CHARACTERIZATION OF RESERVOIR SANDS FROM SEISMIC AND …wojast.org/wp/wp-content/uploads/Vol9-2/130_140.pdf · Ilozobhie and Egu: Characterization of Reservoir Sands from Seismic

Ilozobhie and Egu: Characterization of Reservoir Sands

from Seismic and Well Log Data in the Niger Delta

World Journal of Applied Science and Technology, Vol. 9 No. 2 (2017), 130 – 140 130

ISSN: 2141 – 3290

www.wojast.com

CHARACTERIZATION OF RESERVOIR SANDS

FROM SEISMIC AND WELL LOG DATA

IN THE NIGER DELTA

ILOZOBHIE1 A.J. AND EGU2 D. I. 1Physics Department, University of Calabar, Nigeria

2 Petroleum Department, Madonna University Nigeria

[email protected]

ABSTRACT This paper presents a study of identifying opportunities in an oil Field and

describes a pragmatic approach to integrated modeling and uncertainty

management. Production started from two oil bearing sands, in the depth range

5,000 to 8,400 ft, which have strong aquifer support. Reservoir A with an Oil water

Contact (OWC) at 5520ft thickness ranging from 5450ft to 5750ft with STOIIP of 59MMSTB and an underlying reservoir B with OWC at 6100ft with thickness

ranging from 6000ft to 6300ft with STOIIP of 21MMSTB. Average permeabilities

and porosities ranges from 2.54md – 1115.2md and 0.2 to 0.33. Integrated static

and dynamic reservoir modeling indicated further development opportunities in

two of the vertically stacked reservoirs. The key uncertainty were the fault

structures that deformed parts of the reservoirs followed by contacts, porosity,

water saturation, Net To Gross (NTG) ratio and initial oil formation volume factor

with standard deviations of 7.295, 4.51, 0.20, 0.11, 0.1 and 0. However, second

case scenario results of dynamics gave cumulative oil production of 46.62MMSTB

at 55% RF in reservoir A and 17.21MMSTB at 48% RF in reservoir B. This

corresponds to maximum cumulative oil production of 40MMSTB by August,

2020. Modeling indicated that the overall recovery from the field could potentially

be increased by 6% from drilling infill wells with drainage targets. Risk mitigation

measures include a focused data acquisition plan to calibrate the current fluid

contacts and drilling wells targeting two sub-surface locations with dual or

sequential phased development. Production data analysis indicates that the

recovery factors and well recoverable volumes are highly correlated to average net

oil pay. The correlations may be used for reserve quality control. It is

recommended that improved quality of data and results of static and dynamic

modeling should be a priority to reduce uncertainties and solve problems in-situ in

field operations.

INTRODUCTION

The sources of most of the information concerning reservoirs are from wells drilled at different

locations on the identified hydrocarbon reservoirs (James, et al., 1973). Such information

include; permeability, porosity, fluid saturation, and lithology, all of which are very vital for

reservoir characterization, management, and description. (Schlumberger, 1991; Tearpock and

Bischke, 1990 and 1991). It is on record that many oil fields within the Niger Delta which were initially abandoned have

been reactivated and are producing because of reservoir analysis of such fields (Oyedele et al,

2013). Hence reservoir characterization using seismic and well logs (statics) integrated with

dynamic (data) serves as a valuable tool that helps in preventing errors in reservoir decision

making and enable the operator to avoid deleterious effects of heterogeneity as well as enhancing

the production of oil and gas to the best economic advantage.




LOCATION OF STUDY AREA / DATA The study area is located between latitudes 30.451 and 60.051N and longitudes 50.101 and 70.101

E at the onshore depobelt of Eastern Niger Delta, Nigeria (Figure 1). The data used includes: 3D Seismic Sections with 7 inlines and 5 cross lines, Composite well logs from five wells, Check

shot Data, Reservoir Data, Production and Pressure Volume Temperature (PVT) data. The

software used includes Petrel 2005 version, Integrated Petroleum Management (IPM) software

suites and Eclipse simulation software.

METHODS

Static Methodology The Soft copies of the seismic sections in SEG-Y format and complete well log suites in LAS

format were loaded and imported.

Figure 1: Map of the study area in the Niger Delta (Adapted after Nton and Esan, 2010 Two horizons were picked using strong, coherent, continous and bright reflections as criteria.

These horizons were deformed by faults in the west and eastern parts of the section. A total of 11 fault zones were picked with most of the faults positioned below horizon B. (Figure 2).

Figure 2: Interpreted horizons and faults (inline)




On the crossline sections the interpreted horizons and faults sections show the presence of a fault

from the West and inwards towards the bottom and eastward. The other three faults occurred

below the horizon B. Two of the faults slightly deformed the horizons particularly horizon B (blue coloured fault), Figure 3. The following structural maps (time and depth maps) were

generated to ascertain the structural features and sediments distribution within the study area

Figure 3: Interpreted horizons and faults (crossline)

Dynamic Simulation Method

Schlumberger Eclipse Simulation A detailed simulation was done using the Schlumberger simulation 2005A software which

consist of three major component namely the reservoir characterization, simulation and work flow. The reservoir characterization panel consist of the Flogrid, PVTi, SCAL, Schedule and

VFPi. The simulation panel comprises of Eclipse, Frontsim and reservoir to surface links while

the workflow consist of Floviz, simopt, planopt, near wellbore modelling and well test 200. Surface Time Maps Surface time maps were generated for horizon A and B picked. Figure 4 shows the surface time

map for horizon A with the Two Way Travel time (TWT) ranging from -1410ms to -1660ms.

Figure 4: Time surface map for reservoir A The northern section depicts regions of low time values while the southwest and south-south

depicts high TWT ranging from -1610ms to -1660ms. Figure 5 shows the surface time map for

horizon B with the two way travel time ranging from -1520ms to -1820ms. The northern and

south-eastern section depicts regions of low two way travel time ranging from -1520ms to -

1570ms. The South-western region depicts high two way travel time ranging from -1770ms to -

1820ms. Depth Maps

Using the velocity model output result from the five wells, Figure 6 shows the depth map for

reservoir sand A, the depth ranging from -4850ft to -5750ft. The Northern part depicts region of

high depth with values ranging from -4850ft to -4950ft. The South-western part depicts region




of shallow depth with values ranging from -5650ft to -5750ft. Figure 7 shows the depth map for

reservoir sand B with depth values ranging from -5300ft to -6600ft. The Southwest part depicts

region of shallow depth with values ranging from -6100ft to -6600ft.The North-western part

depicts region of high values with depth values ranging from -5300ft to 5400ft. The three

dimensional side view of these structural maps of reservoirs A and B are shown in Figure 8

Figure 5: Time surface map for reservoir B

Figure 6: Depth surface map of reservoir A

Figure 7: Depth surface map of reservoir B

Figure 8: Reservoirs A and B in 3 dimensional view




Reservoir Parameter Model Reservoir A porosity model shows heterogeneous porosity properties with different colours as

shown in Figure 9. Higher porosities (red) colour exists internally in the reservoir while the

lowest porosities exist at the boundaries (pink colour). The net to gross model result for reservoir

A shows that at approximately 1.0, this property evenly spread (red colour), Figure 10. The permeability model result for Reservoir A (Figure 11) shows that higher permeabilities exist

internally (red colour) while lower permeability also exist at the boundaries (pink and blue

colour).

Figure 9: Reservoir A porosity model

Figure 10: Reservoir A net to gross model

Figure 11: Reservoir A permeability model Porosity model result for reservoir B (Figure 12) shows that low porosities exist at the boundaries

(pink – blue colours) while slightly higher porosities exist westward (blue colour). Permeability

model result for reservoir B (Figure 13) shows low permeabilities at the boundaries particularly

southwest (pink colour) while high permeability exist in this reservoir (red colour). Net to gross

model result of reservoir B (Figure 14) shows high values southwest to Northwest region (red

colour) while low value (pink colour) exist from the South to Northeastern boundary.




Figure 12: Reservoir B porosity model

Figure 13: Reservoir B permeability model

Figure 14: Reservoir B net to gross model Results of Dynamic Simulation

Results of Dynamic Simulation for Reservoir Sand A

Future production prediction was done on oil rate constraint where oil production started January

2015 to July 2040. This was made possible by the application of data from static modelling,

Pressure Volume and Temperature (PVT), reservoir and production data. Dynamic pre-

processing using laboratory PVT data was done for Sand A reservoir to determine the appropriate

correlations to use for the reservoir properties such as the expected bubble point, solution gas oil

ratio and oil formation volume factor. Al Marhoun correlation was selected for the bubble point and solution gas/oil ratio while the Standing correlation was selected for the oil Formation

Volume Factor (FVF). Reasons for these selection are based on the applied production data used

in the ECLIPSE software. Results of model initialization with global grids of NX = 165, NY = 72 and NZ = 5 and grid 611

dimensions of 50m x 50m and 2ft. The total number of active cells was 59400. The oil saturation

increased northeast to southwest of the reservoir while the least saturation occurred in the eastern

part of the reservoir. Permeability variations tend to increase from southwest (Figure 15).

Results of oil production simulation predictions (Figure 16) shows that for oil rates of 2500

STB/Day and 25 years duration for 2 case scenarios, the cumulative oil productions volumes

obtained were 23. 3MMSTB for case 1 and 46.62MMSTB for case 2 while the recovery factors

was 28% for case 1 and 55% for case 2 (Table 1).




\

Figure 15: Reservoir sand A oil saturation and permeability model overviews Results of Dynamic Simulation for reservoir sand B

When the formation gas-oil ratio and oil gravity reduced from 3,600 scf/STB to 2500 scf/STB

and from 62API to 51API and the gas gravity was slightly increased from 0.61 sp gravity to 0.7

sp gravity, the model initialization results gave a total of 59,169 active cells for oil saturations

and permeability models. At a reservoirs pressure of 7400 psig and OWC of 6020ft, results of

model predictions (Figure 17 and 18) shows that for a reduced oil rate of 1500 STB/DAY for 25

years duration, the cumulative productions gave 13.32 MMSTB for case 1 and 17.21 MMSTB

for case 2 while the recovery factors are 35% and 48% for case 1 and 2.

Figure 16: Sand A prediction for case 1 and 2

OIL SATURATION

PERMEABILITY




Table 1: Summary of Predictions and Development Scenarios for Reservoir A Case

1

No of wells Oil Rates

(stb/day)

Duration (yrs) Cum Oil prod

(MMSTB)

Recovery

factor (%)

1 2500 25 23.3 28

Case

2

Additional

wells

Oil Rates

(stb/day)


(MMSTB)

Recovery

factor (%)

1 2500 25 46.62 55

Summary of predictions and development scenarios for reservoir B

Case

1

No of wells Oil Rates

(stb/day)


(MMSTB)

Recovery

factor (%)

1 1500 25 13.32 35

Case

2

Additional

wells

Oil Rates

(stb/day)


(MMSTB)

Recovery

factor (%)

1 1500 25 17.21 48

Figure 17: Reservoir sand B oil saturation and permeability model overviews

Figure 18: Sand B prediction case1 and 2

OIL SATURATION

PERMEABILITY




Results of Material Balance Simulation Results of black oil PVT correlations for solution GOR against the reservoir pressure shows that

the GOR increased linearly for all the temperatures to a pressure of about 2836.5 psig at a GOR

of 498.45scf/STB and it became constant. The bubble point was relatively constant at different

reservoir temperatures. The oil density reduces with increasing reservoir pressure to 43.769 lb/ft3 at 3015psig and slightly increased. The calculated bubble point pressure 3053.75 psig at 250oF

from the tank input data panel. This indicates that the variation of the GOR and bubble points

under various degrees of flow (dynamic) conditions were not the same under static conditions. However, relative permeability results from the input data simulation using the Corey function

for oil and water showed the oil relative permeability and water relative. When permeability was

plotted on the same axis versus the water saturation, results showed decreased oil relative permeability with increased water saturation. The water relative permeability increased with

increased water saturation from 0 to 0.75. Results of cumulative oil production (MMSTB) and

Reservoir pressure (psig) plotted on the same axis with time showed that the reservoir pressure

declined steadily with time from 01/01/2015 to 01/06/2020 but with an anomalous pressure

behaviour at 3660.4psig at 31/05/2015 at a 15.755 MMSTB to 3706psig at 01/07/2015 and at

about 16.325 MMSTB (Figure 19). The cumulative oil production increased from zero at

01/01/2015 to at 2409.6 psig to 16.85 MMSTB at 3748psig at 01/06/2020.

Figure 19: Production history of reservoir pressure and cumulative oil production Results of Impact of Production Induced Seismicity This study has provided information on the activation of pores by pressure or slight stress

changes. This is shown in the results of top structural map of reservoir A with three faults (1, 2 and3) showing slight variation in the length of the faults with east –west moved from -5140ft to

-5150ft at location 1 faults 2 increased slightly from – 5700ft to -5730ft at location 2. Faults 3

moved from -5450ft to -5500ft (Figure 20). Results of top structural map of reservoir B showed

the east – west that fault 1 initially truncated at – 5750ft to – 6000ft at location 1. Fault 2 truncated

at -6515ft to -6505ft at location 2. Fault 3 truncated at 6015ft to -6025ft at location 3 (Figure 21). This study has also shown the impact of seismicity induced exploitation of the activation of faults by pore pressure or slight stress build up in and around the reservoir. The southern faults 2 and 3

with some wells likely got infatuated with fluids that led to slight increase in pore pressure. The

pressurized faults was persistently active for several months after drilling, completion and during

production. Patterns of seismicity may indicate stress changes during operation which can

activate fault slip to an offset distance.




Figure 20: Top structural map of reservoir A during post exploration (statics)

and production (post dynamics)

Figure. 21: Top structural map of reservoir B during post exploration (statics)

and production (post dynamics)

CONCLUSION Static interpretation used in this work involves lithostratigraphic correlation and well log

interpretation which includes reservoir identification, reservoir fluid, fluid contacts and petrophysical analysis. Three dimensional seismic interpretations was done by carrying out

seismic to well tie, horizon mapping and domain conversion, seismic attributes analysis and maps

generation. The stock tank oil in place was calculated for the study area to know the reserve

estimate while the reservoirs identified were good. High values of the two way travel time, depth,

amplitude and surface attributes were observed around Northeastern region of each of the

generated maps indicating deep regions. Dynamic modelling used involves validation of data and




pre-processing using different reservoir parameters to run sensitivity modelling for a

predetermined period of time. The cumulative oil production and recovery factor were finally

obtained and models reconstructed. Qualitative analysis of the horizons for sand A and B suggest

the presence of fault traps which affected the stratigraphy. Both reservoirs have thicknesses above

120ft from all the wells penetrating them while porosities and permeabilities showed good continuity for improved transmissibility. However, the integration of static and dynamic modelling results showed slight variation in the

estimated hydrocarbon volume while the RF, cumulative production and oil flow rates prediction

when synchronized with the reservoir position from static modelling could enhance well and

reservoir performance. The suggested reason why the hydrocarbon volume estimates changed in

the dynamic simulation may be due to assumptions used such as type of correlations and qualities of reservoir data, production data and economic data and estimations done under discretized flow

conditions. Meanwhile, wellbore challenges such as sand production, fine particles and pressure

decline can be quickly solved if the production and reservoir engineers have a foresight of the

reservoir architectural and dynamic simulation results.

RECOMMENDATION From the results and findings of this work, it is recommended that with improved quality of static

and dynamic data, highly technical and synergized procedures using state of the art computer

simulation models with reduced assumptions and very efficient history matching of results,

seismologist would be better positioned to harness dynamic modelling methods thereby improve

their skills and increase the quality of static data produced which would also reduce the time and

cost of interpretation.

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Nton, M. E. & Esan, T. B. (2010) Sequence Stratigraphy of EMI Field, Offshore Eastern Niger

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Nwachukwu, J. I. & Chukwura, P. I. (1986). Organic matter of Agbada Formation Niger Delta,

Nigeria. American Association of Petroleum Geologists Bulletin, 70, 48-55.

Oyedele, K.F, Ogagarue, D.O & Mohammed, D.U (2013) Integration of 3D Seismic and Well

Log Data in the Optimal Reservoir Characterisation of EMI field, Offshore Niger Delta

Oil Province, Nigeria. American Journal of Scientific and Industrial Research

Schlumberger (1991). Log interpretation principles/applications. Schlumberger Educational

Services, Texas.

Tearpock, D. J., & Bischke, R. E. (1990). Mapping Throw in Place of Vertical Separation: A

Costly Subsurface Mapping Misconception. Oil and Gas Journal. 88(29), 74-78.

Tearpock, D. J. & Bischke, R. E. (1991). Applied Subsurface Geological Mapping. Prentice -

Hall, PTR, New Jersey. 111-120.

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CHARACTERIZATION OF RESERVOIR SANDS FROM SEISMIC AND …wojast.org/wp/wp-content/uploads/Vol9-2/130_140.pdf · Ilozobhie and Egu: Characterization of Reservoir Sands from Seismic