Pre- Injection:Hantush-Bierschenk method for determining parameters B and C

C= gradient = 9.30 x 10-8


0.00E+00

2.00E-04

4.00E-04

6.00E-04

8.00E-04

1.00E-03

1.20E-03

0 500 1000 1500 2000 2500 3000

S w(n

)/Qn

(d/m

2 )

Q (m3/d)


C = gradient =3.64 x 10-7

D.8

0.00E+00

2.00E-04

4.00E-04

6.00E-04

8.00E-04

1.00E-03

1.20E-03

0 500 1000 1500 2000 2500 3000

S w(n

)/Qn

(d/m

2 )

Q (m3/d)


C = gradient =3.64 x 10-7

0.00E+00

5.00E-03

1.00E-02

1.50E-02

2.00E-02

2.50E-02

0 400 800 1200 1600 2000

S w(n

)/Qn

(d/m

2 )

Q (m3/d)


C = gradient = 5.85 x 10-6

D.9

Figure D-3 The reduction in well efficiency (EW) between pre-injection and post-injection data

15.9

1%

17.9

1%

20.0

3%

22.2

6% 27.0

1%

29.8

1%

27.0

4%

25.3

6%

24.5

3%

24.6

7%

1.57% 0.45% 1.32% 2.30%3.57%

0%

5%

10%

15%

20%

25%

30%

35%

1 2 3 4 5

Red

uctio

n in

Wel

l Eff

icie

ncy

(Ew

)

Step

Well Efficiency Reduction


D.10

Figure D-4 Results of the sensitivity analysis where the well efficiency at each step is calculated using a range of P values

0%10%20%30%40%50%60%70%80%90%

100%

1.5 2 2.5 3 3.5

Wel

l Eff

icie

ncy

(Ew)

Exponent (P)

Sensitivity Analysis: HGA0001P (Pre-Injection)

Step 1

Step 2

Step 3

Step 4

Step 5

0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

100.00%

1.5 2 2.5 3 3.5

Wel

l Eff

icie

ncy

(Ew)

Exponent (P)

Sensitivity Analysis: HGA0001P (Post-Injection)

Step 1

Step 2

Step 3

Step 4

D.11

0%10%20%30%40%50%60%70%80%90%

100%

1.5 2 2.5 3 3.5

Wel

l Eff

icie

ncy

(Ew)

Exponent (P)


Step 1

Step 2

Step 3

Step 4

Step 5

0%10%20%30%40%50%60%70%80%90%

100%

1.5 2 2.5 3 3.5

Wel

l Eff

icie

ncy

(Ew)

Exponent (P)

Sensitivity Analysis: HGA0002P (Post-Injection)

Step 1

Step 2

Step 3

Step 4

0%10%20%30%40%50%60%70%80%90%

100%

1.5 2 2.5 3 3.5

Wel

l Eff

icie

ncy

(Ew)

Exponent (P)


Step 1Step 2Step 3Step 4Step 5

0%10%20%30%40%50%60%70%80%90%

100%

1.5 2 2.5 3 3.5W

ell E

ffic

ienc

y (E

w)

Exponent (P)

Sensitivity Analysis: HGA0003P (Post-injection)

Step 1Step 2Step 3Step 4

D.12

Table D-2 Water quality sample results undertaken during injection with the ANZEC 2000 Guideline values; grey indicates that the analyte is below the detectible limit

Analyte 23-Jun-10 25-Oct-10 13-Dec-12 24-Jan-13 11-Jun-13 12-Jun-13 13-Dec-12 24-Jan-13 15-Jun-13 23-Jun-10 14-Jun-12 9-Jun-13Aluminium (mg/L) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.055Arsenic (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.007Barium (mg/L) 0.001 0.001 0.008 0.009 0.007 0.006 0.008 0.008 0.009 0.007 0.011 0.005 0.7Bicarbonate Alkalinity (mg/L) 253 258 249Boron (mg/L) 0.19 0.15 0.2 0.22 0.22 0.17 0.2 0.22 0.21 0.27 0.22 0.37Cadmium (mg/L) 0.0001 0.0001 0.001 0.0001 0.0001 0.0001 0.001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002^Calcium (mg/L) 68 59 51 34 49 48 48 37 51 63 41 50Chloride (mg/L) 43 46 42 40 34 34 42 38 37 51 42 37 250Chromium (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001Copper (mg/L) 0.001 0.001 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 0.0014^Fluoride (mg/L) 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 1.5Iron Sol. (mg/L) 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.3Lead (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.0034^Magnesium (mg/L) 42 39 34 33 33 32 34 31 35 39 34 35Manganese (mg/L) 0.001 0.001 0.002 0.001 0.001 0.038 0.001 0.001 0.042 0.001 0.001 0.001 1.9Mercury (mg/L) 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.00006Molybdenum (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.05Nickel (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.028 0.013 0.001 0.011^pH (pH) 8.01 8.06 8.32 8.11 8.02 8.07 8.29 8.23 7.92 8.01 8.05 8.05 6.5-8.0^^^Potassium (mg/L) 10 9 10 8 9 8 10 9 9 11 9 9Selenium (mg/L) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.005Silica (mg/L) 38.2 34.1 30.6 28.7 32.1 32.1 30 27 31.7 34 22.2 33.2Sodium (mg/L) 36 33 67 33 33 32 36 33 34 37 35 35 180Sulphate as SO4 2- (mg/L) 49 43 49 40 42 42 47 39 44 52 43 45 500Suspended Solids (SS) (mg/L) 1 5 5 22 5 5 5 5 5 5Total Dissolved Solids(mg/L) 484 454 372 323 330 288 400 339 358 435 351 351 500Zinc (mg/L) 0.005 0.005 0.056 0.005 0.007 0.005 0.035 0.005 0.005 0.005 0.005 0.005 0.008

Notes for ANZECC 2000 Fresh Water Guidelines for slightly-moderately disturbed ecosystems Grey text Below detectible limit^ Guideline is based on a hardness (CaCO3) of 30 mg/L. ^^ This range is for upland and lowland rivers in South-west Australia^^^ Denotes value for a lowland river in South-west Australia

ANZECC 2000 Guidelines


D.13

Table D-3 Image quality estimates and comments on image reliability for the downhole camera study for each 1m section of HGA0001P, HGA0002P and HGA0003P.

HGA0001P Depth

From (m)

Depth To (m)

Casing Type

Pre-Injection (July 2011) Post-Injection (May 2013) Post-Test Pumping (June 2013) Quality

Estimate Comments Quality Estimate Comments Quality

Estimate Comments

82 83 S G Depth scale of image out with respect to the May 2013 image.

A G Depth scale of image out with respect to the May 2013 image. 83 84 S G A

Under estimate do to removal of biofilm layer from OTV tool. "Stripes"

G 84 85 S G Image is clear, no biofouling. A G

Biofouling layer doesn‟t cover slots, just blank PVC 85 86 S G

Analysis by visual inspection

A G 86 87 S G A G 87 88 S G P Not useable, contains join P Not useable, contains join 88 89 S G G A Different image, light filter

looks different 89 90 S G G A 90 91 S G G A Biofouling layer looks to be

decreasing 91 92 S G G A 92 93 S G G A Screen looking much clearer 93 94 S G G A Over estimate, contains join. 94 95 S G G A Screen looks very clear 95 96 S G G A 96 97 S G G A 97 98 S G G A 98 99 S G G A 99 100 S P Not useable, contains join P Not useable, contains join P Not useable, contains join

100 101 W G Some 'floaties'; turn red not counted in biofilm

A

Biofouling covers most of screen. Image slightly unclear but still useable

P Two different light settings 101 102 W G A A 102 103 W A

A A

103 104 W A

A A 104 105 W A

Floaties in water casting a shadow and cover the screen.

A A 105 106 W A A A 106 107 W A P Image gets quite dark and

un useable from 106 - 112m. Looks like biofouling increases to full depth. Use the average as an estimate.

A 107 108 W A P A 108 109 W P Warped but still useable P A 109 110 W P As above. 109.4-110.4m P P

Image is getting quite dark, almost unuseable 110 111 W P

Short section to the end of the screen P P

D.14

HGA0002P

Depth From (m)

Depth To (m)

Casing Type



Estimate Comments

100 101 S A Image is clear P Includes the join section A Includes the join section 101 102 S A

Analysis by visual inspection

P A 102 103 S A P A 103 104 S A P A 104 105 S A P Image very light A 105 106 S P

P A

106 107 S P Not useable, contains join P Not useable, contains join A Not useable, contains join 107 108 S P

P A

108 109 S P Dark streak in image (not biofilm), therefore over estimate

P A "Stripes" 109 110 S P P A Very pixilated 110 111 S P P Image is pixilated, however

biofouling is still observable A

111 112 S P P A Very light 112 113 S P Not useable, contains join P Not useable, contains join P Not useable, contains join 113 114 W P Very dark P Pixilated A Slightly distorted 114 115 W P A Still distorted but better A Sharper than above 115 116 W P Not in focus A A Slightly distorted 116 117 W P Image too dark and distorted to

use effectively; assume average for the remainder of the screened section

A A

117 118 W P G Image is much sharper G Used as an example in write up

118 119 W P G End of OTV survey G End of OTV survey

D.15

HGA0003P Depth

From (m)

Depth To (m)

Casing Type



Estimate Comments

94 95 S P

Image blurry and out of focus

P

Image not useable due to light settings: too dark then too light

G

No biofilm; slots are clear on PVC

95 96 S P P G 96 97 S P P G 97 98 S P P G 98 99 S P P G 99 100 S P P P Not useable, contains join

100 101 W P

Image difficult to process due to particulates floating in the water.

A Raw image looks like more biofouling than shows up in the enhanced image

A Particulates floating in the water, creates an over estimate

101 102 W P A A 102 103 W P A A 103 104 W P A A 104 105 W P A A Different light settings

Image quality scale Description G Good The image is clear and sharp; the biofouling layer is easily distinguishable from the screen A Acceptable The image is reasonably clear; particulates suspended in the water affect screen visibility P Poor The image is distorted or warped; the colouring is too dark or too light

Casing Type S Slotted PVC 1mm aperture

W Wirewound stainless steel screen

D.16

Table D-4 Key inputs and outputs for the PHREEQC modeling (red = average values used in the absence of measured values) IN

PUT

Bore HGA0001P HGA0002P HGA0003P Date 23/6/10 25/10/10 13/12/12 24/1/13 11/6/13 13/6/13 22/10/13 13/12/12 24/1/13 15/6/13 22/10/13 23/6/10 14/6/12 9/6/13 Temperature 31 32.7 30.0 28.5 30.0 30.0 28.3 29.8 28.6 29.8 27.7 31.0 29.9 30.0 pH (field) 7.2 7.1 7.6 7.9 7.6 7.6 7.6 7.7 7.9 7.7 7.4 7.2 7.6 7.4 log pCO2 -1.77 -1.65 -2.34 -2.61 -2.37 -2.38 -2.36 -2.35 -2.60 -2.34 -2.16 -1.99 -2.22 -2.03

OU

TPU

T

Modelled pH 7.20021 7.10198 7.70208 7.90806 7.70014 7.70212 7.60867 7.70867 7.90056 7.70976 7.40789 7.20471 7.60298 7.40729 SI_Anhydrite -1.8744 -1.9611 -1.9775 -2.2082 -2.0384 -2.0427 -2.1886 -2.0089 -2.1817 -2.0146 -2.1994 -1.8725 -2.1024 -2.0099 SI_Bartite N/A N/A -1.0738 -1.0419 -1.177 -1.2392 -1.0569 -1.0725 -1.1036 -1.0542 -0.9986 -1.1428 -0.9657 -1.3033 SI_Calcite 0.0182 -0.0996 0.3235 0.294 0.2843 0.2708 -0.0096 0.3046 0.3264 0.3435 -0.225 -0.0151 0.1606 0.0431 SI_Dolomite 0.1356 -0.0637 0.7748 0.8706 0.7003 0.6689 0.1527 0.7618 0.8714 0.8259 -0.2855 0.0701 0.5432 0.2344 SI_Gypsum -1.9595 -2.0627 -2.0531 -2.2694 -2.114 -2.1183 -2.2474 -2.0821 -2.243 -2.0878 -2.2533 -1.9576 -2.178 -2.0855

D.17

Figure D-5 Sensitivity Analysis for Saturation Index (SI) with respect to temperature for carbonates and sulphates

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)

Sensitivity Analysis for HGA001P23/06/2010 - Carbonates

Anhydrite

Gypsum-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)

Sensitivity Analysis for HGA001P23/06/2010 - Sulphates

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


D.18

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)

Sensitivity Analysis for HGA001P24/01/2013- Sulphates

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


D.19

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


D.20

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


D.21

Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)

Sensitivity Analysis for HGA003P14/06/2012- Carbonates

Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


Calcite

Dolomite

-0.5

0

0.5

1

1.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)

Sensitivity Analysis for HGA003P09/06/2013- Carbonates

Anhydrite

Gypsum

Barite

-2.5

-2

-1.5

-1

-0.5

25 27 29 31 33 35

Satu

ratio

n In

dex

(SI)

Temperature ( C)


E.1

Appendix E – Research Proposal

E.2



Lily Smith 208262

Lily Smith 21224043 October 2012

E.3

Cover Photo

The Pilbara near Mining Area C. Photo by author.

Abbreviations

AOC Assimilable Organic Carbon

ASR Aquifer Storage and Recovery

ASTR Aquifer Storage, Transport and Recovery

BIF Banded Iron Formation

BOM Bureau of Meterology

BWT Below Water Table

DEC Department of Conservation

DOC Dissolved Organic Carbon

FMG Fortescue Metals Group

GDE Groundwater Dependent Ecosystems

IGRAC International Groundwater Resource Assessment Centre

MAC Mining Area C

MAR Managed Aquifer Recharge

mbgl meters below ground level

MFI Membrane Filtration Index

NWQMS National Water Quality Management Strategy

ORP Oxidation-Reduction Potential

PFI Parallel Filter Index

PHREEQC PH (pH), RE (redox), EQ (equilibrium), C (programming language)

PVC Polyvinyl chloride

SWL Static Water Level

TD Tertiary Detritals

TOC Total Organic Carbon

TSS Total Suspended Solids

VWP Vibrating Wire Piezometer

E.4

List of Figures

Figure 1 Mining Area C location and site layout, showing the components of the MAR

system (Figure created by author)............................................................................................ 14

Figure 2 MAR schematic diagram showing the seven system components of an MAR project

outlined by Dillon et al. (2009). ............................................................................................... 16

Figure 3 Historic monthly rainfall record at Flat Rocks gauge, 20km north of MAC ............ 17


Pilbara Grid), unit codes from MAC is located in the Fortescue River basin within the Weeli

Wolli Creek system. The regional groundwater system flows to the east towards Weeli Wolli

Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to



Wolli Spring (RPS Aquaterra, 2008). ...................................................................................... 18


Pilbara Grid) , unit codes from MAC is located in the Fortescue River basin within the Weeli

Wolli Creek system. The regional groundwater system flows to the east towards Weeli Wolli





Figure 6 Geological cross section for HGA0002P looking east along 709770 (Central Pilbara

Grid), , unit codes from MAC is located in the Fortescue River basin within the Weeli Wolli

Creek system. The regional groundwater system flows to the east towards Weeli Wolli





Figure 7 A graphical diagnostic tool for determining different mechanisms of clogging (Pyne,

2005) ........................................................................................................................................ 24

E.5

Figure 8 Hydrograph at observation bore GWB0012M showing a declining regional

groundwater trend prior to injection (1997 – 2012)................................................................. 32

Figure 9 An example of a 1m section showing both the raw and enhanced OTV image ........ 35


Figure 11 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0002P38


Figure 13 Pre-injection groundwater surface in mRL from the 09/02/2012 (0.1m contour

intervals) .................................................................................................................................. 39

Figure 14 Post-injection groundwater surface in mRL from the 11/4/2013 (0.1m contour

intervals) .................................................................................................................................. 39

Figure 15 Groundwater mounding in m following 12-months of injection (0.1m contour

intervals) .................................................................................................................................. 40

Figure 16 Plot of the difference between the injection bore and the closest observation bores

groundwater levels ................................................................................................................... 40

Figure 17 Long-term specific Injectivity, SiL over time for each injection bore ..................... 42

Figure 18 Short-term specific injectivity, SIS over time for each injection bore ..................... 43

Figure 19 The cumulative deviation from mean rainfall plot showing a dry period prior to the

commencement of the groundwater injection trial .................................................................. 44

Figure 20 Matching theCDFM plot with the GWB0012M hydrograph to determine a

relationship ............................................................................................................................... 44

Figure 21 Well efficiency (%) showing results for both pre-injection (dark) and post-injection

(light) datasets for HGA0001P (diamonds), HGA0002) (squares) and HGA0003P (triangles).

.................................................................................................................................................. 46

Figure 22 Flow resistance in terms of in-well mounding overlain with the standard curves

from Pyne (2005). .................................................................................................................... 47

E.6

Figure 23 Field measured turbidity for HGA0001P and HGA0002P (HGA0003P not

available) showing the target of <5 NTU outlined by Martin (2013) ...................................... 48

Figure 24 Bar chart showing the percentage of biofouling on the slotted PVC screens from

the image analysis for the pre-injection, post-injection and post-test pumping scenarios ....... 48

Figure 25 Percentage of biofouling with depth for HGA0001P, HGA0002P and HGA0003P

showing the slotted PVC and stainless steel wire wound screen sections. .............................. 49

Figure 26 Saturation Index (SI) versus time for carbonate minerals (calcite and dolomite) ... 50

Figure 27 Saturation Index (SI) versus time for sulphate minerals (bartite, gypsum and

anhydrite) ................................................................................................................................. 51

Figure 28 Types of MAR schemes (Dillon, 2005) .............................................................. E.33

Figure 29 Map of Managed Aquifer Recharge in Australia as of 2011 (Ward and Dillon,

2012) .................................................................................................................................... E.33

Figure 30 Global application of artificial recharge (IGRAC, 2012) .................................... E.34

Figure 31 A graphical diagnostic tool for determining different mechanisms of clogging

(Pyne, 2005) ......................................................................................................................... E.34

Figure 32 Apparatus for a membrane filter test used to determine MFI (Dillon et al., 2001)

.............................................................................................................................................. E.35

Figure 33 MAR Monitoring Network at MAC .................................................................... E.36

Figure 34 Units of the Hamersley Region (Kneeshaw 2008). ............................................. E-37

List of Tables

Table 1 MAR bore details (Refer to MAC is located in the Fortescue River basin within the

Weeli Wolli Creek system. The regional groundwater system flows to the east towards Weeli

Wolli Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to


E.7



Table 2 Geological reference table for unit codes ................................................................... 20

Table 3 Static Water level for the injection bores recorded on 9/2/12 .................................... 31

Table 4 Details of the step-drawdown tests conducted by Woodward-Clyde (1997),

Woodward-Clyde (1998) and AquaGeo (2013) ...................................................................... 32

Table 5 Static Water level for the injection bores recorded on 9/2/12 .................................... 34

Table 6 Dissolution reactions and ion activity products (IAP) for minerals included in the

analysis (adapted from Deutsch, 1997) .................................................................................... 36

Table 7 Jacobs equation coefficients determined using the Hantush-Bierschenk method ...... 45

Table 8 Assumptions of the Huntush-Bierschenk method (Kruseman and de Ridder, 1994) . 54

Table 10 Examples of MAR systems adapted from NWQMS (2009) ................................ E-38

Table 11 Summary Table of the MAR Monitoring Bore Network ..................................... E-39

E.8

Abstract

Managed Aquifer Recharge or MAR is a well-established method, useful in the development

of sustainable water management practices. The changing climate, growing population,

effects of urbanisation and surface water scarcity increase pressure on future global water

resources. MAR can help maximise abstraction from groundwater resources while mininsing

environmental impacts. While it has been primarily utilized for potable and agricultural water

supplies in Australia, it has the potential to form an integral part of mine site water

management. It is an effective and sustainable method for disposing of surplus water in an

operational mining capacity and can reduce the long term dewatering footprint of below

water table deposits. Clogging of injection well and the surrounding aquifer matrix is a

common operational issue that results in reduced permeability of injection surfaces. Clogging

can occur via physical, chemical or biological mechanisms or a combination thereof.

Remedial options such as bore redevelopment and pre-treatment of injection water are

available to system operators to manage the effects of clogging. A study is proposed to

determine the clogging potential and redevelopment frequency of a MAR scheme at BHP

Billiton‟s Mining Area C in the Pilbara.

Key words: Managed Aquifer Recharge, clogging, mining, Pilbara

E.9

Table of Contents

List of Figures .......................................................................................................................... vi

List of Tables ........................................................................................................................... ix

Abbreviations ........................................................................................................................... x

Abstract ................................................................................................................................... 11

1 Introduction .................................................................................................................... 12

1.1 Overview .................................................................................................................. 12

1.2 Aims ......................................................................................................................... 13

1.3 System Characterisation........................................................................................... 13

1.3.1 Site Description .................................................................................................... 13

1.3.2 MAR System Characterisation ............................................................................. 14

1.3.3 Climate Characterisation..................................................................................... 16

1.3.4 Hydrogeological Characterisation ...................................................................... 17

2 Literature Review .......................................................................................................... 21

2.1 Managed Aquifer Recharge ..................................................................................... 21

2.1.1 Definition ............................................................................................................. 21

2.1.2 Purpose ................................................................................................................ 21

2.1.3 Types .................................................................................................................... 22

2.1.4 Benefits ................................................................................................................. 22

2.2 Clogging ................................................................................................................... 22

2.2.1 Introduction.......................................................................................................... 23

2.2.2 Types and Causes ................................................................................................. 23

2.2.3 Management Options ........................................................................................... 25

2.2.4 Diagnostic Tools .................................................................................................. 25

2.3 Application of MAR in the Mining Industry ........................................................... 26

2.3.1 Mining Water Management ................................................................................. 26

2.3.2 MAR Schemes in the Pilbara Region ................................................................... 28

2.3.3 Mining Specific Operational Considerations ...................................................... 28

3 Materials and methods .................................................................................................. 29


3.1.1 Hydrographs ........................................................................................................ 29

E.10

3.1.2 Mounding ............................................................................................................. 29

3.2 Operational Performance ......................................................................................... 29

3.2.1 Aquifer vs In-Well Groundwater Level ................................................................ 29

3.2.2 Specific Injectivity ................................................................................................ 30

3.2.3 Well Efficiency ..................................................................................................... 32


3.3.1 Graphical Tool ..................................................................................................... 34

3.3.2 Water Quality Analysis ........................................................................................ 34



4 Results ............................................................................................................................. 37


4.1.1 Hydrographs ........................................................................................................ 37

4.1.2 Mounding ............................................................................................................. 38


4.2.1 Aquifer vs In-Well Groundwater Level ................................................................ 40

4.2.2 Specific Injectivity ................................................................................................ 41

4.2.3 Well Efficiency ..................................................................................................... 44


4.3.1 Graphical Tool ..................................................................................................... 46

4.3.2 Water Quality Analysis ........................................................................................ 46



5 Discussion........................................................................................................................ 51




6 Conclusions ..................................................................................................................... 56

7 References ....................................................................................................................... 58

Abstract .............................................................................................................................. E.8

1 Literature Review ....................................................................................................... E.13

1.1 Introduction .......................................................................................................... E.13

E.11

1.2 Managed Aquifer Recharge ................................................................................. E.13

1.2.1 Definition ......................................................................................................... E.13

1.2.2 Purpose ............................................................................................................ E.14

1.2.3 Types ................................................................................................................ E.14

1.2.4 Benefits ............................................................................................................. E.15

1.3 Clogging ............................................................................................................... E.16

1.3.1 Introduction...................................................................................................... E.16

1.3.2 Types and Causes ............................................................................................. E.16

1.3.3 Management options ........................................................................................ E.18

1.3.4 Diagnostic tools ............................................................................................... E.18

1.4 Application of MAR in the Mining Industry ....................................................... E.20

1.4.1 Water Management in Mining ......................................................................... E.20

1.4.2 MAR Schemes in the Pilbara Region ............................................................... E.21

1.4.3 Mining Specific Operational Considerations .................................................. E.21

1.5 Conclusion ........................................................................................................... E.22

2 Project Proposal .......................................................................................................... E.23

2.1 Title ...................................................................................................................... E.23

2.2 Investigator .......................................................................................................... E.23

2.3 Introductory Statement......................................................................................... E.23

2.4 Objective .............................................................................................................. E.23

2.5 Background .......................................................................................................... E.23

2.6 Significance.......................................................................................................... E.24

2.7 Methodology ........................................................................................................ E.24

2.8 Budget .................................................................................................................. E.26

2.9 Timetable ............................................................................................................. E.26

3 References .................................................................................................................... E.27

Figures .................................................................................................................................. E.33

Tables .................................................................................................................................. E-38

Appendix 1 ........................................................................................................................... E.41

Appendix 2 ........................................................................................................................... E.44

Appendix 3 ........................................................................................................................... E.45

E.12

E.13

1 Literature Review

1.1 Introduction

MAR is the process of enhancing natural rates of recharge to groundwater systems and is

becoming an increasingly important water management tool globally. Dillon (2005) states

that MAR has the potential to be a major contributor to the UN Millennium Goal for water

supply for villages in developing nations. MAR schemes can take on a number of different

forms, with the Aquifer Storage and Recovery (ASR) and infiltration basin type being the

most common. MAR schemes have been successfully implemented in Australia and around

the world for the purposes of stormwater harvesting and wastewater recycling to secure

additional water supplies and maintain environmental outcomes. However, there is huge

potential for MAR to be applied to the mining industry to aid mine site water management.

Clogging at the injection surface poses one of the most challenging and persistent technical

issues for the operation of an MAR scheme (Dillon et al. 2001). Clogging can occur via

physical, chemical and biological processes and several diagnostic methods exist to predict

the clogging potential. An understanding of the clogging formation process and potential in

each MAR system enables efficient operation and maximises the longevity of MAR

infrastructure. This literature review investigates the practicalities of MAR in the mining

industry and the potential for MAR to be applied in the Pilbara mining region of Western

Australia.

1.2 Managed Aquifer Recharge

1.2.1 Definition

Managed aquifer recharge is the “intentional recharge of water to aquifers for subsequent

recovery or environmental benefit” (NWQMS 2009 pg 13). The process has also been

referred to as enhanced recharge, water banking and sustainable underground storage in the

literature (Dillon 2005). The definition specifies „intentional‟ recharge to separate MAR from

incidental or unintended recharge processes such as the effects of land clearing, over

irrigation and increased runoff from urbanisation. Recently, the term “artificial recharge” has

been effectively replaced with MAR to avoid the negative connotations associated with the

perceived unnatural and non-sustainable process.

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1.2.2 Purpose

MAR systems may be implemented for a number of purposes:

to harvest urban stormwater to supplement water resources (Page et al. 2011;

Vanderzalm et al. 2010; Dillon et al. 1999);

to reclaim wastewater to supplement water resources (Page et al. 2010; Bosher et al.

1998; Asano and Levine 1998);

to sustain environmental flows and phreatophytic vegetation (Naumburg et al. 2005);

and to act as a barrier to prevent saline intrusion (Daher et al. 2011; Shammas 2008).

The use of MAR to capture and store stormwater to reduce demand on conventional water

resources has become increasingly popular in Australia over the last decade. Figure 29 from

Ward and Dillon (2012) shows a map of major MAR schemes by type around Australia The

largest Australian MAR project is located in Queensland‟s Burdekin Delta, where recharge of

100GL/year via infiltrations ponds maintains sugar cane production. MAR initiatives have

been implemented with success worldwide, with India, USA, Sweden, Finland, New Zealand,

France and Germany leading the way with application of groundwater recharge management

schemes as shown in Figure 30 (IGRAC 2012).

1.2.3 Types

MAR encompasses a wide variety of water management systems, which vary with recharge

method, source of recharge water, end use of recovered water, scale and complexity. Aquifers

may be recharged by two methods: (a) the injection of source water directly into the target

aquifer through screened wells, or (b) the infiltration of source water through open basins,

galleries or channels. Recharge water may be sourced from drinking water treatment plants,

sewage treatment plants, harvested storm water, irrigation districts, ephemeral streams or

industrial specific sources (Bouwer 2002).

Types of MAR systems are listed below from Tuinhof and Heederik (2003), NWQMS (2009)

and Dillon (2005):

Aquifer Storage and Recovery (ASR): Injection into a well for storage and recovery

from the same well in either a confined or unconfined aquifer;

Aquifer Storage, Transport and Recovery (ASTR): Injection into a well for storage

and recovery from a different well;

E.15

Vadose Zone wells: Injection into a dry well to allow infiltration to a deep underlying

unconfined aquifer;

Percolation tanks and recharge weirs: Construction of a dam or weir in an

ephemeral stream channel to allow infiltration to underlying unconfined aquifers and

subsequent recovery downstream;

Rainwater harvesting: Diversion of roof runoff into a well or sump filled with sand

or gravel;

Bank filtration: Extraction from a well near or under a surface water body to induce

infiltration;

Infiltration galleries: Construction of geotechnically- stabilised buried trenches to

allow infiltration to an underlying unconfined aquifer;

Dune filtration: Construction of a pond in a dune to allow infiltration for extraction

at lower elevations;

Infiltration ponds: Construction of a pond or channel off-stream to allow infiltration

to an underlying unconfined aquifer;

Soil aquifer treatment: Diversion of treated sewage effluent to infiltration ponds for

water quality treatment purposes;

Underground dams: Construction of a trench across an ephemeral stream bed,

backfilled with low permeability material for flood management purposes;

Sand dams: Construction of a sand dam on an ephemeral stream to create an artificial

aquifer following periods of inundation.

Recharge releases: Construction of a dam on an ephemeral stream, followed by the

slow release of water to promote downstream infiltration.

An example of each type of MAR systems is given in Table 9. For a full description of each

scheme refer to page 15 of NWQMS (2009). Figure 28 (Dillon 2005) shows a schematic of

several types of MAR schemes listed above. The ASR type of MAR scheme shall be the

focus of this literature review, as it is the model for the case study at Mining Area C.

1.2.4 Benefits

ASR has many benefits over surface water storages structures such as dams and reservoirs

making it an efficient option for long-term water storage. These include (NWQMS 2009;

Pyne 2006; Bouwer 2002; Kimrey 1989):

E.16

Low capital installation costs;

Low evaporation loss from the aquifer;

Multi-purpose capacity for water quality treatment in addition to storage;

Reduced project area footprint;

Low potential for structural failure (i.e. dam wall failure);

Reduced potential for mosquito habitat;

Flexible system size to meet incremental growth in water demand;

Reduced potential for pollution or damage by sabotage or other hostile action;

Improved reliability of existing supplies

While the benefits of ASR have been widely publicised, the ongoing energy requirements and

operational cost of maintaining an ASR scheme is seldom touched on in the literature.

1.3 Clogging

1.3.1 Introduction

A common operational issue affecting ASR schemes is clogging of the recharge surface,

gravel pack or surrounding aquifer matrix that, in serious cases, can lead to the abandonment

of projects. Also known as aquifer plugging, the Australian MAR Guidelines define clogging

as “the reduction in permeability of a porous medium” (NWQMS 2009, pg114). Clogging

leads to a reduction in flow rates, which limits the volume of water stored in the aquifer, or an

increase in head to maintain a constant recharge rate. It is important to understand the types

and causes of various forms of clogging and the associated management options, to define

source water treatments needs and maximize the operational life of the injection system.

1.3.2 Types and Causes

Clogging occurs due to reactions between the source water, target water and the aquifer

matrix as result of physical, chemical or biological mechanisms. Pyne (2005) identified that

following processes could be responsible for clogging:

Air entrapment;

Deposition of total suspended solids (TSS);

Biological growth;

Geochemical reactions;

Particle rearrangement in the aquifer materials.

E.17

Each process is discussed in detail below:

Air entrapment

Air entrapment or gas binding is caused by the cascading of water inside the injection well

casing or air entering the recharge pipe network under negative pressure, producing air

bubbles that may block pore spaces in the aquifer matrix and screened casing. It is similar to

bubble lock which can occur during bore/well development. The entrained air increases the

oxidation-reduction potential (ORP), which promotes microbial activity and geochemical

reactions, leading to further clogging. Air entrapment can also occur due to the release of

dissolved gases through temperature or pressure changes or as a metabolic byproduct of

microbial activity (release of nitrogen or methane). Pyne (2005) suggests that clogging by air

entrapment is characterised by a rapid increase in flow resistance as shown in Figure 31.

Deposition of total suspended solids (TSS)

The accumulation of organic and inorganic suspended solids, such as clay and silt particles,

algae cells or their tests (diatoms), microorganism cells, can form a low permeability

clogging layer on injection surfaces (Bouwer 2002). Dillon et al. (2001) asserts that the

deposition of suspended sediments is the most frequently reported form of clogging.

Biological growth

Microbial clogging occurs through the growth of microorganisms and the production of

biofilms (extracellular polysaccharides). Pyne (2005) states that clogging due to biological

growth is not well understood. Recharge waters rich in organic carbon, nitrogen and

phosphorus, promote biological clogging and it is a commonly reported issue in recharge

basins (NWQMS 2009). Schuh (1990) identified that biological clogging in surface

infiltration systems can vary seasonally, in response to changes in water temperature and

viscosity.

Geochemical reactions

Chemical clogging is the result mineral precipitation affecting aquifer permeability.

Common geochemical reactions are the precipitation of calcium carbonate (calcite), gypsum,

phosphate, iron and manganese oxide hydrates (Bouwer 2002; Pyne 2005). Bacteria catalyse

many geochemical reactions therefore it can be difficult to separate chemical clogging from

biological clogging. These reactions occur due to the changes in redox conditions inherent in

injection of oxygenated water into typically reduced aquifers.

E.18

Particle rearrangement

Particle rearrangement in the aquifer matrix, caused by repeated cycles of recharge and

recovery, can affect aquifer permeability (Olsthoorn 1982). Australian MAR Guidelines

(NWQMS 2009) fail to identify the particle rearrangement as a clogging mechanism however

Pyne (2005) states that particle rearrangement is not an important mechanism in aquifer

clogging but still must be considered.

1.3.3 Management options

A well-designed and constructed system is critical to the effective operation of an MAR

scheme. Brown et al. (2000) suggested that air entrapment clogging issues could be

eliminated through the design phase in an ASR case study at Hamersley Iron‟s Nammuldi

iron ore mine in the Pilbara. Youngs et al. (2010) suggested that the rapidly constructed pipe

network, paired with poor scouring practices, promoted physical clogging of injection wells

at FMG‟s Cloudbreak operation.

Evidence of several options to manage injection well clogging exist in the literature:

Injection well redevelopment: The periodic redevelopment or backflushing of

injection wells by airlifting or pumping is the preferred method to manage clogging

according to Pyne (2005). The frequency of redevelopment depends on the rate of

clogging and can vary from daily to annually.

Pre-treatment of injection water: This is common for reinjection schemes where the

end use is for potable purposes or the quality of the source water is significantly lower

than the target aquifer. Bouwer (2002) indicates that in addition to reducing the

effects of clogging, pre-treatment of water enables the protection of the receiving

groundwater quality.

Alterations to MAR infrastructure: Youngs et al. (2010) remediated the physical

clogging of FMG‟s Cloudbreak operation by removing the slotted PVC casing, in

addition to well redevelopment.

Chemical treatment: Chlorine and chemical treatments such as mineral acids,

organic acids, biodispersants, surfactants and enzymes are utilised as a rehabilitation

procedure (Pyne 2005). It is effective against biological clogging but is limited in

mitigating physical clogging.

1.3.4 Diagnostic tools

E.19

Various techniques have been reported on in the literature to predict clogging potential. These

include:

Membrane Filtration Index (MFI);

Water quality parameters;

Laboratory column experiments;

Numerical modeling;


Membrane Filtration Index (MFI)

The MFI method provides a relatively easy field assessment of physical clogging potential

(Dillon et al. 2001). Membrane filtration tests are used to develop an MFI. The test involves

passing recharge water through a membrane of fixed aperture at a constant pressure whilst

measuring the decline in flow rate. An example of the apparatus used in the test is shown in

Figure 32. The MFI is then determined graphically from the slope of the linear portion of the

time/volume (t/V) vs. volume (V) plot. Dillon et al. (2001) states that the greater the slope,

the higher the MFI and the greater potential for physical clogging. As the standardised

membrane is unlikely to be representative of the pore spaces in the aquifer, the test provides a

guide only and cannot be relied upon as an absolute measure.

Water Quality Parameters

Water quality parameters can be useful indicators of clogging potential. Measurement of

turbidity and TSS indicate physical clogging while total organic carbon (TOC), dissolved

organic carbon (DOC) and assimilable organic carbon (AOC) indicate biological clogging

(NWQMS 2009).

Laboratory Column Experiments

Laboratory column studies, also known as the parallel filter index (PFI), are determined by

passing recharge water through columns filled with aquifer material (Bouwer 2002; Wood et

al. 2005; Rinck-Pfeiffer et al. 2000). Due to the small-scale nature of the PFI test, this method

is not usually representative of the field scale processes.

Modeling

Youngs et al. (2010) applied the PHREEQC geochemical model to determine the potential of

chemical clogging. The model relies on water chemistry and hydrogeological data to predict

the potential for mineral precipitation.

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Simple graphical techniques to predict clogging have been proposed by Pyne (2005), where

the relationship between resistance to flow and time is compared with standard curves for

each clogging type (Figure 31).

1.4 Application of MAR in the Mining Industry

1.4.1 Water Management in Mining

There is huge potential for MAR to be applied to the mining industry to aid mine site water

management, although only few studies investigate this prospect. The Australian MAR

Guidelines (NWQMS 2009) and investigations into legislation and policy governing MAR in

Australia (Ward and Dillon 2012) make no reference to the potential for MAR in a mining

context.

Dewatering of open pits and underground mining areas to enable safe conditions for below

water table (BWT) mining can result in large volumes of water abstracted from the orebody

aquifer. Current water management practices endeavor to use this supply for operational

requirements, which include dust suppression and ore processing. However, often the

dewatering abstraction volumes exceed mine site water demand. In such cases of water

surplus, excess water is discharged to the surface water environment and is effectively

wasted. Over the life of mine, water balances can fluctuate between water surplus and water

deficit depending on mine planning, pit sequencing of BWT deposits and climatic conditions.

MAR has the potential to buffer these fluctuations by banking water during periods of water

surplus to meet future water demand in a deficit scenario.

In addition to managing fluctuations in the minesite water balance, MAR has several benefits

over water management strategies currently applied in the industry. Firstly, the disposal of

excess dewatering volumes into ephemeral surface water system leads to negative ecological

and cultural implications. Youngs et al. (2010) state that the constant discharge provides a

water source for ecosystems, which then become dependent on minesite operations and

acknowledged that surface discharge is discouraged by traditional landowners. Secondly, the

reinjection of the dewatering surplus reduces the net groundwater drawdown of the minesite

operations. MAR can also be used to mitigate impacts to groundwater dependant ecosystems

(GDE) proximal to mine dewatering by reducing drawdown at GDEs.

E.21

Compared to the conventional MAR projects intended to secure potable supply, MAR

schemes in an operational mining environment are designed „fit for purpose‟. This means that

mining-related MAR schemes are typically designed and constructed rapidly on a larger

scale, operated over a shorter life-of-project duration with less emphasis on control of water

quality (given the primary objective of water disposal).

1.4.2 MAR Schemes in the Pilbara Region

The potential for ASR as a minesite water management tool in the Pilbara mining region of

Western Australia has had limited attention in the scientific literature. Only few authors have

investigated this prospect (Windsor et al. 2011; Youngs et al. 2010; Brown et al. 2000;

Kneeshaw and Clark 1983). By examining two ASR case studies, Brown et al. (2009)

contended that in addition to meeting future water demand, MAR could reduce impact to the

surrounding surface water environment.

Reductions in impacts to GDEs are another potential benefit. The successful implementation

of a relatively large and complex MAR scheme at Fortescue Metals Group (FMG)‟s

Cloudbreak operation proves that MAR is a viable option for water managers in the mining

industry and is an „ideal‟ tool for the Pilbara region (Youngs et al. 2010). The scheme

consists of a saline and fresh water injection system and was implemented to bank fresh

water for future use and to maintain ecological water requirements of the nearby Fortescue

Marsh (Youngs et al. 2010).

1.4.3 Mining Specific Operational Considerations

Brown et al. (2000) identified three issues specific to the application of MAR in a mining

context:

4. The potential for injected water to re-circulate back into the dewatered orebody

aquifer, with implications for the efficiency of dewatering operations. The optimal

distance between dewatering operations and injection wells is dictated by the degree

of hydraulic connectivity between the orebody aquifer and the target injection aquifer.

Numerical modeling can be applied to predict the volumes of reticulated water


5. The practical requirement of periodic well redevelopment due to a reduction in

hydraulic performance from the effects of injection well and aquifer clogging

E.22

(discussed further in Section 2.2 Clogging) and the implications for the active mining

operations.

6. The impact of injecting poor quality water from the active mining area into a high

quality natural groundwater environment.

It should be noted that (3.) is not an issues solely related to MAR in a mining environment.

Studies concerning the injection of treated wastewater to secure drinking water supply

(Bosher et al. 1998; Asano and Levine 1998; Pavelic and Dillon 1997) have also highlighted

the issue of managing the interaction of low quality source water with a higher quality target

aquifer. An issue not identified by Brown et al. (2002) is the effect of reinjection on nearby

mining operations downstream of the MAR site and the implications on their water balance

and dewatering requirements.

1.5 Conclusion

Recent case studies assessed as part of this literature review demonstrate that MAR is viable

option in mine site water management. However, the feasibility of any project is largely site

specific and relies on an understanding the local and regional hydrogeology. A sound

understanding of the physical, chemical and microbial processes occurring within the aquifer

during natural and under MAR is important to manage the ASR system with confidence

(Dillon et al. 1999). Looking forward, the abundance of future below water table (BWT)

deposits across the Pilbara region is likely to increase the potential of MAR to sustainably

manage mine site water resources. Currently, there no guidelines exist to advise on the

optimal MAR system characterisation and design in Pilbara-specific mining environment.

E.23

2 Project Proposal

2.1 Title

“Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C”.

2.2 Investigator

Investigator:

Lily Smith (21224043)

Project Supervisors:

Coordinating Supervisor Associate Professor Ryan Vogwill, School of Earth and

Environment, UWA

Co-supervisor Jed Youngs, BHP Billiton Iron Ore

2.3 Introductory Statement

The proposed study will discuss the operation of Managed Aquifer Recharge in a mining

setting while investigating the clogging potential and its mitigation in injection wells at

Mining Area C (MAC).

2.4 Objective

The objectives of the proposed study are:

To describe the MAR system characterisation and design at MAC;

To assess the operational performance of the MAR system;

To determine the potential for various types of clogging in the injection wells at

MAC;

To develop a Pilbara-specific tool for identifying, assessing and managing clogging

(time permitting).

2.5 Background

Managed Aquifer Recharge (MAR) is the process of artificially recharging the groundwater

for storage and subsequent recovery or environmental benefit. Clogging of the injection well

and aquifer interface is a commonly reported operation issue for such schemes and can occur

E.24

via physical, biological, chemical or mechanical means (Pavelic and Dillon 1997; Baveye et

al. 1998).

MAC is a BHP Billiton owned and operated open pit iron ore mine, situated 100km northeast

of Newman in the Pilbara region of WA. The site is currently experiencing a surplus water

balance and has identified MAR as a sustainable and effective way of disposing excess

abstraction volumes from in-pit dewatering. The testing and commissioning phase has been

underway since March 2012 and a 2-year trial is scheduled to commence in November 2012.

The ASR system at MAC disposes of excess dewatering supply via a temporary storage dam

which is piped to three injection bores as shown in Figure 33. The injection bores are located

adjacent to the A Deposit orebody, downstream of current mining activities, and will be

utilised as abstraction bores for dewatering purposes for future mining at this deposit. The

injection bores are screened in the regional Wittenoon dolomite fractured rock aquifer.

Preliminary results from the testing commissioning phase indicate that clogging is a potential

issue at one of the three injection wells.

2.6 Significance

In Australia and worldwide, MAR has been successfully applied to a number of projects

involving wastewater recycling and stormwater harvesting. However, there is huge potential

for MAR to be applied to the mining industry to aid mine site water management. The MAR

trial at MAC is the first of such schemes to be implemented by BHP Billiton Iron Ore. As

below water table (BWT) mining increasingly becomes a necessary element of future mine

sites in the iron ore project pipeline, practical knowledge of effective design, operation and

management of the MAR system is key for successful project implementation.

2.7 Methodology

The project will be investigated according to the steps outlined below:

1. Describe the MAR system characterisation:

Site Description;

Aquifer Characterisation;

o Describe local and regional geology;

E.25

o Create a conceptual cross section diagram of the study area showing the major

aquifer units, direction of groundwater flow, monitoring/injection bores with

screens depths;

o Determine aquifer parameters via test pumping (February 2013);

o Tracer test, to determine the degree of connection between aquifer units.

Groundwater Characterisation;

o Assess the baseline groundwater quality through analysis of hydrochemistry

sampling.

System Design Characterisation;

o Describe the MAR system design, from source water to injection;

o Describe the construction of injection wells.

2. Determine operational performance:

Aquifer response to MAR:

o Analyse mounding (m) vs. time (days), (Appendix 2).

o Analyse mounding (m) vs. discharge Rate (kL/day)

o Test these responses with numerical modeling studies using MODFLOW.

Note that the model will be developed by an external consultant and only

utilised for scenario analysis as part of this project.

3. Identify and quantify ASR well clogging potential and type:

Clogging diagnostic tools:

o Analyse water quality parameters during the injection trial, with an emphasis

on TSS;

o Compare mounding (m) vs. time (days) to standard curves, to determine type

of clogging mechanism occurring;

o Investigate and apply a PHREEQC geochemical model, to simulate chemical

reactions and transport processes occurring in the injection well and surround

aquifer.

Downhole camera study, followed by scrape samples and analysis on casing.

If time permitting:

4. Develop a diagnostic tool for identifying, assessing and managing clogging:

Test robustness of diagnostic tool on data from BHP Billiton‟s Olympic Dam MAR

scheme (if available).

E.26

2.8 Budget

Refer to Appendix 3.

2.9 Timetable

Refer to Appendix 3.

E.27

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E.33

Figures

Figure 28 Types of MAR schemes (Dillon, 2005)

Figure 29 Map of Managed Aquifer Recharge in Australia as of 2011 (Ward and Dillon, 2012)

E.34

Figure 30 Global application of artificial recharge (IGRAC, 2012)

Figure 31 A graphical diagnostic tool for determining different mechanisms of clogging (Pyne, 2005)

E.35

Figure 32 Apparatus for a membrane filter test used to determine MFI (Dillon et al., 2001)

E.36

Figure 33 MAR Monitoring Network at MAC

E-37

Figure 34 Units of the Hamersley Region (Kneeshaw 2008).

E-38

Tables Table 9 Examples of MAR systems adapted from NWQMS (2009)

MAR System

Type

Description Locality of example

Aquifer Storage and Recovery (ASR)

Injection into a well for storage and recovery from the same well to a confined or unconfined aquifer.

Grange, and Tea Tree Gulley,Adelaide, South Australia

Aquifer Storage, Recovery and Transport (ASTR)

Injection into a well for storage and recovery from a different well for water quality treatment purposes.

Salisbury, South Australia

Vadose Zone Wells

Injection into a dry well to allow infiltration to a deep unconfined aquifer.

Phoenix, United States

Percolation tanks and recharge weirs

Construction of a dam or weir in an ephemeral stream channel to allow infiltration to unconfined aquifers and subsequent recover downstream.

Callide Valley, Queensland

Rainwater harvesting

Diversion of roof runoff into a well or sump filled with sand or gravel.

Perth, Western Australia

Bank filtration Extraction from a well near or under a surface water body to induce infiltration.

Berlin, Germany

Infiltration galleries

Infiltration through geotechnically-stabilised buried trenches to an unconfined aquifer.

Floreat Park, Western Australia

Dune filtration Construction of a pond in a dune to allow infiltration for extraction at lower elevations.

Amsterdam, The Netherlands

Infiltration ponds Construction of a pond or channel off-stream to allow infiltration to an underlying unconfined aquifer;

Burdekin Delta, Queensland

Soil aquifer treatment

Diversion of treated sewage effluent to infiltration ponds.

Alice Springs, Northern Territory

Underground dams

Construction of a trench across an ephemeral streambed, backfilled with low permeability material for flood management purposes.

Northeast Brazil

Sand dams Construction of a sand dam on an ephemeral stream to create an artificial aquifer following periods of inundation.

Kitui, Kenya

Recharge releases Construction of a dam on an ephemeral stream, followed by the slow release of water to promote downstream infiltration.

Little Para River, South Australia

E-39

Table 10 Summary Table of the MAR Monitoring Bore Network H

ole

Nam

e

Eas

ting

(MG

A94

_50)

Nor

thin

g

(MG

A94

_50)

DE

C R

eq’t

Sc

reen

ed

Aqu

ifer

Uni

t

Geo

logi

cal

Des

crip

tion

SWL

@ ti

me

of

drill

ing

(mbg

l) Sc

reen

D

epth

(mbg

l)

Log

ger

Typ

e

Inte

rval

(hrs

)

HGA0001P* 709317.122 7463243.316 Y Paraburdoo DOLOMITE 45.27 82-115 VWP 4

HGA0002P* 709561.560 7463202.707 Y Paraburdoo DOLOMITE 42.63 77.5-125.5 VWP 4

HGA0003P* 709769.705 7463142.631 Y

Paraburdoo DOLOMITE: Blue-green-grey karstic dolomite, yellow and pale grey

shales @ 78 – 80m

42.66 48-106 VWP 4

HGA0035M 709306.264 7463243.275 N TD? N/A N/A 6-65 700 1

HGA0036M 709576.278 7463209.241 N

TD? N/A N/A 6-65 700 1

Paraburdoo? DOLOMITE?

HGA0006M 709778.852 7463152.497 N

Paraburdoo DOLOMITE: Blue-green-grey karstic dolomite, yellow and pale grey

shales @ 78 – 80m

42.49 72-91 300 1

HGA0015M 708935.000 7463355.500 Y

Paraburdoo DOLOMITE: Grey, green grey, yellowgrey, minor fracturing

throughout, somelimonite staining

52.0 106.8-142.8 300 3

HGA0010M 709472.830 7463024.000

Y

Paraburdoo DOLOMITE: Grey, light grey N/A 136.3-176.3 300 3

West Angela GOETHITE: Dark brown (yellow streak) with abundant dark grey,

blackmanganese in bands and invading gothite

CLAY: Brown with minor BIF

BIF: Dark grey, dark red becoming grey, brown, yellow, little

mineralisation 162-165m partly mineralised

HGA0009M 709481.490 7462932.010 Y TD TD2 50.00 33.4-63.4 300 3

E-40

HGA0008M 709482.650 7462976.460 Y TD TD2 49.00 31.7-61.7 300 3

HGA0013M 709228.310 7463148.910

Y

West Angelas SHALE AND BIF: Multicoloured (yellow,brown, white, pink, red,

grey), soft and hard clays, grits and small rock fragments - mainly

bif, shale and chert

N/A 117-132 300 3

Mount

Newman

BIF AND CHERT: Yellow-brown, caramel and red, siliceous BIF

and CHERT

HGA0012M 709229.170 7463009.470 Y TD TD1 55.00 52-82 300 3

HGA0011M 709240.790 7463073.020 Y

TD TD1/2 N/A 66-96 300 3

West Angelas

HGA0007M 709729.310 7462883.980

Y

Paraburdoo DOLOMITE: Grey, mainly fresh, minor fractures with limonite,

minor brown, yellow brown bands

N/A 114-150 300 3

West Angelas BIF: Dark grey, dark red becoming dark grey, yellow brown, hard,

siliceous

GWB0023M 709306.920 7463357.140 N

TD TD2 N/A N/A 700 3

Paraburdoo DOLOMITE

GWB0025M 709547.510 7463441.080 N Paraburdoo DOLOMITE N/A N/A 700 3

HGA0024M1 708396.204 7463325.575 N

Marra Mamba GOETHITE and minor limonite: Dark grey-yellow, numerous

fractures, hard texture, moderately weathered, minor chert

60.08 108-120 300 3

HGA0017M 708411.000 7463380.000 N

Mount

Newman

GOETHITE: ore, dark grey, fine grained, dull lustre N/A 123.9 –

147.9

300 3

HGA0025M1 708394.712 7463511.336

N

Paraburdoo DOLOMITE and minor shale: Dark red-yellow-grey, dolomite (40-

60%), shale (5-40%), weathered BIF (10-45%), hard texture,

fractures

57.50 108-120 300 3

HGA0022M 707314.000 7463574.660 N

Mount

Newman

BIF: Multi coloured chips in green, yellow green matrix, banded,

grey, green grey, yellow brown

64.65 104.7-116.7 300 3

E.41

Appendix 1

Referencing Style

E.42

E.43

E.44

Appendix 2

MAC MAR Trial - Monitoring Bore Network

Figure 33 shows the spatial distribution the monitoring bore network used to assess the aquifer

response to the MAR trial. The details of each bore are summarised in Table 10.

The ‘Requirement’ column refers to bores required to be monitored under the Environmental

Protection Act 1986, Licence L7851/2002/4. Over the 2 yr trial period, the conditions of the

licence state that water levels in the injection bores (HGA0001P, HGA0002P and HGA0003P)

must be recorded daily for the first two weeks, then weekly for the first six months, then monthly

subject to review and monitoring bores recorded weekly. The logger monitoring network

frequency described below exceeds these legal requirements outlined by the DEC.

‘Geological Descriptions’ entries are missing due to bores not being logged or a function of poor

record keeping.

„Logger Type’:

700: In-Situ Level Troll 700 Vented logger, records pressure (kPa), temperature (°C) and depth (m);

300:In-Situ Level Troll 300Non-vented logger, records pressure (kPa), temperature (°C) and depth (m);

VWP: Vibrating Wire Piezometer, records frequency (Hz) and temperature (°C).

The ‘Screened Aquifer Units’ refer to the geological units of the Hamersley Region, as described

by Kneeshaw (2008) in Figure 34.

E.45

Appendix 3

Project proposal Budget and Timetable

Budget

A nominal budget of $7, 200 is proposed.

ITEM COST

Laboratory analysis of water quality samples $3,000

Tracer study $2,000

Camera study $2,000

Printing and binding $100

Miscellaneous $100

TOTAL $7, 200

BHP Billiton Iron Ore can contribute to the cost where work is relevant to mine site

operations.

E.46

Timetable

2012 2013 2014 Task J J A S O N D J F M A M J J A S O N D J F M A M Preliminary research, define research proposal

Proposal seminar Research and draft literature review

Research proposal draft due Review research proposal with supervisors

Research proposal final due

Field work

Analyse monitoring data

Draft thesis

Thesis draft due Review thesis draft with supervisors

Thesis final due

Milestones

Proposal Seminar 11/09/2012

Research Proposal Draft Due 09/10/12

Research Proposal Final Due 06/11/12

Thesis Draft Due 04/04/14

Thesis Final Due 30/05/14

Documents

Clogging mechanisms in Managed Aquifer Recharge: a case ... · water scarcity increase pressure on future global water resources. While MAR has been While MAR has been primarily utilised