SANDSTONE RESERVOIR DESCRIPTION: AN OVERVIEW · PDF fileclay minerals (1986) 21, 791-809 sandstone reservoir description: an overview of the role of geology and mineralogy a. hurst

Clay Minerals (1986) 21, 791-809

S A N D S T O N E R E S E R V O I R D E S C R I P T I O N : A N O V E R V I E W OF T H E R O L E OF G E O L O G Y A N D

M I N E R A L O G Y

A. H U R S T AND J. S. A R C H E R *

Department of Reservoir Evaluation, Statoil, Forus, Posboks 300, N-4001 Stavanger, Norway, and *ER C Energy Resource Consultants Ltd., 15 Welbeck Street, London W1M 7PF

(Received 17 April 1985; revised 25 July 1985)

A B S T R A C T : Reservoir description is achieved by the integration of geological, petrophysical and engineering data. Modelling of reservoir performance is made by creating a three- dimensional model of the reservoir volume, where the reservoir is built of cells and layered zones which are defined geologically. Although the scale of cell size is coarse compared to the scale of geological data, it is important that the geological input to define cells is as precise as possible. The texture of clay minerals and their composition are requisite for understanding their influence on reservoir characteristics. Wireline logs probably do not provide sufficient information about clay mineralogy to evaluate reservoir characteristics, but do allow the extrapolation of 'point' mineralogical data into a continuous reservoir description. Evaluation of porosity, permeability and saturation are described, and the possible influence of clay mineralogy on evaluation of these characteristics is discussed.

A multidiscipfinary approach to reservoir description has gained wide acceptance during the last 10 years (Harris, 1975; Weber et aL, 1978; R R I / E R C , 1980; Hearn et aL, 1984). The integrated analyses and interpretation of core and log information, together with fluid and pressure analyses, by geologists, petrophysicists and reservoir engineers has resulted in a valuable base for field development studies, particularly in circumstances where major investment decisions are taken with the benefit of few appraisal wells. This situation is regularly encountered in North Sea reservoir development where a major portion of capital investment is committed early in project life before significant reservoir performance has been established.

Successful reservoir description requires detailed quantitative sedimentological and mineralogical data fully integrated with a petrophysical interpretation. Together, these data define the volume of hydrocarbons in a reservoir and describe the characteristic porosity, permeability and saturation of the reservoir. Additionally, the geological data should describe lateral and vertical variations of reservoir characteristics, and how those characteristics will effect hydrocarbon production.

In recent years it has become even more necessary for specialists working in multidisciplinary groups to develop an increased understanding of the basis and further application of their colleagues' methods of analysis. Reservoir engineering provides a focus for the application of reservoir description to modelling the dynamics and efficiency of recovering hydrocarbons f rom the reservoir environment. To achieve this aim, geological

1986 The Mineralogical Society

792 A. Hurst andJ. S. Archer

and petrophysical data must be represented as cells characteristic of specific reservoir volumes and arranged in layers, reservoir zones, throughout the volume of the reservoir. Reservoir simulation then models flow between the cells and layers under given production conditions.

It is beyond the scope of this paper to review the 'state of the art' of reservoir description, and we aim only to present an overview of some topics we believe to be important. Particularly, we concentrate on the geological input to reservoir description and the role of clay mineralogy in the interpretation of petrophysical and engineering data. Hurst & Archer (1986) [following paper] give some examples of the use of clay mineralogy in reservoir description and evaluation. Clay mineral data are considered to be of prime importance in the provision of a reliable reservoir description which contributes directly to production strategy.

M E T H O D S A N D M E A S U R E M E N T S

Geological data

Although many types of geological analyses are employed to define reservoir properties the 'standard' techniques are core description, thin-section petrography, X-ray diffraction (XRD) analysis and scanning electron microscopy (SEM). The quantitative determination of volatile components (e.g. clays, carbonates, sulphides) may conveniently be performed by thermal analysis (STA-EGA) (Milodowski & Morgan, 1980).

Petrophysical data

These data comprise routine measurements of porosity, permeability and grain density from core plugs, and the interpretation of lithology, porosity and fluid saturation from wireline log responses in open-hole conditions.

Some of the more common wireline logs run in boreholes are given in Table 1. The 'logs' provide a continuous response record of the borehole environment from bottom to top and can be interpreted to give quantitative petrophysical properties. Logging tools have different resolution and radii of investigation and provide averaged measurements at given depths.

Clay minerals affect all log measurements; thus all logs have some potential for determining clay mineralogy. A primary aim of log evaluation is the evaluation of porosity. However, the sensitivity of logs to mineralogy requires that log responses are calibrated for mineralogical effects (Poupon et al., 1971; Patchett & Coalson, 1982; Suau & Spurlin, 1982). The necessity for evaluating the water saturation of hydrocarbon reservoirs, and the relationship between the clay content of sandstones and formation conductivity (Waxman & Smits, 1968), has generated interest in the definition of a petrophysical parameter gshal e

or Vclay, the volume of shale or clay present in a sandstone. Vshal e may be evaluated from most conventional logs (Fertl & Frost, 1980).

Routine core analysis utilizes small trimmed plugs cut from recovered core. The plugs may be cut along, or perpendicular to, bedding planes. Analyses of plug porosity are made on samples of generally <5 cm 3 rock volume taken at ~35 cm intervals from cores. The core porosity is usually measured by compression of He gas in cleaned, dry core plugs, and is reported without compaction correction to in situ stress conditions. Horizontal and

Sandstone reservoir description 793

TABLE 1. Open-hole wireline logs commonly used in the North Sea, and referred to in this paper.

Abbreviation General name Measurement

GR Gamma total gamma radiation PHIN Neutron porosity hydrogen content RHOB Formation density electron density DT Sonic transit time CAL Caliper borehole diameter RXO 1 Microlog, MSFL r resistivity of flush zone LLS 1 Laterologh shallow resistivity of invaded zone LLD 1 Laterolog deep resistivity of formation ILD 2 Induction log deep resistivity of formation ILM 2 Induction log medium resistivity of formation/invaded zone SP Spontaneous potential spontaneous potential NGT Natural gamma tool individual K, Th and U concentrations LDT r Litho-density photoelectric absorption index and

formation density Dipmeter Dipmeter microresistivity of borehole

1 RXO, LLS and LLD are measured simultaneously. 2 ILD and ILM are measured simultaneously. T Trademark of Schlumberger.

vertical measurements of air permeability (Kh, Kv) are made on the same core plugs and are reported without correction for Klinkenberg slippage or net overburden stress. Grain density and bulk density may also be measured. The use of He in porosity measurements minimizes adsorption in the presence of clay minerals. Humidity-controlled drying may be employed when clay minerals particularly sensitive to desiccation are known to be present.

Engineering data

Engineering data are derived from the integration of measurements from tests conducted at reservoir conditions, drill stem tests (DST) and production tests, and fluid properties measured in the laboratory on recovered core. Additional data from wireline logs may also provide a significant input of engineering data, for example, repeat formation testing (RFT) (Van Rijswijk et aL, 1980; Dake, 1982) and measurements while drilling (MWD) (Gravely, 1983; Grosso et aL, 1983).

Permeability estimation from DST data is dependent on the definition of geological parameters. Flow rates (mD ft -1) are obtained from tests which, if the height (h) of the producing reservoir interval can be estimated, can be represented as a permeability. Definition of 'h ' is possible if clear lithological boundaries are present (Fig. 1A) but becomes more complicated in formations with varied grain size (Fig. 1B). A useful summary of DST data and limited geological applications of these data is found in Dickey (1979).

Log measurements may be used to define permeability (Timur, 1968; Coates & Dumanoir, 1973) where values are generated to solve the empirical equation

O3 K = a - -

$2

794

A GR

0 1 0 0 t I

K=444mD

h : 2 7 . 5 m

A. Hurst and J. S. Archer

B GR

0 1 0 0 L I

DST

K(h 1) 9 3 8 m D or K(h 2) 4 8 8 m D

h2=2 5m

FIG. 1. Evaluation of permeability from DST data by defining 'h" the height of the producing interval. (A) Homogeneous sandstone interval with shale at top and base. (B) Sandstone overlain

and underlain by gradual grain-size variations; two possible values for h are given.

where K = permeability (mD), O = porosity, S = surface area per unit bulk volume, and a = an empirical constant (Kozeny constant).

C O R R E L A T I O N OF L O G A N D C O R E D A T A

The gamma-ray (GR) curve from the same wireline log as the formation density (RHOB) is used as the common base depth for any given section. All conventional logging tools have a natural gamma-ray detector, so allowing the GR curve to be used as a reference curve. In offshore operations, drillers' depths (core depths) rarely correspond to log depths; they are matched with log depths, sometimes with the aid of a core gamma log, by petrophysicists and/or geologists. Core-to-log correlation requires the recognition of log signatures, which are characteristic of different lithologies.

Porosity ((~)

An essential task in reservoir description is the correlation of core-measured porosity (DPOR) with porosity evaluated from 'porosity' logs (PHIN, RHOB, DT). Log-evaluated porosity (PHIF) is representative of porosity at reservoir conditions and thus should normally be lower than laboratory-measured porosity. A typical cored interval is shown in Fig. 2 together with the logs commonly used to evaluate porosity, core-measured porosity (DPOR) and a log-evaluated porosity (PHIF). Although the log-evaluated porosity is normally represented as a continuous curve it is in reality a calculation made at ~0.25 m intervals based on the average responses of the log(s) over a 1 to 2 m thick vertical section (PHIFX, Fig. 2). With each successive 0.25 m increment of log depth a new 0.25 m interval value is added to the average and an old 0.25 m value is excluded.

The neutron porosity (PHIN) log, which measures the hydrogen content of the formation, is sensitive to the presence of clay minerals. Hydrogen is associated with clay


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cross bedding ~ sand

low angle lamination ~ shale

~J ~J dish structures u pyrite

t water-escape pipes . mica

I bioturbation .% conglomeratic clasts

~;~ common I v vertical ~ organic fragments h horizontal

FIG. 2. A core interval correlated with porosity logs (PHIN, RHOB, DT), a log-evaluated porosity (PHIF) curve, lab-measured porosity (DPOR), and the 'point' values used to calculate

PHIF (PHIFX).

minerals as hydroxyl groups in their structures (hydrogen index of Juhhsz, 1979) and as surface-bound water, the electrolyte properties of which are interpreted to increase formation conductivity (Waxman & Smits, 1968). A clay-rich sandstone is expected to give a high hydrogen porosity response (interpreted as a low actual porosity) not attributable to a lowering of porosity but to the presence of clay minerals.

796 A. Hurst and J. S. Archer

Formation density (RHOB) is often interpreted to reflect the 'total porosity' (Juh~isz, 1979), thus only requiring calibration with core data before being applied to the generation of a continuous porosity curve. The reliablility of RHOB for use as a 'total porosity' curve is uncertain. Authigenic chlorite in sandstones is commonly the iron-rich polymorph (e.g. Hurst & Buller, 1984), which may have a density up to 3.3 gcm -3, and will, if present in sufficient quantity, affect the formation density. Detrital heavy minerals commonly influence the measurement of formation density, their presence being particularly difficult to correct for using log responses when they occur as concentrations associated with sedimentary structures, e.g. swash laminations or storm deposits. Authigenic pyrite and siderite indicate low porosity if suitable corrections are not made (Clavier et al., 1976; Suau & Spurlin, 1982).

E'valuation of sandstone porosity from logs is frequently made by combining the responses of the three porosity logs (PHIN, RHOB and DT), so statistically reducing the possible effects of any one mineral on the resultant porosity. Refinement of porosity evaluation is possible by assigning 'cut-off' values which define net sandstone. The histogram distribution of log-evaluated porosity in Fig. 3 is typical of a reservoir interval which contains two sandstones, one of which is shaly or micaceous and considered to be of insufficiently good reservoir quality to be included in the net sandstone. Cross-plotting core porosity (DPOR) against log-evaluated porosity (PHIF) may help to enhance the relationship between the two porosities (Fig. 4). In Fig. 4, shaly samples have low porosity and a net sand cut-off is defined at ~9% O (DPOR). Sandstone lithologies may plot within the cut-off area but they are considered not to be of significance to hydrocarbon volumetrics. It should be noted that the curve defined in Fig. 4 does not pass through the origin, as PHIF will generally be lower than DPOR. Two groups of data do not fit with the curve defined in Fig. 4: (i) poorly consolidated samples which have partially disintegrated during laboratory measurement give too low porosities (DPOR); (ii) micaceous samples which give too high PHIF porosities because of the influence of mica on the density

frequency

cut-off value

net ~ J ~ net

i MIN.

value 0

FIG. 3. Frequency plot of porosity (0).

MAX.


.30

.25 0

PHIF

.20

.15

.10

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s x/~* poorly

xx~ (x : x x) consolidated

Xx Xx x x x x Xx xX x X ~ f x x XXx ~ samples

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' samples

NET SAND

CUT OFF

I / I I | I .O5 .10 .15 .20 .25 .30 0 DPOR

FIG. 4. Cross plot of log-evaluated O (PHIF) against He porosity measured from core plugs (DVOR),

(RHOB) and neutron porosity (PHIN) logs. Definition of porosity cut-offs to some extent limits the effects of mineralogy on the log evaluation of porosity. The influence of clay minerals and detrital heavy minerals on porosity logs cannot be fully assessed without mineralogical data.

Permeability ( K)

Permeability cannot be considered as a unique property as its value depends on the scale of measurement. The quantitative interpretation of permeability from downhole testing and laboratory measurements is dependent on both sample selection and geological factors (e.g. Fig. 1).

Calibration of test data made at in situ reservoir conditions with laboratory measurements is of high priority in reservoir description. In situ permeability (DST and production data) is a measure of effective permeability, Ke, of a given fluid, e.g. oil, in the presence of other fluids, e.g. interstitial water. In contrast, permeability measured under laboratory conditions is the absolute permeability K s, to a single fluid. The relationship between K e and K s is described as

K e ( s ) = K a �9 Kr(s )

where K r is the relative permeability which is assumed to be a function of saturation (s). K r may be particularly sensitive to clay mineralogy through the effect of clays on water


saturation and by physical reorganization of clay minerals caused by laboratory treatments prior to permeability measurements. Fibrous illitic cements have been shown to be particularly susceptible to physical reorganization dependent on drying treatment (McHardy et aL, 1982; Cocker, 1984). Heaviside et aL (1983) showed that the 'interface-sensitive' character of fibrous illite, which changed texture in core samples during drying, was responsible for the discrepancies (x20--30) between well test and laboratory- measured permeability in the water zone of the Magnus Field.

Saturation

Resistivity measurements in wells drilled with water-based fluids are used to define the presence of hydrocarbons and the contacts between fluids of different composition, e.g. oil and water. In hydrocarbon-saturated zones, some formation water remains bound to the surfaces of minerals. Effective porosity (Oe) can be described as

Oe = O t - ~[CBW

where O t = total porosity and Oc. w = porosity occupied by clay-bound water. Various petrophysical models for evaluating the water saturation of shaly sandstones are available (Wyllie & Southwick, 1954; Waxman & Smits, 1968; Bussian, 1983; Clavier et al., 1984) Clay minerals are assumed to be the main sources of surface-bound water (OCBW) in sandstones, and therefore, the volume of clay minerals (Vshale) is commonly used to evaluate S w (irreducible water saturation). Vsh,l ~ may be evaluated by many methods (Fertl & Frost, 1980; Worthington, 1985); in the North Sea fields, GR, RHOB and PHIN logs are commonly used.

S w in the region above a transition zone for any given 'lithology' can be compared with in situ log-derived saturation measurements, and from drainage capillary pressure measurements in representative core samples.

R E S E R V O I R M O D E L L I N G

A reservoir model is a combination of geological, petrophysical and engineering data which will be used to simulate the behaviour of the reservoir under production. Construction of a reservoir model requires that inherently different data are combined, such that submicroscopic observations (/an-scale SEM, and XRD) are integrated with measurements relating to thousands of cubic metres in intervals tens of metres thick (DST and production test data). This possibly daunting multidiseiplinary task is realized by the necessity of planning reservoir production strategy.

Geological description

The extent of geological description is largely dependent on the amount of continuous core available. There may also be an element of unwillingness to invest the resources necessary to obtain adequate geological data. Coring in the Norwegian sector is extensive and, in general, a large geological data base is established prior to the start of production. Core description should be made on a scale comparable to that of the most sensitive wirefine logs, i.e. at least 1: 50 the scale of dipmeter logs. Recognition of sedimentary facies is important in defining reservoir zones, layers with similar reservoir characteristics


(Johnson & Stewart, 1985), and detailed sedimentological analysis has been most important in successful reservoir management (Richardson et al., 1978, Weber et aL, 1978, Hearn et aL, 1984).

Mineral analyses, including thin-section analysis and various methods of clay mineral analysis, are (in the authors' experience) rarely conducted on a sampling interval comparable to log and core-plug sampling intervals (every 0.25 m). In practice it is more productive to make mineralogical analyses on tightly-spaced samples from restricted intervals rather than from samples taken routinely at metre intervals. Correlation between mineralogical data and petrophysical data, both logs and core plugs, is best achieved by investigation of specific intervals of interest (Everett, 1984; Hurst & Buller, 1984). These intervals may be defined by variations of reservoir characteristics as shown by log responses and porosity and permeability measurements. Two examples of the value of clay mineralogy in reservoir description are given by Hurst & Archer (1986).

Geological descriptions for reservoir simulation are presented as maps which define the volume and characteristics of the reservoir. Isochore maps (drilled thicknesses of given stratigraphic units) of reservoir zones, often based on sedimentary facies analysis, together with maps of porosity, permeability, saturation and net:gross sand distributions are required. If a sedimentological or diagenetic zonation defines zones only a few metres thick, most of those zones will be grouped together and given an 'average' value in the final reservoir zonation (Fig. 5). This should not detract from the value of small-scale zonation (Fig. 5A) which allows coarser average characteristics (Fig. 5B) to be defined. The known relations between sedimentary structures and permeability (Potter & Mast, 1963; Pryor,

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(met res )

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A B

J/TS --

~iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiil

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FIG. 5. (A) Zonation of a cored interval using geological and petrophysical characteristics. (B) Coarser zonation probably used in the final reservoir zonation.

80,0 A. Hurst andJ. S. Archer

1973; Richardson et al., 1978; Weber, 1982), sedimentary structures and cementation (Glennie et aL, 1978; Hurst & Buller, 1984), and cement distribution and log responses (Hurst, 1984; Hurst & Archer, 1986), emphasize the importance of obtaining detailed geological data in reservoir characterization.

Geology from wireline logs

Wireline logs are commonly used to generate geological information both by integration of the information from conventional logs (e.g. GR, CAL, RHOB, PHIN, DT, MSFL, Table 1), and by developing logging tools (geological logs) capable of measuring geological parametres in the reservoir environment. The processing of log data into 'geological' information is commonly known as 'electro facies'. Although many examples of integrated electro facies analysis exist (Serra & Sulpice, 1975; Mayer & Sibbit, 1980; Wolff & Pelissier-Combescure, 1982) few are rigorously correlated with mineralogical data (Van der Wel & Langeland, 1984).

Geological logs have been developed in order to obtain mineralogical information from the reservoir environment. Natural gamma-ray spectrocopy logs (NGT) measure concentrations of the common radioactive isotopes 4~ 232Th and 236U (Serra et al., 1980; Fertl, 1983). Gamma spectroscopy logs (GST) have the ultimate goal of determining elemental concentrations (Hertzog, 1980; Schweitzer et al., 1984). Lithodensity logs (LDT) measure formation density and photoelectric absorption index, and are of value for identifying clay and mica intervals (Suau & Spurlin, 1982). Dipmeter microresistivity logs provide highly focussed measurements of formation resistivity and may be applied routinely to reservoir description (Serra & Abbott, 1982), giving information about internal structures, sorting and permeability anisotropy. The highly focused nature of the dipmeter log allows lam{nated sandstones and shales to be distinguished from sandstones with a high proportion of interstitial clay minerals (Sallee & Wood, 1984). Dipmeter logs undoubtedly contribute much additional information to traditional geological descriptions. Independent field applications of the other geological (NGT, GST, LDT) logs are few and it remains unproven how useful they will be after further development, of if they are economic to run (Willey & Zittel, 1982; Peveraro & Russell, 1984).

Geological logs and electro facies techniques are developed to reduce coring costs, especially after production is established. Many geological logs are developed primarily not to provide geological or mineralogical information but to provide data to define saturation and porosity in the reservoir environment.

Construction of the simulation model

A reservoir zonation is generated from the combination of geological and petrophysical parameters which define the volume and characteristics of a reservoir as a series of layers (Harris, 1975; Weber et al., 1978). Application of geological data to a reservoir simulation requires that reservoir cells are defined (Fig. 6A) and given numerical values for their reservoir characteristics (porosity, permeability, saturation, net:gross sand). Construction of the reservoir model with cells is analogous to assembling building blocks (Fig. 6B), where typical cell sizes may be 200 m x 100 m x 20 m in a field development study. Although cells of 4 x 104 m 3 volume are unlikely to represent realistic geological units, for simulation it is necessary to model the behaviour of heterogeneous regions as equivalent homogeneous cells (Fig. 7). The

Sandstone reservoir description

A

AZ

AX

SINGLE CELL

�9 UNIFORM POROSITY

UNIFORM PRESSURE7 �9 POTENTIAL

�9 UNIFORM SATURATION

�9 UNIFORM PERMEABILITY IN A GIVEN DIRECTION

B

m

I2om

FIG. 6. (A) Single cell defined for reservoir simulation. (B) Typical composite of homogeneous cells representing a volume of heterogeneous reservoir.

801

simplification implicit in Fig. 7 is analogous to the combination of several fine geological zones into coarser zones (Fig. 5). Simulation models with various cell sizes may be defined to accommodate extra geological information, e.g. finer cell sizes to describe a more heterogeneous volume of reservoir. Flow between cells (Fig. 8) is controlled during simulation by characterizing transmissibility (Kyte & Berry, 1975). RFT (Table 1) pressure data from new wells in a producing field are particularly helpful for quantifying vertical transmissibility (Bishlawi & Moore, 1980; Nadir, 1980). The presence of faults, permeability barriers and fractures can be simulated by enhancing or reducing transmissibilities by using multiplier factors. Zones of diagenetic mineralization are frequently represented in simulation models by using transmissibility multiplication factors.

Major limitations to the amount of geological information which can be incorporated into a reservoir simulation are the capacity of the available computer facility and cost. Computer size limits the number of cells which can be defined, and thus limits the complexity of geological information which can be simulated.

D I S C U S S I O N

Use of #m-scale mineralogical observations (SEM, XRD) to describe reservoir characteristics is only worthwhile if the distribution of minerals and their textures can be related to larger-scale features (Weber, 1982). Study of sandstone diagenesis, to a limited

802 A. Hurst and J. S. Archer

2 0 0 m I

A

HETEROGENEOUS

RESERVOIR

CELLS

2 0 0 m I I

B

SINGLE

E Q U I V A L E N T

CELL

FIG. 7. (A) A unit of heterogeneous reservoir cells defined by geological description which for the purposes of simulation might be described as (B), a single equivalent cell.

I \ \

~ - ~ - - I C E L L ~

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21~ H �9 k r A Tx ( -X-d i rect ion transmissibil i ty)=

(L 1 § 2 )

FIG. 8. Transmissibi l i ty in a reservoir cell.


extent, provides information about porosity distribution (both primary and secondary), by examining the relationships between sedimentary facies, lithology and diagenesis.

Clay mineralogy in sandstones has often been the focus of interest concerning formation damage, both during primary production and during reservoir stimulation (Almon & Davies, 1979; Thomas, 1979). Stimulation of reservoirs often involves the injection of corrosive fluids into a reservoir to enhance permeability. Fluid injection programmes must take into account the possible interactions between minerals and injected fluids. For example, acid injection may cause dissolution of one phase and reprecipitation of another, less-dense, phase which lowers rather than increases permeability. The main contribution of mineralogy in reservoir description, however, is to reservoir volumetrics and production strategy, although the same data may later contribute to stimulation planning.

Use of any specific log to evaluate porosity or water saturation may result in generation of too low or high values, and produce unrealistic trends of these values, because that log is particularly sensitive to the presence of a clay mineral (example 2 in Hurst & Archer, 1986). Uncritical application of shaly sand models in North Sea reservoirs may give an overestimation of formation conductivity and therefore an overestimation of hydrocarbon saturation (/~,bo, 1984). Overestimation of conductivity is common in sandstones where authigenic kaolinite is the only common clay mineral. The electrolytic properties of water bound to the surface of kaolinite are thus interpreted to be poor.

Clay mineralogy

Kaolinite, illite, chlorite, smeetite and mixed-layer illite-smectite are the most commonly described and volumetrically significant clay minerals in reservoir sandstones. Distribution of clay minerals in sandstones can be achieved by integrating log and mineral data (examples 1 and 2, Hurst & Archer, 1986). Various log-detectable characteristics of clay minerals are listed in Table 2. A major problem with log identification of clay mineralogy is that textural variations of individual clay minerals change some characteristics. For example, hydrogen index and natural gamma radiation remain unchanged whereas cation exchange capacity (CEC) varies with texture.

Descriptions of clay mineral distributions in sandstones are commonly made in terms of laminated, dispersed or structural types (Frost & Fertl, 1981), and are described in Fig. 9. If a clay mineral is 'dispersed' (e.g. most authigenic clays) it has a higher surface area:mass

TABLE 2. Characteristics of common clay minerals of significance in reservoir description. Data from Fertl & Frost (1980).

Clay Structural Density Hydrogen CEC Natural gamma radioactivity mineral formula (gcm -3) index (mEq/100 g) K (%) Th (ppm) U (ppm)

Kaolinite AI4[Si4010](OH) 8 2.60-2.68 0-36 3-15 0.42 6-19 1.5-3.0 Chlorite (Mg, AI, Fe)I~[(SI, Al)sO 2o] 2.60-2.96 0.34 10-40 - - - - - -

(OH)s Smectite (�89 a, Na)0.7(Al, Mg,Fe) 4 2.20--2.70 0.13 80-150 0.16 14 -24 2.0-5.0

[Si, A1)8020](OH)4. nH20 Illite K1_I.sAI4[SiT_6.sAIl_l.sO20] 2.64-2.69 0.12 10-40 4.5 <2.0 1.5

(OH),


A B

C D

E FIG. 9. Textural styles of clay minerals in sandstones: (A) authigenic grain-coating cement; (B) detrital clasts; (C) authigenic pore-filling cement; (D) clay-rich lamina; (E) micaceous lamina.

(A) and (C) are dispersed clays, (B) are structural clays, (D) and (E) are laminated clays.

ratio and a higher CEC than if it occurs as a structural clay. High CEC values imply good electrolytic characteristics for the clay-bound water and thus the potential for lowering formation resistivity as measured by logs. Illite occurring in shale clasts (Fig. 9B) has much lower surface properties than illite occurring as a grain-coating cement (Fig. 9A), despite that both illites may have the same composition. Indeed, structural clay (shale clasts) may be considerably more abundant than dispersed clay (authigenic) but nevertheless have less effect on reservoir characteristics (Fig. 10). In Fig. 10 permeability reduction in intervals containing shale clasts is interpreted as being caused by differential compaction of the shale. Grain-coating cement is of the type described by Pallett et al. (1984) where a permeability reduction of between one and two orders of magnitude occurs. In Fig. 10 it is assumed that the clay mineral present has a detectable gamma radioactivity.

Authigenic clay minerals are known to have very varied textures (Wilson & Pittmann, 1978; Welton, 1984) and consequently wide ranges of surface properties (Table 2). It is emphasized that the characteristics of clay minerals listed in Table 2, which are routinely used in petrophysical analysis, are largely derived from measurements made on standard clays--mineral samples from clay deposits or generally coarse-crystalline minerals--and not clay minerals from sandstone reservoirs. CECs of standard clays are known to vary with increased surface area (Patchett, 1975); therefore the use of such laboratory-measured CEC values (Table 2) to evaluate clay characteristics in sandstones is perhaps unwise.


GR 1OO

I

:~:i!B.'ii:i:i~:OB]

P H I N .6 o I !

1,7 R H O B I

2.7 R X O I

1 2 I

10 I

KH:16OOmD

KH=2OOOmD

~.~ K H = 8 O O D

o 0 t~ s h a l e c l a s t s ~ clay g r a i n - c o a t i n g

K H : h o r i z o n t a l p e r m e a b i l i t y

FIG. 10. Effect of dispersed clays (grain-coating cement) and structural clays (shale clasts) on log responses and permeability.

Natural gamma radioactivity of clay minerals is bound by similar constraints, i.e. laboratory measurements of standard minerals and an absence of data from authigenic clay minerals. Uncritical application of standard data (Table 2) to characterize sandstones in the absence of supporting mineralogical and textural data will inevitably result in erroneous assumptions regarding clay mineral type and abundance.

It is inadequate only to identify and quantify the clay mineralogy of sandstones; textural data are requisite to reservoir description. At present, petrophysical log analysis does not provide sufficient information about clay mineralogy to evaluate fully reservoir characteristics. Improvements of existing logging tools and the increased awareness of engineers and petrophysicists to mineralogy will undoubtedly contribute much to a better understanding of the reservoir characteristics of clay minerals in sandstones.

Reservoir zonation

A convenient method of examining the validity of sedimentological zonation in terms of reservoir characteristics (porosity, permeability, saturation) is by plotting histograms of the characteristic of interest for a defined zone. Some typical histogram distributions of porosity (0 ~) and permeability (K) are shown in Fig. 11. A reservoir zone with

806

A 5 0 -

4 0

3 0 -

2 0

10

0

0 I

0 .1 I

0 . 2 0 , 3

0

A. Hurst and J. S. Archer

B 5 0

4 0

3 0

2 0

10

I I 0 . 4 0 . 5

I I I I 0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5

O

C 5 0

D 5 0 -

4 0

3 0

2 0

10

0

4 0

3 0 -

2 0 -

i r I I I 0 I i ! 0.1 0 . 2 0 . 3 0 . 4 0 , 5 0 0 .1 0 . 2 0 . 3 0 . 4

l o g (K) 0

FIG. 11. (A) Normal 0 distribution typical of a homogeneous reservoir zone. (B) Slightly skewed bimodal 0 distribution associated with a reservoir zone containing two distinct lithologies. (C) Skewed log normal K distribution typical of a reservoir zone containing grain-size gradation. (D) Skewed broad normal 0 distribution from a heterogeneous (varied grain-size

sorting) reservoir zone.

I

0 . 5


homogeneous porosity characteristics will give a normal distribution (Fig. 11A), whereas if porosity heterogeneities are present, e.g. finer grained or micaceous intervals (cf. Fig. 3), a bimodal distribution will be more typical (Fig. 11 B). If a reservoir zone contains distinct grain-size variations, e.g. a coarsening-upward sequence from very-fine micaceous sandstones to medium sandstones, a log-normal distribution for permeability (Fig. 11C) and a broad normal distribution of porosity (Fig. 11D) are expected.

Bimodal distribution of characteristics, or anomalous data points on cross-plots (Fig. 4) must be evaluated with respect to possible mineralogical controls. The significance of mineral effects on porosity must in turn be evaluated with respect to their influence on permeability and water saturation. Anomalous porosity distribution need not represent permeability heterogeneity. Statistical tests can be applied to data sets to determine whether log and core data belong to the same reservoir zone (Archer, 1985).

Bimodality (Fig. l iB) of reservoir characteristics does not mean that the existing reservoir zonation is incorrect, although pronounced bimodal distributions may indicate the need for defining further sub-zones. Bimodality is also typical of interbedded sandstone facies, e.g. fine micaceous sandstones interbedded with coarse quartzose sandstones. The heterogeneity of a particular zone is readily shown by histograms: Figs. 11D and 11A have similar average porosities but the former is more heterogeneous than the latter.

Sedimentological reservoir zonations lack the necessary numerical definition for reservoir simulation. The average porosity, permeability and saturation values derived from plots of zones (as in Fig. 11) are typical of the values used in simulation. It is therefore critical that heterogeneity of reservoir characteristics and geology are appreciated for input to the reservoir simulation.

ACKNOWLEDGMENTS

A. T. Buller is acknowledged for encouraging the compilation and writing of this paper and for criticism of an early draft of the manuscript. Tone Lien is thanked for patiently re-writing several drafts of notes which eventually formed the basis of the text. Den norske stats oljeselskap (Statoil) are acknowledged for supporting and encouraging publication of the paper.

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