Integration of geological and petrophysical data.pdf

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Improved Prediction of Reservoir BehaviorThrough Integration of QuantitativeGeological and Petrophysical Data

D. K. Davies, SPE, R. K. Vessell, and J. B. Auman, David K. Davies & Assocs. Inc.

Summary

This paper presents a cost effective, quantitative methodology forreservoir characterization that results in improved prediction of

permeability, production and injection behavior during primary

and enhanced recovery operations. The method is based funda-

mentally on the identification of rock types intervals of rock with

unique pore geometry. This approach uses image analysis of core

material to quantitatively identify various pore geometries. When

combined with more traditional petrophysical measurements, such

as porosity, permeability and capillary pressure, intervals of rock

with various pore geometries rock types can be recognized from

conventional wireline logs in noncored wells or intervals. This

allows for calculation of rock type and improved estimation of

permeability and saturation. Based on geological input, the reser-

voirs can then be divided into flow units hydrodynamically con-

tinuous layers and grid blocks for simulation. Results are pre-sented of detailed studies in two, distinctly different, complex

reservoirs: a low porosity carbonate reservoir and a high porosity

sandstone reservoir. When combined with production data, the

improved characterization and predictability of performance ob-

tained using this unique technique have provided a means of tar-

geting the highest quality development drilling locations, improv-

ing pattern design, rapidly recognizing conformance and

formation damage problems, identifying bypassed pay intervals,

and improving assessments of present and future value.

Introduction

This paper presents a technique for improved prediction of per-meability and flow unit distribution that can be used in reservoirs

of widely differing lithologies and differing porosity characteris-

tics. The technique focuses on the use and integration of pore

geometrical data and wireline log data to predict permeability and

define hydraulic flow units in complex reservoirs. The two studies

presented here include a low porosity, complex carbonate reser-

voir and a high porosity, heterogeneous sandstone reservoir.

These reservoir classes represent end-members in the spectrum of

hydrocarbon reservoirs. Additionally, these reservoirs are often

difficult to characterize due to their geological complexity and

frequently contain significant volumes of remaining reserves.1

The two reservoir studies are funded by the U.S. Department of

Energy as part of the Class II and Class III Oil Programs for

shallow shelf carbonate SSC reservoirs and slope/basin clasticSBC reservoirs.

The technique described in this paper has also been used to

characterize a wide range of other carbonate and sandstone reser-

voirs including tight gas sands Wilcox, Vicksburg, and Cotton

Valley Formations, Texas, moderate porosity sandstones Middle

Magdalena Valley, Colombia and San Jorge Basin, Argentina ,

and high porosity reservoirs Offshore Gulf Coast and Middle

East.The techniques used for reservoir description in this paper meet

three basic requirements that are important in mature, heteroge-

neous fields.

1. The reservoir descriptions are log-based. Flow units are

identified using wireline logs because few wells have cores. Inte-

gration of data from analysis of cores is an essential component of

the log models.

2. Accurate values of permeability are derived from logs. In

complex reservoirs, values of porosity and saturation derived from

routine log analysis often do not accurately identify productivity.

It is therefore necessary to develop a log model that will allow the

prediction of another producibility parameter. In these studies we

have derived foot-by-foot values of permeability for cored and

non-cored intervals in all wells with suitable wireline logs.3. Use only the existing databases. No new wells will be

drilled to aid reservoir description.

Methodology

Techniques of reservoir description used in these studies are based

on the identification of rock types intervals of rock with unique

petrophysical properties. Rock types are identified on the basis of

measured pore geometrical characteristics, principally pore body

size average diameter, pore body shape, aspect ratio size of

pore body: size of pore throat and coordination number number

of throats per pore. This involves the detailed analysis of small

rock samples taken from existing cores conventional cores andsidewall cores. The rock type information is used to develop the

vertical layering profile in cored intervals. Integration of rock type

data with wireline log data allows field-wide extrapolation of the

reservoir model from cored to non-cored wells.

Emphasis is placed on measurement of pore geometrical char-

acteristics using a scanning electron microscope specially

equipped for automated image analysis procedures.2– 4 A knowl-

edge of pore geometrical characteristics is of fundamental impor-

tance to reservoir characterization because the displacement of

hydrocarbons is controlled at the pore level; the petrophysical

properties of rocks are controlled by the pore geometry.5– 8

The specific procedure includes the following steps.

1. Routine measurement of porosity and permeability.

2. Detailed macroscopic core description to identify verticalchanges in texture and lithology for all cores.

3. Detailed thin section and scanning electron microscope

analyses secondary electron imaging mode of 100 to 150 small

rock samples taken from the same locations as the plugs used in

routine core analysis. In the SBC reservoir, x-ray diffraction

analysis is also used. The combination of thin section and x-ray

analyses provides direct measurement of the shale volume, clay

volume, grain size, sorting and mineral composition for the core

samples analyzed.

4. Rock types are identified for each rock sample using mea-

sured data on pore body size, pore throat size and pore intercon-

nectivity coordination number and pore arrangement.

Copyright © 1999 Society of Petroleum Engineers

Original manuscript received for review 7 October 1997. Revised manuscript received 8December 1998. Paper peer approved 4 January 1999. Paper (SPE 55881) was revisedfor publication from paper SPE 38914, first presented at the 1997 SPE Annual TechnicalConference and Exhibition, San Antonio, Texas, 5 –8 October.

SPE Reservoir Eval. & Eng. 2 2, April 1999 1094-6470/99/22 /149/12/$3.50

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5. Algorithms that relate porosity to permeability for each rock

type in cored wells are developed.

6. Log analysis is performed using normalized and environ-

mentally corrected logs. The log shale indicators are calibrated to

data from petrographic analysis, specifically, shale volume de-

rived from thin section analysis, to allow improved accuracy in

the determination of porosity.

7. Identification of rock types using log responses in cored

intervals, and comparison with core data.

8. Extension of the rock-log model to all wells with sufficient

logs in the field. Specific algorithms are developed on a field-by-

field basis that allow the identification of rock types from log data.9. Prediction of permeability, foot-by-foot, in all wells using

algorithms that relate porosity to permeability by rock type.

10. Field-wide correlation of rock types and identification of

flow units for reservoir simulation.

Pore Geometry Modeling. Analysis of pore geometry and inte-

gration of this data with wireline log data allow field-wide reser-

voir characterization to be pore system oriented. Pore geometry

analysis involves identification of pore types and rock types. Pro-

cedures for the measurement of pore geometry parameters have

been documented in geological and engineering literature and are

briefly discussed below.

Pore Types. The determination of pore types in a reservoir re-

quires the use of rock samples conventional core, rotary sidewall

cores, and cuttings samples in favorable circumstances. In this

study, analysis is based on 1 in. plugs removed from conventional

cores. Individual pore types are classified in terms of the follow-

ing parameters.

Pore Body Size and Shape. Determined using scanning elec-

tron microscope SEM image analysis of the pore system.2

Pore Throat Size. Determined through capillary pressure

analysis, SEM analysis of pore casts and direct measurement in

the SEM.4,7

Aspect Ratio. The ratio of pore body to pore throat size: a

fundamental control of hydrocarbon displacement.7,9

Coordination Number. The number of pore throats that inter-

sect each pore, determined from SEM analysis of pore casts.

10

Pore Arrangement. The detailed distribution of pores in each

sample as determined in thin section and SEM analyses.10

These parameters are combined to yield a classification of the

various pore types in these rocks. Pore types are identified in each

core sample. A complex core sample may contain several differ-

ent pore types. It is therefore necessary to group pore types into

rock types. A rock type is an interval of rock characterized by a

unique pore structure11 not necessarily a unique pore type. Each

rock type is characterized by a particular assemblage suite of

pore types. For each sample, the volume proportion of each pore

type is determined using SEM-based image analysis.3 This proce-

dure for rock type identification offers the following advantages. It has long been known that rock types, classified on the basis

of pore geometry, directly control hydrocarbon displacement effi-ciency aspect ratio, coordination number, and pore

arrangement.7,9,11

The classification procedure presented here assumes that no

fixed relation exists between the size of pore bodies and pore

throats. In this regard, we accept the well known premise of the

independence of pore body and pore throat size.12

Rock types are identified independent of measured values of

porosity and permeability. Predictions of permeability are based

on a knowledge of porosity and rock type. This avoids the circu-

larity evident in classification schemes that use porosity and per-

meability data to identify rock types and, in turn, use the rock

types to predict permeability.

Because throat size is known for each pore type, it is possible

to develop a pseudo-capillary pressure curve for each sample us-

ing the well known relationship:13

Pc214/ d . 1

Different rock types have different pseudo-capillary pressure

curves. The validity of the geologically determined rock types is

evaluated through mercury capillary pressure analysis of selected

samples. Results reveal differences between the rock types in

terms of measured capillary characteristics. Such cross checks al-

low independent validation of the pore geometrical classification

of rock types. The mercury capillary pressure data are also used toaid in the determination of pore throat sizes.

Low Porosity, Shallow Shelf Carbonate Reservoir

Background. Shallow shelf carbonate reservoirs in the U.S. origi-

nally contained 68 BBO about one-seventh of all the oil dis-

covered in the lower 48 states. Recovery efficiency is low; some

20 BBO have been produced and current technology may only

yield an additional 4 BBO.1 The problem of low recovery effi-

ciency in SSC reservoirs is not restricted to the U.S.—it is a

worldwide phenomenon. SSC reservoirs share a number of com-

mon characteristics, including the following. A high degree of areal and vertical heterogeneity, relatively

low porosity and relatively low permeability. Reservoir compartmentalization, resulting in poor vertical and

lateral continuity of the reservoir flow units and poor sweep effi-

ciency. Poor balancing of rates of injection and production, and early

water breakthrough in certain areas of the reservoir. This indicates

poor pressure and fluid communication and limited repressuring. Porosity and saturation as determined from analysis of wire-

line logs do not accurately reflect reservoir quality and perfor-

mance. Many injection and production wells are not optimally com-

pleted with regard to placement of perforations, and the stimula-

tion treatment can be inadequate for optimal production and in-

jection practices.

The North Robertson Clearfork Unit exhibits all of these char-acteristics. The North Robertson Unit NRU was the single larg-

est waterflood installed in the onshore, lower 48 states of the U.S.

during the 1980s. The unit covers 5,633 acres, has 259 wells and

uses a 40 acre 5-spot waterflood pattern with 20 acre nominal well

spacing. The field was on primary production from 1954 to 1987;

the secondary waterflood has been in place since 1987. Currently,

the field has 144 active producing wells, 109 active injection wells

and 6 water supply wells. An objective of this current study is to

identify the areas of the Unit with the best potential for additional

in-fill drilling planned for 10 acre spacing.

The original oil in place is estimated at 260 MMSTB with an

estimated ultimate recovery factor of 13.5% primary recovery

7.5%, secondary recovery6% based on the current produc-

tion and workover schedule. Current Unit production is approxi-mately 3,000 STB/D and 11,000 BWPD at a water injection rate

of 20,000 BWIPD.

The NRU is located in Gaines County, West Texas, on the

northeastern margin of the central basin platform Fig. 1. Produc-

tion is from the Lower Permian Glorieta and Clear Fork Carbon-

ates. The reservoir interval is thick (gross interval1400 ft).

More than 90% of the interval has uniform lithology dolostone,

but is characterized by a complex pore structure that results in

extensive vertical layering see rock type distribution, Fig. 2. The

reservoir is characterized by discontinuous pay intervals and high

residual oil saturations 35% to 60%, based on steady state mea-

surements of relative permeability. The most important immedi-

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ate problem in the field is that values of porosity and saturation

determined from wireline logs do not accurately reflect reservoir

quality and performance. Intervals with relatively low porosity

and high water saturation frequently produce oil at higher rates

than intervals with relatively high values of porosity and low val-

ues of water saturation.

Depositional/Diagenetic Model. Permian carbonates in the NRU

were deposited in several environments related to a low relief

shoreline and shallow marine shelf. Small a few feet vertical

fluctuations in sea level caused significant lateral migration of

facies due to lack of vertical relief 0.5 ft/mile. This resulted in

rapid vertical stacking and alternation of deposits of different en-

vironments facies. Post-depositional diagenetic dolomitization

resulted in significant ‘‘blurring’’ of facies boundaries, but this

did not totally eradicate the facies-related layering profile estab-

lished at the time of deposition: reduction of the original porosity

and permeability and modification of the original pore geometry.

Because of the diagenetic modification of pore structure, thereis no obvious relationship between porosity and permeability Fig.

3. It is not possible to predict permeability with any acceptable

degree of accuracy from knowledge of the porosity. Hence log

identification of potential pay intervals is difficult. Such complex

relationships between porosity and permeability are not confined

to the NRU: they are common in most carbonate reservoirs world-

wide because most carbonate rocks have undergone significant

diagenesis.

A method used in many reservoir studies to resolve this di-

lemma is to relate porosity and permeability to depositional envi-

ronment. In the NRU, there is no relationship among porosity,

permeability and depositional environment Fig. 3. Different en-

vironments have similar ranges of porosity and permeability. This

is not surprising. The carbonates have undergone significant di-agenetic alteration of pore geometry in all environments thus there

is no fundamental relationship between depositional environment

and permeability. This problem is common in many diagenetically

altered reservoirs sandstones and carbonates.

Most geological reservoir characterizations are rock oriented:

they stress environments of deposition and lithology. However,

useful models of the reservoir are pore system dependent. There-

fore in rocks with complex pore structure, it is necessary to de-

scribe the reservoir in terms of pore geometry rather than in terms

of the characteristics of the solid components of the reservoir

based on environments or lithology.

Porosity/Permeability Relationship. In the NRU, eight rock

types are identified based on the relative volumetric abundance of

each pore type. While each rock sample normally contains more

than one pore type, most rock types are characterized by one

dominant pore type. For example, rock type 1 is dominated by

pore type A and lacks pore type B; rock type 2 is dominated by

pore types B and C; rock type 8 contains only pore type 8 Fig. 4,

Table 1. Identification of rock types is fundamentally important

because porosity and permeability are related within a specific

pore structure.12

The basic relationship between porosity and permeability ex-

hibits a considerable degree of scatter in the NRU up to four

orders of magnitude variation in permeability for a given value of

porosity. However, porosity and permeability are closely related

for each rock type RT Fig. 5. The rock type relationship with

permeability has an error range of less than one-half decade for

most samples. Regression equations are developed for each rock

type to quantitatively define each relationship using log-log plots

to avoid zero porosity intercepts. These equations are used in the

field-wide prediction of permeability permeability being a func-

tion of porosity and rock type.

The slope of the individual regression lines varies among rock

types. This demonstrates the well known independence of pore

body and pore throat size.12 Some methods of flow unit classifi-

cation assume a constant relationship between pore body and pore

throat size.14,15 This is unfortunate because, in such classification

schemes, the slopes of the porosity-permeability regression lines

Fig. 1–Location map of some of the major carbonate fields in

West Texas that have been characterized with the methodology

described in this paper.

Fig. 2–Log response profiles and vertical distribution of rock

types, NRU.

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are identical for each rock type. This means that pore bodies and

pore throats increase in size at identical rates in all rock types: an

improbable characteristic of rocks with complex pore systems.

Average values of porosity and permeability are given for each

rock type in Table 2. Rocks with the highest porosity in the NRU

do not have the highest permeability. The principal pay rocks in

the field are rock types 1 and 2. They have significantly lower

values of porosity but higher values of permeability than rock type

4. This has important implications in terms of selecting zones to

perforate. Obviously, zones with the highest porosity should not

be the principal targets in this field. Accurate prediction of per-

meability from wireline logs is therefore of fundamental impor-

tance.

Rock/Log Model. The existing database consists of conventional

cores from 8 wells and 120 wells with a relatively complete mod-

ern log suite that includes the gamma ray GR, photoelectric

factor PEF, bulk density RHoB, neutron porosity PHIN and

dual laterolog Ll. Pore geometry analysis reveals that eight rock

types occur in the NRU. Six of the rock types are dolostone, one

Fig. 3–Core-derived values of porosity and permeability for principal depositional environments, NRU.



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is limestone non-pay: structurally low and wet in this field, one

is shale Table 2. Individual rock types can be recognized using

specific cut-off values based on analysis of environmentally cor-

rected and normalized log responses and comparison with core-

based determination of rock type. The combinations of log re-

sponses used to discriminate among rock types in the NRU are the

following. Apparent matrix density Rhomaa versus apparent matrix

volumetric photoelectric factor Umaa with gamma ray: allows

discrimination of dolostone rock types 1 through 4, limestone

rock type 5, anhydritic dolostone rock type 6, siltstone rock

type 7 and shale rock type 8 Fig. 6. Laterolog shallow Lls, laterolog deep Lld and porosity:

allows discrimination among dolostones and rock types 1 through

4 Fig. 7.

The rock-log model was first developed for five cored wells

only. Subsequently the model was extended to the three remaining

cored wells. Evaluation of cored intervals reveals successful dis-

crimination 80% of each of the principal rock types rock types 1 through 4 despite the fact that the wells were logged by

different companies at different times. Misidentification of rock

type 1 results in identification of rock type 2, while misidentifica-

tion of rock type 2 results in identification of rock type 1, thus,

there is no significant misidentification of the dominant rock types

by logs over the cored intervals. Much of the misidentification is

due to the fact that eight rock types are identified using five inde-

pendent log responses, together with normally calculated porosity

and water saturation. The rock type model is extended to all wells

with sufficient log suites in the field 120 wells in the NRU.

Specific algorithms allow rock type identification on a foot-by-

foot basis in each well. As has been shown previously Fig. 5,

permeability is a function of rock type and porosity. Rock type

and porosity can be determined from well log responses alone.

Therefore, permeability can be predicted using well log informa-

tion. This allows the development of a vertical layering profilebased on rock type and permeability in cored and non-cored wells

Fig. 2. The resulting reservoir model is numeric, log-based and

suitable for simulation input.

Hydraulic Flow Units. Individual hydraulic flow units HFUs

are identified based on integration of data regarding the distribu-

tion of rock types and petrophysical properties particularly per-

meability and fluid content. Evaluation of these data for 120

wells reveals that rock types are not randomly distributed. The

principal reservoir rocks rock types 1 and 2 generally occur in

close association, and they alternate with lower quality rocks

rock types 3, 4, 6, 7 and 8. Correlation of rock types between

wells reveals an obvious layering profile in which 12 distinct lay-

ers, hydraulic flow units, are distinguished in the NRU.Maps were prepared for each of the HFUs to illustrate the

distribution of important petrophysical parameters. The distribu-

tion of the principal rock types for each HFU was also mapped.

This allows rapid identification of areas of the field dominated by

either high quality or low quality rock. Examples of these maps

and cross sections of hydrodynamic flow units are presented

elsewhere.16

There is a general tendency in the NRU for the higher quality

rocks rock types 1 and 2 to occur in discrete belts on the north-

east edge of the Unit while lower quality rocks rock types 3 and

4 occur in southwest portions of the Unit. Within this general

Fig. 4 –Volumetric proportions of

pore types in each rock type.

TABLE 1– PORE TYPE CLASSIFICATION, NRU

Pore Type

Size

( m) Shape Coordination Aspect Ratio Pore Arrangement Geological Description

A 30–100 Triangular 3–6 50–100:1 Interconnected Primary interparticleB 60–120 Irregular 3 200:1 Isolated Shell molds and vugsC 30–60 Irregular 3 100:1 Isolated Shell molds and vugsD 15–30 Polyhedral 6 50:1 Interconnected IntercrystallineE 5–15 Polyhedral 6 30:1 Interconnected IntercrystallineF 3–5 Tetrahedral 6 20:1 Interconnected IntercrystallineG 3 Sheet/slot 1 1:1 Interconnected Interboundary sheet and

intercrystalline pores

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trend, perturbations exist in the distribution of permeability. These

perturbations are important because they result in compartmental-

ization of the reservoir. There are no faults in the NRU. Compart-

mentalization is entirely stratigraphic. It is the result of areal

variations in the distribution of individual rock types.

Specific log shapes are not unique to each rock type. Thus flow

units cannot be chosen and traced through clusters of wells in the

NRU using a log signature. This is a common problem in most

SSC reservoirs, worldwide. Hence the need to use rock type dis-

tribution to determine reservoir quality and to assist in the defini-

tion of flow unit continuity.

It is obvious that uniform in-fill drilling is neither prudent nor

warranted due to the stratigraphic compartmentalization and ir-

regular permeability distribution of this reservoir. In-fill drilling

should be restricted to areas of the field where rock types 1 and 2 are dominant and

Fig. 5–Core porosity and permeability for dolostone rock types „RT 1 through 4…, NRU.

TABLE 2– POROSITY, PERMEABILITY AND LITHOLOGY BY ROCK TYPE, NRU

Rock Type

Median Porosity

(%)

Median Permeability

(md) Lithology

1 5.0 1.5 Dolostone2 5.6 0.2 Dolostone3 4.5 0.08 Dolostone4 7.5 0.02 Dolostone5* 5.8 0.40 Limestone6 1.0 0.01 Dolostone (anhydritic)7 2.3 0.01 Siltstone, dolomitic8 ¯ ¯ Shale and argillaceous dolostone

*Structurally low and water-bearing in the NRU.



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to areas that have good permeability and hydrocarbon pore vol-

ume HPVH characteristics, high primary and secondary recov-

ery, and areas of poor reservoir continuity with acceptable porosity

and permeability values, significant abundance of rock types 1, 2

or 3, and good primary but poor secondary recovery.

Application and Results. Comparison of the geological modelwith historical production performance data for the NRU reveals

that the producing characteristics of individual wells are a direct

function of local rock type distribution. The reservoir depletes and

re-pressures as a function of rock type throughout all areas of the

Unit.16 Therefore rock type distribution and rock type thickness

per flow unit are important variables that allow us to understand

and to predict reservoir behavior on well-by-well and field-wide

scales.

Maps of historical production characteristics contacted oil in

place, estimated ultimate recovery and reservoir pressure were

compared to maps of rock type distribution, permeability thick-

ness, and hydrocarbon pore volume to identify areas of the Unit

for in-fill drilling. Specific areas targeted for new in-fill wells in

the NRU were areas of the field with a significant thickness of undrained rock type 3, characterized by relatively low porosity

and low permeability.

Eighteen new wells 14 producers and 4 injection wells were

drilled in 1996 on the basis of this integrated geological-

engineering work. These are 10 acre in-fill wells. Initial produc-

tion and average production of each well are higher than the his-

toric well average for the field. Field-wide production has

increased by 25% with an increase of only 7% in the total number

of wells in the field.

High Porosity, Slope/Basin Clastic Reservoir

Background. The SBC reservoir study concentrates on the Tar

Zone of Fault Block IIA in Wilmington Field, California Fig. 8.Wilmington Field was discovered in 1936. It is the third largest oil

field in the U.S. based on total reserves. Approximately 2.4 BBO

have been produced to date from an OOIP of 8.8 BBO. In the Tar

Zone, the oil has a gravity of 14°API and a viscosity of 360 cp

and Fault Block IIA is on steamflood. The production history is

summarized in Table 3. Fault Block IIA is developed using a

7-spot pattern with a well spacing of 7.5 acres and currently has

39 injection and 57 production wells. Steam is supplied at the rate

of 395 mmBtu/hr, 1250 psig at 80% steam quality 25,500 bbl/d

cold water equivalent. Reservoir pressures are maintained at 700

to 900 psi to prevent surface subsidence. Temperatures in the

steam chest reach 500 to 540°F.

Depositional/Diagenetic Model. The Tar Zone produces oil fromtwo, unconsolidated, fine grained, lithologically complex arkosic

sands in the Pliocene Repetto Formation T and D Sand Intervals,

Fig. 9. The sands in these Intervals were deposited in heteroge-

neous, turbidite reservoirs. Internal reservoir compartmentaliza-

tion is common both vertical and areal due to the deposition of

Fig. 6–Differentiating potential pay from non-pay reservoir

rock, NRU. Fig. 7– Differentiating between pay rock types, NRU.

Fig. 8 –Location map, Wilmington Field.

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individual sand beds by successive turbidites. Individual sand

beds range in thickness from 1 to 20 ft, with most beds being less

than 10 ft thick. Very thick 3 ft sand beds are the result of

amalgamation of the deposits of successive turbidites. Individual

sand beds experienced different transport histories, i.e., different

hydrodynamic regimes established during deposition. Changes in

the hydrodynamic regime, such as changes in flow characteristics

turbulent or laminar, flow velocities, and internal sediment con-

centration, significantly influence the vertical and lateral distribu-

tion of important rock parameters, such as grain size, sorting and

grain composition—all of which act as significant, fundamental

controls of the permeability.

Sandstones of the Tar Zone have values of porosity that range

between 30% and 40% and values of permeability that range from400 to 8,000 md, with a weighted average of 1000 md. Formation

evaluation is complicated by the fact that the permeability of po-

tentially productive sand intervals ranges over several orders of

magnitude for any value of porosity Fig. 10. Log-based forma-

tion evaluation is complicated by the fact that stratigraphically

equivalent intervals in different wells can have the same porosity

but significantly different values of permeability.

Porosity/Permeability Relationship. It is generally recognized

that the relationship between porosity and permeability is

asymptotic when plotted arithmetically. For values of porosity

between 0% to 5%, the rate of permeability increase is low the

least squares line has a low slope. For porosity values between

5 and 25%, the rate of permeability increase is relatively highthe least squares line has a high slope. Above 25% porosity,

the rate of permeability increase is low the least squares line has

a low slope.

Routine core analysis data from the Tar Zone reveal that all

rock samples have high values of porosity generally 25%, Fig.

10. Image analysis of porosity in the scanning electron micro-

scope confirms the core measured values for rock samples. This is

also confirmed by log analysis. The relationship between porosity

and permeability reveals the following. Between values of 25% and 40% porosity, values of perme-

ability increase slowly as predicted from general theory. The basic relationship exhibits a considerable degree of scat-

ter more than three orders of magnitude variation in permeability

for a given value of porosity, Fig. 10.

Five rock types have been quantitatively identified in the TarZone on the basis of a combination of lithology from macro-

scopic core analysis and image analysis of pore body and pore

throat size Table 4. Rock types 1, 2 and 3 shale-free, arkosic

sandstones, high quality reservoir rocks are differentiated solely

on the basis of measured pore geometrical characteristics size of

pore bodies and pore throats. There is no compositional or grain

size difference among each of these rock types. Rock types 4 and

5 are differentiated lithologically, specifically by using the volume

of shale rock type 4, V shale 5% to 40%: Rock Type 5, V shale

40%.

The wide dispersion of porosity/permeability data Fig. 10 re-

flects changes in the distribution of pore types pores with bodies

and throats of varying size

within the Tar Zone. Virtually allpores 95% in the sandstones are of primary intergranular ori-

gin. The coordination number number of pore throats per pore is

uniform for all pore types 6. The difference in the pore types

is the pore body size and the size of the pore throats that inter-

connect the adjacent pores Table 4. Pore body and pore throat

size are fundamentally controlled by sorting range of grain sizes

of the sand grains.

Permeability varies largely as a function of rock type in the Tar

Zone. Intervals with identical values of porosity have significantly

different values of permeability. While there is some degree of

overlap between rock types 1 and 2, it can be seen that porosity

and permeability are closely related within each rock type Fig.

10. This confirms the early work of Calhoun who pointed out that

there is a close relationship between porosity and permeabilitywithin rocks with a specified pore geometry.12

Algorithms have been developed that relate porosity to perme-

ability for the four sandstone rock types with routine core analysis

Fig. 9–Characteristic log response profiles and

vertical distribution of rock types, Well

UP901B, Wilmington Field.

TABLE 3– SUMMARY OF PRODUCTION HISTORY,

FAULT BLOCK IIA, TAR ZONE

Production Mode Time Period

Oil Recovery

(mbbls)

Recovery Factor

(%)

Primary 1937–1960 15,201 15.4

Waterflood 1960–1982 8,299 8.4

Steamflood 1982–1/1/96 8,422 8.4

Total 1937–1/1/96 31,922 32.2

156 Davies, Vessell, and Auman: Reservoir Behavior SPE Re se rvoir Eval. & Eng., Vol. 2, No. 2, Apri l 1999


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measurements rock types 1 through 4, Table 5. Porosity/ permeability algorithms for values of porosity 25% are based on

the measured core data. No data exist for low porosity rock in this

area. Simple linear extrapolation of these algorithms to values of

low porosity results in calculation of excessively high values of

permeability. This is obviously incorrect. Thus, we have extrapo-

lated the porosity/permeability relationship for each rock type

from values of 25% porosity though an intercept at 0% porosity

and 0.1 md permeability.

No petrophysical measurements exist for rock type 5 shale.

The permeability has been estimated as 0.01 md based on mea-

surement of pore throat size from direct scanning electron micro-

scope analysis.

Rock Type Identification. Individual rock types can be identifiedusing specific ‘‘cut-off’’ values based on analysis of environmen-

tally corrected and normalized well log responses and using the

comparison of the core-based determination of rock type. Rock

types 1 through 4 are identified using a cross plot of apparent

grain density versus the logarithm of the absolute value of the

separation between the resistivity of the flushed zone Rxo and

the resistivity of the uninvaded zone Rt Figs. 11 and 12. Rock

type 5 shale is identified using the gamma ray log 37 API°

units of gamma rayshale lithology based on the macroscopic

core description. Rock types can thereby be identified, foot-by-

foot, in all wells with a sufficient logging suite.

Fig. 10–Core porosity and permeability by rock type, Tar zone, Wilmington Field.



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Shale Volume Calculation. Log shale indicators have been cali-

brated to actual values of measured shale from petrographic

analysis; see the log track labeled ‘‘Rock Calibrated’’ Fig. 9.

This is a very important analytical procedure in the petrophysical

interpretation of these sands and any sand with complex

mineralogy/lithology because wireline logs are affected by non-

shale components: radioactive sand grains such as orthoclase feld-

spar, mica and metamorphic rock fragments; heavy minerals such

as siderite, pyrite; and grains with high hydrogen content, such as

altered metamorphic and igneous rock fragments. Traditional

techniques of shale volume calculation using gamma ray, neutron-

density separation or apparent matrix density Rhomaa incor-

rectly calculate these structural framework components as shale.

One of the biggest problems in the Tar Zone is that traditional

log interpretation techniques yield an average shale volume V

shale of 17% in the productive sandstones see log track headed

‘‘Scaled’’ in Fig. 9. This is a significant error because the clean

sands rock types 1, 2 and 3 contain 1% V shale, based on

direct measurement of rock samples. Production experience dur-

ing waterflood and steamflood operations reveals no shale-related

problems in this field.

For this study we have calibrated all wireline log shale indica-

tors to the results of petrographic analysis in cored wells. Theseindicators include the gamma ray, Rhomaa, PHIN and neutron-

density separation. A composite algorithm is developed for log-

based, shale volume determination track labeled ‘‘Rock Cali-

brated,’’ Fig. 9. The shale volume correction algorithm,

developed in the cored wells, is applied to all wells in the field

because the non-shale, radioactive sand grains occur throughout

the reservoir interval. In addition, we have corrected for thin bed

effects using macroscopic core descriptions and the logarithm of

the absolute values of separation between Rxo and Rt versus frac-

tional neutron porosity Fig. 13.

Permeability Prediction. Log derived values of porosity are de-

rived using shale-corrected neutron and density porosity values

with appropriate corrections for the zones that have been steamed.

In a clean, non-steamed sand, the density and neutron curves

stack. The presence of steam in the near well bore region signifi-

cantly affects the neutron response. The neutron porosity log in

this field reads several porosity units lower than the density log

where there is steam. The effect on the density log is negligible.

Steam corrections have been made through reconstruction of the

neutron porosity curve by regressing RHoB and PHIN data.

As was shown earlier Fig. 10, permeability is a function of

porosity and rock type. Since rock type and porosity can be de-

termined from well log response, permeability can be predicted

using well log responses only. This enables a vertical layering

profile based on rock type and permeability in cored and non-

cored wells to be developed Fig. 9.

Hydraulic Flow Units. Individual hydraulic flow units are iden-tified based on rock type distribution each rock type represents a

different flow unit. Evaluation of these data reveals that the rock

types are not randomly distributed. The principal rock types rock

types 1 and 2 occur in close association and alternate with one

another. Lower quality rocks rock types 3, 4 and 5 tend to occur

together and alternate with one another. There is a direct relation-

ship between rock type and potential producibility. The highest

quality sand in Wilmington Field D sand, Fig. 8 consists pre-

dominantly of rock type 1 with a lesser net footage of rock type 2.

Fig. 11 –Rock type identification plot: Discrimination of rock

types 1 and 2 from 3 and 4.

TABLE 4– ROCK TYPE CHARACTERISTICS, TAR ZONE

Rock Type

Median Porosity

(%)

Median Permeability

(md) Lithology

Pore Diameter

( m)

Pore Throat Radius

( m)

1 32 2000 Clean sandstone* 50–150 5–10

2 33 1100 Clean sandstone* 20–50 2–5

3 35 300 Clean sandstone* 10–20 2

4 33 7 Shaly siltstone/

sandstone**5 1

5 ¯ ¯ Shale*** ¯ ¯

*Less than 5% Vshale.**10% to 40% Vshale.

***More than 40% Vshale (based on petrographic analysis).

TABLE 5– RELATIONSHIP OF POROSITY TO

PERMEABILITY, TAR ZONE

Rock

Type Porosity-Permeabil ity Algorithm

1 If porosity0.25

then K 10∧

(1.100* porosity)2.940

If porosity0.25

then K 10∧

(16.8* porosity)1 2 If porosity0.25

then K 10∧

(2.2474* porosity)2.227

If porosity0.25

then K 10∧


then K 10∧

(1.697* porosity)1.840

If porosity0.25

then K 10∧


then K 10∧

(0.746* porosity)0.526

If porosity0.25

then K 10∧

(6.8* porosity)1

5 If rock type 5 then

K 0.01



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The lower quality, highly compartmentalized T sand consists pre-

dominantly of rock type 2 with a significant net footage of rock

types 3, 4 and 5.

The net footage of each rock type is determined for each well

and each zone. This allows rapid, computer based mapping of the

distribution of each rock type throughout the Field.

In Wilmington Field, the relative rate of fluid recovery was

correctly predicted for two DOE-sponsored field development ar-

eas an in-fill area and a step-out area using the permeability

model developed here. Thus permeability modeling is of value inthe planning of field development programs in unconsolidated

rocks with high values of porosity.

Conclusions

1. Measurement of pore geometrical parameters allows an im-

proved prediction of permeability and permeability distribution

from wireline logs in partially cored intervals, and in adjacent

uncored intervals and adjacent uncored wells. It improves the pre-

diction of reservoir quality in non-cored intervals for improved

completions and for EOR decisions.

2. Detailed pore geometrical attributes allow a definition of

hydraulic flow units to be made. These attributes can be related to

log response, thus allowing the development of a field-wide, log-

based reservoir model.3. Existing logs and cores can be used to develop a pore

geometry-based, predictive model of permeability and well behav-

ior for in-fill and step-out wells. This allows optimum planning of

field development projects.

4. Uniform well spacing patterns in heterogeneous reservoirs

are not prudent because of the existence of significant areal varia-

tions in permeability. In-fill drill patterns should be based on the

distribution of kH and HPVH.

5. The reservoir characterization methodology used in this

study can be used in reservoirs of widely differing lithologies and

quality. It allows identification of areas of the reservoir character-

ized by i high values of porosity, permeability, and HPVH, ii

thick sequences of potentially productive rock, and iii compart-

mentalization.

6. The technique uses existing data and can eliminate the need

for ‘‘evaluation’’ wells. In some reservoirs it can reduce the num-

ber of required well tests, thereby minimizing the loss of produc-

tion that occurs when wells are shut in for testing purposes. These

DOE-sponsored studies reveal that comprehensive analysis, inter-

pretation and prediction of well and field performance can be

completed quickly on the order of weeks or months for complex

fields with large numbers of wells, at minimal cost.

Nomenclature

214 constant Ref. 13

d diameter of pore throat m

cp centipoise

Acknowledgments

We gratefully acknowledge the financial support of the US De-

partment of Energy, Class II and Class III Oil Programs, Fina Oil

and Chemical Company and Tidelands Oil Production Company.

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Fig. 12–Discrimination of rock types 3 and 4.

Fig. 13–Lithology determination „ numbersV shale%….



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