Making sense of carbonate pore systems

AUTHOR

Arve Lønøy � Norsk Hydro Oil & Energy,Sandsliveien 90, N-5049 Sandsli, Bergen,Norway; [email protected]

Arve Lønøy received a Cand. Real. degree incarbonate sedimentology from the Universityof Bergen in 1981. He has since then workedas a research geologist on carbonate sedimen-tology and, to a lesser extent, siliciclastic petro-graphy in the Norsk Hydro Research Center.His main work on carbonates has been on Pa-leozoic carbonates of the Arctic (Norway,Canada, and Russia) and the Mesozoic of west-ern Africa and the Middle East.

ACKNOWLEDGEMENTS

I am grateful to my colleagues David Hunt,Ole Martinsen, and Brian A. Farrelly for theirreview of an early version of the manuscriptand to Norsk Hydro for permission to pub-lish the article. I also thank David Eby, JerryLucia, and Wayne M. Ahr for their helpfulreviews and comments. I am particularly in-debted to William A. Morgan, who gave con-structive criticism and suggestions that sig -nificantly improved the article.

Making sense ofcarbonate pore systemsArve Lønøy

ABSTRACT

Themost widely used pore-type classification systems for carbonate

reservoirs are limited by the fact that the relation between porosity

and permeability is poorly defined. Existing classification schemes

for porosity-permeability data do not, in many cases, optimally

integrate sedimentology, diagenesis, and flow-related properties. In

many carbonate reservoirs, it is therefore difficult to generate

predictive models for reservoir-quality distribution, resulting in

significant uncertainty in hydrocarbon reserve calculations.

Based on empirical data, mostly from Europe and the Middle

East, a new pore-type classification system has been developed. The

new system not only uses elements from existing pore-type clas-

sification systems, but also introduces many new elements. The

new pore-type system includes 20 pore-type classes that show a

predictable relation between porosity and permeability. It com-

bines sedimentologic and diagenetic features with flow-related

properties, and reservoir-critical parameters can thus be predicted

using sedimentologic and diagenetic models.

A practical example based on data from a Devonian hydrocar-

bon field shows that pore-type variations may account for several-

hundred-percent differences in calculated hydrocarbon reserves.

INTRODUCTION

The porosity classification system most widely used by carbonate

petrographers and petroleum geologists is the one developed by

Choquette and Pray (1970). This system is closely linked to sedi-

mentological fabric, and pore types can therefore, to some extent,

be predicted based on depositional setting and/or diagenetic evo-

lution. This classification system is particularly useful for porosity

evolution studies that are important for exploration. Among

petrophysicists and reservoir engineers, the classification of Archie

(1952) and, to a greater extent, Lucia (1983, 1995, 1999) is pre-

ferred because the pore classification is more directly linked to pore

geometries and flow properties.

AAPG Bulletin, v. 90, no. 9 (September 2006), pp. 1381– 1405 1381

Copyright #2006. The American Association of Petroleum Geologists. All rights reserved.

Manuscript received July 5, 2005; provisional acceptance September 12, 2005; revised manuscriptreceived January 4, 2006; final acceptance March 13, 2006.

DOI:10.1306/03130605104

A major challenge in the evaluation of carbonate

reservoirs is to understand the relationship between

pore type and porosity and permeability. Such infor-

mation, commonly derived from routine thin-section

description and core-plug analysis, together with wire-

line-log data, is a basic constraint of both stock-tank-

original-oil-in-place (STOOIP) calculations anddynamic

simulations of hydrocarbon reservoirs. Lucia (1983,

1995, 1999) made a major contribution to understand-

ing porosity-permeability relationships in carbonates.

He showed how incorporating textural and particle

size information helps to better understand porosity-

permeability relationships in carbonates. The impor-

tance of this work is highlighted by its widespread use

in the hydrocarbon industry.

The aim of this article is to present the results of a

study that set out to analyze porosity and permeability

relationships in carbonate reservoirs from a wide range

of sedimentary basins, geological ages, and burial depths.

As part of this study, core-plug thin sections were de-

scribed using the semiquantitative classification of Lucia

(1999) and the descriptive terminology of Choquette

and Pray (1970). These data show a wide range of

coefficient of determination values for permeability

and porosity (R2 in the range of 0.45–0.88), and it is

apparent that some pore types are, in terms of porosity-

permeability relationships, poorly classified in com-

parison to others (Table 1). This realization prompted

the search for an alternative carbonate pore-system

classification that would provide a higher degree of cor-

relation between porosity and permeability.

The new pore-type classification presented here

incorporates elements of both rock texture and pore

size, reflecting depositional and diagenetic fabrics. As

such, it builds from the important work of Choquette

and Pray (1970) and Lucia (1983, 1995, 1999). How-

ever, the new classification provides a significant im-

provement over existing schemes in terms of a better

correlation of matrix-related porosity and permeabili-

ty, hence, for porosity cutoff values and permeability

prediction. The latter is one of the most important

elements of carbonate reservoir description.

This paper first discusses the data types and meth-

ods employed followed by an illustrated description of

the different pore classes. Then, a consideration of the

observed porosity-permeability relationships for each

pore class and a comparison to other pore classifications

follow. A case study from a Devonian reservoir using

the new classification scheme is used to illustrate its

potential impact on reserve estimations.

DEVELOPMENT OF THE PORECLASSIFICATION SCHEME

The impetus for developing a new pore-classification

scheme resulted from classification of the carbonate

database inNorskHydro using thewidely applied Lucia

(1983, 1995, 1999) pore classification. However, a wide

range and scatter of data in each of the pore class fields

were apparent, with different pore classes showing very

different coefficients of determination (R2) for porosity

and permeability (Table 1). This observation prompted

the analysis of the data using the Choquette and Pray

(1970) pore classification system. Although there is a

wide range of R2 using the Choquette and Pray classifi-

cation (Table 1), this analysis led to the realization that

there is a significant difference in permeability trends

between intercrystalline and interparticle porosity, two

pore types that are grouped together in the Lucia classifi-

cation. One striking feature in the data set was the patchy

distribution of porosity in a high proportion of the studied

thin sections (i.e., >30%of the interparticle and intercrys-

talline pore-type samples). This observation prompted

a first attempt at subdividing the porosity scheme of

Choquette and Pray (1970) into uniform and patchy

subclasses on the basis of both thin-section and plug

description. This approach revealed natural pore-type

subclasses that allow for a refined relationship between

porosity and permeability and increased R2 values.

Table 1. Porosity-Permeability Coefficients of Determination

(R2) for the Lucia (1983, 1995, 1999) and Choquette and Pray

(1970) Classification Systems*

Pore Type R2

Lucia (1983, 1995, 1999) Classification SystemInterparticle, class 3 0.68

Interparticle, class 2 0.62

Interparticle, class 1 0.79

Vuggy, separate 0.86

Vuggy, touching 0.45

Choquette and Pray (1970) Classification SystemInterparticle 0.70

Intercrystalline 0.50

Moldic 0.88

Intraparticle 0.86

Vuggy 0.50

*Using samples from the present study.

1382 Making Sense of Carbonate Pore Systems

The final step in the development of the new classi-

fication scheme was to incorporate the observation of

Lucia (1983) that pore size is a primary factor in un-

derstanding porosity-permeability relationships.Whereas

Lucia recognized the control of pore size on porosity-

permeability relationships, his classification schemeuses

grain size (of particles or crystals) instead of pore size as

the primary means of pore class division (i.e., his inter-

particle classes 1, 2, and3). In this study, direct description

of pore size was used (instead of particle or crystal size).

This is in part because of the range of sorting observed

inmany samples thatmade classification of particle size

problematic and partly because later cements common-

ly act to occlude pore space,making the relation of pore

size to grain size indirect. The result of incorporating

pore size into the systemwas a further increase in theR2.

During the evolution of the pore-type classification

described herein, subdivision of the classes was made

until a high coefficient of correlation was realized. This

was a primary goal behind the project, instead of pro-

ducing a genetic scheme that attempts to relate classes

to depositional and diagenetic processes (e.g., Ahr and

Hammel, 1999; Ahr et al., 2005). Given the above dis-

cussion, it follows that the pore systemproposed here is

texturallyderivedmostly fromChoquette andPray (1970)

and incorporates the pore-size differentiation that is

underpinned by Lucia’s (1983) work. Important modi-

fications include the addition of uniform and patchy

porosity distribution and the incorporation of mud-

stone microporosity made to achieve high R2 values.

Mathematical analysis of the different pore classes re-

veals that some are described by linear relationships,

whereas others show polynomial and logarithmic best-

fit trends with a relatively high coefficient of correla-

tion (Tables 1, 2). This is in contrast to the logarithmic

boundaries to pore class fields described byLucia (1983).

SAMPLES AND METHODS

Approximately 3000 plug samples from a wide range

of sedimentary basins and geological ages are included

in the database of this study, including (in order of

numeric representation in the database)

� Maastrichtian and Danian well data, North Sea� Upper Devonian and Lower Permian well data,

Timan-Pechora Basin, Russia

Table 2. New Porosity Classification System*

Pore Type Pore Size Pore Distribution Pore Fabric R2

Interparticle Micropores (10–50 mm) Uniform Interparticle, uniform micropores 0.88

Patchy Interparticle, patchy micropores 0.79

Mesopores (50–100 mm) Uniform Interparticle, uniform mesopores 0.86

Patchy Interparticle, patchy mesopores 0.85

Macropores (>100 mm) Uniform Interparticle, uniform macropores 0.88

Patchy Interparticle, patchy macropores 0.87

Intercrystalline Micropores (10–20 mm) Uniform Intercrystalline, uniform micropores 0.92

Patchy Intercrystalline, patchy micropores 0.79

Mesopores (20–60 mm) Uniform Intercrystalline, uniform mesopores 0.94

Patchy Intercrystalline, patchy mesopores 0.92

Macropores (>60 mm) Uniform Intercrystalline, uniform macropores 0.80

Patchy Intercrystalline, patchy macropores

Intraparticle Intraparticle 0.86

Moldic Micropores (<10–20 mm) Moldic micropores 0.86

Macropores (>20–30 mm) Moldic macropores 0.90

Vuggy Vuggy 0.50

Mudstone microporosity Micropores (<10 mm) Tertiary chalk 0.80

Cretaceous chalk 0.81

Uniform Chalky micropores, uniform 0.96

Patchy Chalky micropores, patchy

*Partly based on Choquette and Pray (1970) and Lucia (1983, 1995, 1999). Porosity-permeability coefficients of determination (R 2) are based on samples from thepresent study.

Lønøy 1383

� Upper Carboniferous and Lower Permian well data,

Barents Sea� Cretaceous outcrops and well data, western Iran� Tertiary well data, southeastern Asia� Eocene well data, Libya� Paleocene and Miocene outcrops, Majella, Italy� Miocene outcrops, Almeria region, southeastern

Spain� Cretaceous well data, offshore Namibia

Most plugs have a diameter of 1 in. (2.5 cm), but

some 1.5-in. (12.7-cm)-diameter plugs (especially from

vuggy pore systems) are also included. Only plugs that

fulfill all of the following selection criteria are in-

cluded in the database: (1) thin sections are available

from the plugs; (2) each plug has a dominance of only

one pore type; (3) plugs are unfractured; and (4) po-

rosity and permeability measurements are available for

the plug.

The first selection criterion ensures the accurate

definition of pore types, thereby making it possible to

directly relate pore type to measured porosity and

permeability. Selection criteria 2 and 3 strongly reduce

(but do not eliminate) the risk of reservoir parameters

being the result of pore-type mixing.

Thin-section porosity was impregnated with blue-

dyed epoxy for visualization of pore types. Helium

porosity and air permeability (Klinkenberg corrected)

were measured on horizontal and a few vertical plugs.

The definition of pore-size categories (micro-,

meso-, and macropores) was initially based on visual

comparison and grouping of thin sections and later

quantified from selected samples using both two- and

three-dimensional (2-Dand3-D) techniques.Thediam-

eter of the volumetrically most common pore sizes

was measured from thin sections (2-D) using a petro-

graphic microscope. Three-dimensional pore-size dis-

tributions were estimated from core plugs using scan-

ning electron microscopy image-processing techniques

on slices cut at 10-mm increments. Interpolation was

applied between the slices.

Porosity distribution (uniform and patchy) was

visually defined from both thin sections and core plugs.

A thin section represents only a thin slice through a

plug, and some thin sections therefore show uniform

pore distribution in larger scale patchy pore networks.

Porosity and permeability were measured on the entire

plug, and it is the pore distribution in the plug instead

of the thin section that is important in relating pore

types to reservoir properties. With few exceptions, the

patchy porosity distribution was on a scale where it

could be clearly defined by naked-eye observation of

plugs, rock slabs, or thin section. Therefore, porosity

should be classified as having a patchy distribution only

when the patchy distribution can be seen without

magnification.

The absence of fractures was confirmed by thin-

section observations and, in most cases, by visual ex-

amination of core plugs.

A REVISED PORE-TYPECLASSIFICATION SYSTEM

As discussed, the new pore-type classification system

developed here is based on the Choquette and Pray

(1970) classification system, but incorporates elements

from the Lucia (1983, 1995, 1999) system. However,

the new system also introduces new elements that are

critical for predicting reservoir properties.

The main differences between the new carbonate

pore system and those of Choquette and Pray (1970)

and Lucia (1983, 1995, 1999) are as follows:

� Porosity distribution is a major new element in the

classification.� Lucia’s subdivision of interparticle porosity has

been partly incorporated into the new classification

system, but is now based on pore size instead of

grain size and sorting.� Lucia’s three interparticle pore-type classes andCho-

quette and Pray’s interparticle and intercrystalline

porosity types have been subdivided into 12 new

classes (6 interparticle and 6 intercrystalline).� Micromoldic andmacromoldicpores aredifferentiated.� A new pore-type category, consisting of four pore

types, is introduced: mudstone microporosity.

Porosity-permeability crossplots for data sets used

in this study yield significantly higher coefficients of

determination (R2) with the new classification system

than with the classification systems of Choquette and

Pray (1970) andLucia (1983, 1995, 1999) (Tables 1, 2).

The new classification system combines sedimentologic

and diagenetic features with flow-related properties,

and reservoir-critical parameters can thus be predicted

using sedimentologic and diagenetic models.

Thenewclassification system is based on threemain

elements: pore type, pore size, and pore distribution.

Age is an important factor for some of the mudstone

micropore classes.


Pore Type

Six main pore types are identified: interparticle,

intercrystalline, vuggy, intraparticle, moldic, and mud-

stone microporosity (Table 2). The first five pore types

are almost identical to those defined by Choquette and

Pray (1970), whereas the last one is new.

Ten additional pore types defined by Choquette

and Pray are valid pore types to consider. However, be-

cause of insufficient data for fenestral, shelter, boring,

burrow, and shrinkage porosity and/or analytical con-

siderations for fracture, channel, cavern, growth frame-

work, and breccia porosity, these pore types were not

included in the new system. Insufficient data probably

suggest that pore types are of minor significance in

reservoirs examined to date. Analytical considerations

aremostly related to high pore/plug size ratio, i.e., plug

sizes are too small to adequately represent the pore

system.

Pore Size

Lucia (1983, 1995, 1999) realized that pore-size

distribution controls permeability and is related to

rock fabric. Therefore, he used average particle size and

sorting to differentiate between different interparticle

pore-type classes. The term ‘‘particle’’ was used as a

general term for grains (multicrystalline particles) and

crystals (single-crystal particles) (Lucia, 1983).

The studied data set shows a relatively good re-

lation between intercrystalline pore size and crystal

size. However, the relation between intergrain pore

size and particle size is sometimes poor, which, as

pointed out by Lucia (1995, 1999), is partly because of

poor sorting. Pore sizes in packstones may be con-

trolled by allochem size and interparticle mud, result-

ing in a wide range of pore sizes. At the same average

particle size, pore size is sometimes controlled by in-

terparticle mud (pores between mud particles) and, in

other cases, by the larger grains (pores between allo-

chems). Average particle size is also difficult to define

because it may be defined by the volume or by the

number of grains.

Lucia (1995, 1999) discussed sorting in relation to

mud infilling of interparticle pore space by differenti-

ating between grainstone, mud-lean packstones (grain-

dominatedpackstones), andmud-richpackstones (mud-

dominated packstones). However, this classification

system does not consider the important effect allochem

sorting has on pore size. Moderately to poorly sorted

grainstones andmud-lean packstones have awide range

of grain sizes even within the same grain-size class,

resulting in closer packing of grains and smaller pore

sizes.

Another important effect on pore size that has not

been covered by the Lucia (1983, 1995, 1999) classifi-

cation system is cement. Both particle size and sorting

are unaffected by interparticle cement, but the cement

will reduce pore size and pore throats. Samples with

similar particle size and sorting thus may have a sig-

nificant spread in pore size and permeability-porosity

relationship, depending on the cement volume andmor-

phology. The foundation of Lucia’s (1983, 1995, 1999)

classification system, that pore-size distribution controls

permeability and is related to rock fabric, is therefore

missing an important element.

Based on these considerations, pore size instead

of particle size has been applied in the new pore-type

classification system. Pore-size differentiation provid-

ed a better coefficient of determination (R2) in the

porosity-permeability crossplots than particle-size

differentiation.

Pore-size differentiation was performed by visual

examination of thin sections and has been applied to

interparticle, intercrystalline, and moldic pore types

(Table 2). Interparticle and intercrystalline pores have

been visually subdivided into micro-, meso-, and

macropores, whereas moldic pores have been subdi-

vided into micro- and macropores. Pore sizes of the

different classes vary with pore type (Table 2) and are

not based on other published definitions.

Interparticle pore diameters and size distributions

were measured on the reference samples shown in

Figure 1a–c. Micropores have a dominant pore diam-

eter in the 10–60-mm range (70% of the pores). Meso-

pores have a dominant pore diameter of 40–100 mm,

although approximately 30% of the pores are in the

100–300-mm range. Macropores are generally larger

than 100 mm in diameter (approximately 75% of the

pores). Interparticle pore-size groups may thus be de-

fined by 10–50 (micropores), 50–100 (mesopores),

and greater than 100 mm (macropores). Porosity with

dominant pore diameters less than 10 mm is classified as

mudstone microporosity.

Intercrystalline micropores are commonly 10–

20 mm indiameter,whereasmesopore diametersmostly

are in the 20–60-mm range. Intercrystalline macro-

pores have diameters larger than 60 mm.

Moldic micropore diameters are typically less than

10–20 mm, although they occasionally can be larger.

Moldic macropores are larger than 20–30 mm.

Lønøy 1385

Pore Distribution

Porosity distribution is a new element in pore-type

classification and has a significant effect on porosity-

permeability relationships (also noted by Lucia et al.,

2004a, b). The distribution of interparticle pores, inter-

crystalline pores, and mudstone micropores has been

visually classified as either uniform or patchy.

At similar porosities, a patchy porosity distribution

is observed to yield significantly higher permeability

than uniform porosity distribution. The reason for this

is that the porosity is concentrated over a smaller vol-

ume and the pore system is better connected than for an

equivalent, uniformly distributed pore volume. Further-

more, a patchy porosity distribution is often related

to secondary dissolution with slight corrosion of pore

throats, and this process also tends to favor connected

pores.

The effect of patchy porosity on porosity-perme-

ability relationships may be illustrated by a porous,

sucrosic dolomite with anhydrite nodules. Core plugs

from this dolomite may show uniform porosity distri-

bution or varying degrees of patchiness, depending on

the location of the plugs. Plugs with uniform porosity

Figure 1. Thin-section micro-graphs showing samples with apredominance of interparticleporosity, some of which aresolution enlarged. Minor intra-particle and moldic pores arepresent, but interparticle porespredominate: (a) microporosity(10–50 mm pore diameter) withuniform porosity distribution,f = 17.6%, k = 0.84 md;(b) mesoporosity (50–100 mmpore diameter) with uniformporosity distribution, f = 19.3%,k = 9.47 md; (c) macroporosity(>100 mm pore diameter) withuniform porosity distribution,f = 15.3%, k = 132 md; and(d) macroporosity (>100 mmporediameter) with uniform po-rosity distribution and pore-lining calcite cement, f = 9.7%,k = 0.465 md. All photomicro-graphs were taken under plane-polarized light.


distribution (no anhydrite) will have higher porosity

than plugs with patchy porosity distribution (with an-

hydrite). The effect on permeability, however, is minor

because pore-throat diameters are the same (indepen-

dent of anhydrite). The only effect on permeabilitymay

be a slight increase in tortuosity. The permeability is

thus nearly uniform in both examples, whereas the

porosity is reduced in the anhydritic sample.

Obviously, there may be various degrees of po-

rosity patchiness, and ideally, there would be a relation

between patchiness and porosity-permeability. Patch-

iness could probably be measured in some way from

thin sections. However, it is the patchiness in the plug

and not the thin section that controls the porosity-

permeability relationship. In plugs with only two min-

eral phases, where grains and patchily distributed ce-

menthave significantly different grain densities, the grain

density could be used as a measure of patchiness. An

attempt to use grain density as a measure of patchiness

in this study was not successful because very few sam-

ples had the appropriate mineral combinations.

Controls on porosity distribution vary for different

pore types and are therefore discussed under the re-

spective pore-type description sections.

Figure 1. Continued.

Lønøy 1387

PORE-TYPE DEFINITIONS AND ORIGINS

Interparticle Porosity

Choquette and Pray (1970) defined interparticle po-

rosity as porosity occurring between grains (intergrain).

Lucia (1983) extended the term ‘‘interparticle’’ to also

include pore spaces between crystals (intercrystal). This

redefinition thus included both the interparticle and

intercrystalline porosity types of Choquette and Pray

(1970).However, the results of this study show that the

petrophysical properties of intergrain and intercrystal

pores are different. Choquette and Pray’s definition of

interparticle porosity has therefore been applied in the

new pore-type classification system presented in this

article. Solution-enlarged interparticle pores were

included in the definition when the pore’s interparticle

origin could be clearly defined. Interparticle pores are

commonly primary in origin, butmay also be formed by

secondary dissolution of cements or matrix.

To achieve a good porosity-permeability relation-

ship, six natural interparticle pore-type classes are appar-

ent, based on pore size and pore distribution (Table 2;

Figures 1, 2). Three pore-type classeswere defined based

onpore size (micro-,meso-, andmacropores; 10–50, 50–

100, and >100 mm, respectively), each being subdivided

on the basis of a uniform or patchy pore distribution.

In the studied data set, a patchy, interparticle pore

distribution is commonly related to a diagenetic over-

print. Diagenesis may include the selective dissolution

of interparticle matrix and cement, and patchy cemen-

tation of bothprimary and secondary interparticle pores.

Less commonly, a patchy pore distribution is primary in

origin and is related to very large grains in poorly or

bimodally sorted sediment (typically oncoid-rich sam-

ples in the studied data set).

In the studied data set, selective dissolution of

cements and matrix is strongly controlled by fluid-

migration pathways (e.g., fractures and permeable grain-

stones), commonly yielding a patchy pore distribution

in the early stages of dissolution. Patchy cement distri-

bution is commonly controlled by selective precipita-

tion of syntaxial calcite cement overgrowths on single-

crystal grains (typically echinoderm fragments), but

also occurs where noncarbonate minerals precipitate

within carbonate host sediments.

Interparticle pores are normally associated with

medium- to high-energy depositional settings in the stud-

ied data set, such as rimmed, platform-margin shoals,

distally steepened ramp-margin shoals, inner-ramp fring-

ing shoals, middle-ramp barrier shoals, local platform-

interior shoals, gravity-driven flow deposits, beaches,

wash-over fans, and others. Micropores and mesopores

occur within grain-supported textures composed of ex-

tremely small bioclastic fragments, where larger inter-

particle pores have been partially occluded by cement,

or within poorly sorted grainstones. Micropores also

occurwithin recrystallizedmudofmud-lean packstones.

Macropores are most common in moderately to well-

sorted, high-energy grainstones.

Intercrystalline Porosity

Intercrystalline porosity is the porosity between crys-

tals that may be of either primary or secondary origin

(Choquette and Pray, 1970). All intercrystalline pores

included in the studied data set are secondary in origin

and occur between crystals that have grown more or

less in place by calcite recrystallization or dolomitiza-

tion. Thus, only the pores of secondary origin are con-

sidered in the following discussion.

In grain-rich dolostones, porosity may be classified

as either intercrystalline or interparticle, depending on

the dominant control on pore structure. When deposi-

tional grains (allochems) are the dominant control, the

porosity is here classified as interparticle. Where pore

structures are controlled by dolomite crystals, they are

instead classified as intercrystalline. Based on these cri-

teria, dolograinstones in the studied data set commonly

(but not always) have interparticle porosity, whereas do-

lopackstones commonly have intercrystalline porosity.

Intercrystalline pores are described by six pore-

type classes based on pore size and pore distribution,

each with a distinct porosity-permeability relationship

(Table 2; Figure 3). Three pore-type classes are defined

based onpore size (micro-,meso-, andmacropores; 10–

20, 20–60, and >60 mm, respectively). These are, in

turn, subdivided into uniform or patchy pore distribu-

tion to optimize porosity-permeability best-fit trends.

Crystals are commonly of uniform size in a sample,

resulting in a typically good correlation between crystal

size and pore size. In some cases, notably within micro-

porous, argillaceous dolomites, the pore size is smaller

than expected from the dolomite crystal size alone be-

cause clays partially fill the porosity.

Patchy intercrystalline pore distribution is related

to patchy cementation and, less commonly, to the spa-

tial arrangement of crystals (i.e., texture). Themineral-

ogy of patchy cements and host rock is commonly dif-

ferent.Whenmineralogies are identical, the cements tend

to have more uniform distribution because more nucle-

ation points are available during cement precipitation.


The most common patchy cement mineralogies in the

studied data set are chert and gypsum-anhydrite and, less

commonly, calcite. The host rock is commonly dolomite.

Patchy intercrystalline cementation is commonly

controlled by depositional setting, but may also be con-

trolled by fluid-migration pathways and reprecipitation

of cements during later diagenesis. Restricted to hyper-

saline settings in an arid climate favor evaporite pre-

cipitation and patchy pore distribution in dolomites.

Patchy intercrystalline pore distribution related to

texture is, in the studied data set, commonly associated

with early diagenetic, finely crystalline dolomites.Many

of the dolomite crystals are arranged in millimeter- to

centimeter-size, relatively tight clusters, where each

cluster is composed of several dolomitized grains and

intergrainmud.Outside these clusters, dolomite grains

are commonly still tight, but there is high intercrystal-

line porosity in dolomitized mud between the grains.

Mudstone Microporosity

Mudstone micropores have extremely small pore sizes,

commonly a few micrometers in diameter. Individual

pores cannot be seen with a standard petrographic

Figure 2. Example of patchy interparticle mesoporosity (50–100 mm pore diameter) in Devonian rocks from Russia. Left: core slabshowing patchy pore distribution related to differential dissolution. Tight, calcite-cemented areas are gray; porous, oil-stained areasare brown. Right: thin-section photomicrographs showing patchy interparticle mesopores related to differential dissolution ofcements. Lower photomicrograph is a close-up showing patchy porosity at a larger scale. Note the abundance of rhombohedral poreoutlines, which are indicative of dissolution. Calcite cements are white. Both photomicrographs were taken under plane-polarizedlight.

Lønøy 1389

microscope. However, using blue-dyed epoxy, the pres-

ence of these pores can commonly be discerned by a

weak bluish stain to the thin section, especially when

the porosity is high (Figure 4). Mudstone microporos-

ity may be defined as interparticle or intercrystalline

porosity. However, because of the extremely small

pore sizes and variable pore structure (interparticle or

intercrystalline), these pores were classified as a sepa-

rate pore-type class.

Mudstone microporosity includes both true chalks

and chalky microporosity. Chalk micropores are pri-

mary in origin and occur between grains of planktonic

calcareous algae (coccospheres) or their component

crystal plates (coccoliths). Chalky micropores are not

related to chalk, but the pore structure is similar. These

pores occur between recrystallized mud particles and

may be formed either during early meteoric leaching

or deeper burial diagenesis (Pittman, 1971; Budd, 1989;

Moshier, 1989). The pores typically form in low-energy,

muddy, platform-interior facies.

Four natural classes of mudstone microporosity

exist in the database: (1) Tertiary chalk, (2) Cretaceous

Figure 3. Thin-section photo-micrographs showing examplesof intercrystalline porosity:(a) microporosity (10–20 mmpore diameter) with uniformporosity distribution, f = 18.1%,k = 0.476 md; (b) mesoporos-ity (20–60 mm pore diameter)with uniform porosity distribu-tion, f = 19.3%, k = 4.93 md;(c) macroporosity (>60 mmpore diameter) with uniformporosity distribution, f = 11.0%,k = 16.7 md; and (d) meso-porosity (20–60 mm pore diam-eter) with patchy porosity dis-tribution related to gypsumcementation (white); f = 12.1%,k = 16.0 md. All micrographswere taken under plane-polarized light.


chalk, (3) chalky micropores with uniform distribu-

tion, and (4) chalky micropores with patchy distribu-

tion (Figure 4). The distinction between Tertiary and

Cretaceous chalks is important because a general de-

crease in the size of calcareous nannoplankton across the

Cretaceous–Tertiary boundary (Macleod et al., 1997)

corresponds to a decrease in reservoir quality in Ter-

tiary chalks (Hardman, 1983). Chalks with patchy po-

rosity distribution have been identified, especially re-

lated to patchy chert cementation, but no porosity and

permeability data are available in the studied data set.

Moldic Porosity

Moldic pores are secondary pores formed by the se-

lective, complete, or partial dissolution and recrystal-

lization of grains or crystals. This definition is slightly

modified from Choquette and Pray (1970) by includ-

ing pores formed by partial dissolution and recrystal-

lization. A distinctive difference in solubility between

grains and/or crystals and the surrounding matrix is

commonly needed and is commonly related to miner-

alogical differences (Moore, 2001).


Lønøy 1391

Figure 4. Thin-section photomicro-graphs showing examples of mudstonemicroporosity (<10 mm pore diameter):(a) chalk, f = 25.4%, k = 0.734 md;(b) chalky microporosity, f = 4.9%, k =0.01 md; and (c) patchily distributedchalky microporosity, no porosity, or per-meability measurements are available.Individual pores are too small to be seen,but porosity can be discerned becauseof impregnation with blue-dyed epoxy.The micrographs were taken under plane-polarized light.


Moldic pores are divided into two natural classes

on the basis of intramold pore sizes: moldic micro-

pores (Figure 5a) and moldic macropores (Figure 5b).

Moldic micropores occur as small intramold pores

(typically less than 10–20 mm in diameter) developed

between intramold mud particles or small crystals

(Figure 5a). The pores are commonly associated with

partially dissolved or recrystallized peloids, ooids, and

foraminiferawithmicrogranular structure.Moldicmicro-

pores resemble mudstone micropores in structure, but

the porosity distribution is different, resulting in

different porosity-permeability relationships. Mud-

stone micropore distribution is controlled by micritic

matrix, whereas moldic micropores are controlled by

allochem distribution.

Moldicmacroporesmay have relatively small inter-

mold pore-throat diameters, although individualmolds

can be relatively large (>20–30 mm) (Figure 5b). The

pores contain no or subordinate fine crystals and mud

and are commonly formed through dissolution. Both

diagenesis and depositional setting control the macro-

moldic porosity distribution. Mineralogically unstable

Figure 5. Thin-section photo-micrographs showing examplesof moldic porosity: (a) micro-porosity, f = 21.7%, k = 3.45 md;and (b) macroporosity, f =24.74%, k = 35.2 md. Someof the moldic micropores in(a) are marked by m. Bothmicrographs were taken underplane-polarized light.

Lønøy 1393

grains, e.g., aragonite and evaporite minerals, are com-

monly more susceptible to dissolution than the sur-

rounding calcitic or dolomiticmatrix (Matthews, 1974;

Moore, 1989;Moore, 2001). Aragonite grains are com-

mon in euphotic skeletal assemblages, and shallowwater

depth, warm climate, and low nutrient supply are im-

portant factors for the generation of this assemblage

(see James, 1997, and references therein). Partially do-

lomitized sequences are also susceptible to secondary

porosity development, including moldic macropore for-

mation, because of contrasts in mineralogical stability

(calcite versus dolomite) (Moore, 2001).

Intraparticle Porosity

Intraparticle pores are pore spaces occurring within

grains (Figure 6), either of primary origin or formed

through the decay of organic material in carbonate

skeletons. A skeletal wall will therefore enclose, at least

partly, most intraparticle porosity. The definition of

intraparticle porosity is slightly modified from Cho-

quette and Pray (1970) by not including porosity re-

lated to dissolution (this porosity is part of the moldic

pore system in the classification systempresented here).

The reason for this modification is to differentiate

between

� pores enclosed by skeletal walls (at least partly),

mostly of primary origin (intraparticle pores)

� pores of diagenetic origin, commonly not enclosed

by skeletal walls (moldic pores)

The distribution of intraparticle pores is fauna de-

pendent. Some of the more common faunal constituents

that exhibit this pore type in the studied data set are fora-

minifera, gastropods, bryozoans, corals, and calcispheres.

Vuggy Porosity

The definition of vuggy porosity in this article follows

the definition of Choquette and Pray (1970). Vuggy

pores are secondary solution pores that are not fabric

selective (i.e., the pores cut across grains and/or cement

boundaries) (Figure 7). The pores are of irregular size

and shape and may or may not be interconnected.

Many vugs are solution-enlarged molds where the out-

lines of the precursor grains are poorly defined.

Lucia (1983, 1995) defined vuggy porosity as pore

space significantly larger than or within particles. He

distinguished between separate vugs that typically are

fabric selective in origin and touching vugs that typi-

cally are nonfabric selective in origin. Separate vugs are

connected only through interparticle or intercrystalline

porosity and include intraparticle, shelter, and moldic

porosity. Touching vugs include interconnected larger

cavities, channels, fenestrae, and fractures.

In this study, the vuggy pore definition of Cho-

quette and Pray (1970) has been applied, and the vuggy

Figure 6. Thin-section photo-micrograph showing an exam-ple of intraparticle porosityin fusulinid foraminifera. f =16.0%, k = 1.25 md. The micro-graph was taken under plane-polarized light.


pores are thereforenonfabric selective inorigin.Thevuggy

pores mostly fall into Lucia’s (1983, 1995) touching-

vug pore system, although in some low-porosity sam-

ples, the vugs appear to be nontouching and fall into

Lucia’s separate-vug pore system. For many samples, it

is difficult to tell if the vugs are touching in 3-D, and

one is tempted to classify the sample based on the pe-

trophysical properties. This exemplifies the difficulties

in applying the Lucia vuggy pore system classification.

Vuggy porosity (as defined by Choquette and Pray,

1970) is formed by the dissolution of cement, matrix,

and grains. This typically occurs under the influence of

near-surface meteoric waters (Loucks and Handford,

1992; Saller et al., 1994), but may also be related to

deep-burial fluids (Moore and Druckman, 1981; Cho-

quette and James, 1987; Moore and Heydari, 1993).

Meteoric diagenesis is commonly associated with sea

level lowstands and subaerial exposure surfaces in hu-

mid climates (Loucks andHandford, 1992; Saller et al.,

1994).Deep-burial dissolutionmay be related to hydro-

carbonmaturation and shale dewatering (Moore, 1989).

Fluid-migration pathways, such as fractures and faults,

are the main controls on vuggy porosity distribution dur-

ing deep burial.

POROSITY-PERMEABILITY RELATIONSHIPS

Porosity-permeability crossplots are used for predict-

ing permeability from porosity or vice versa. Borehole

porosity can be estimated fromwire-line logs, but apart

from the nuclearmagnetic resonance log, there is no log

that measures permeability. Therefore, it is common

practice to establish porosity-permeability relationships

through core analysis and then estimate permeability

where cores do not exist using porosity logs and core-

derived porosity-permeability relationships. This

method works well when there is a simple relationship

between porosity and permeability, as in many sand-

stone reservoirs. In carbonate reservoirs, however, the

porosity-permeability relationship is very complex be-

cause of the great variability of pore types.

Porosity cutoffs are applied in reservoir evaluation

to exclude porosity that is thought to have insufficient

permeability to contribute to flow. The cutoff is com-

monly defined from porosity-permeability crossplots

by defining a critical permeability. Application of poros-

ity cutoffs is an easy and quick method for calculating

reserves, and cutoffs give a simple representation of the

pore-type control on reservoir properties.However, the

validity of using a single porosity cutoff value is ques-

tionable when multiple pore types are present because

different pore types may be associated with different

permeabilities for the same porosity value. This is indi-

cated by the data presented here.

Porosity-permeability crossplots were made for

each of the 20 pore-type classes defined in this article

(Figures 8, 9). If the pore types are known or can be

predicted from sedimentologic and/or diageneticmodels,

Figure 7. Core slabs showing vuggy porosity. Plug-derivedporosities and permeabilities are strongly dependent on thelocation of core plugs because of the large vug sizes. Repre-sentative measurements will require whole-core analysis.

Lønøy 1395

Figure 8. Porosity-permeabilitycrossplots for interparticle andintercrystalline pores: (a) interparti-cle micropores (10–50 mm porediameter); (b) interparticle meso-pores (50–100 mm pore diameter);(c) interparticlemacropores (>100 mmpore diameter); (d) intercrystallinemicropores (10–20 mm pore di-ameter); (e) intercrystalline meso-pores (20–60 mm pore diameter);and (f) intercrystalline macropores(>60 mm pore diameter). Notethe significant differences in porosity-permeability relationships relatedto porosity distribution and pore-size variations. Porosity cutoffscharacterize these differences. Cut-offs are derived from inverted axes(permeability on x-axis). Circleddata points in (c) are from sampleswith pore-lining cements, which prob-ably explains the lower permeability-to-porosity ratio. R2 = coefficientof determination; n = numberof samples.



Lønøy 1397

Figure 9. Porosity-permeabilitycrossplots for: (a) chalk, (b) chalky mi-cropores, (c) moldic pores, (d) intra-particle pores, and (e) vuggy pores. Fewsamples were available with chalkymicroporosity. Note the difference inporosity-permeability for Cretaceousversus Tertiary chalks (a). Porositycutoffs are derived from invertedaxes (permeability on x-axis). R2 =coefficient of determination; n = num-ber of samples.


permeability can be predicted fromwire-line-log poros-

ities using the crossplots. Furthermore, porosity cutoffs

can be more accurately defined.

Porosity-permeability is traditionally plotted with

porosity along the x-axis, which is suitable for esti-

mating permeability by a regression of permeability on

porosity. However, to estimate the porosity from a

given permeability (e.g., to estimate a porosity cutoff)

by a regression of porosity on permeability, permeabil-

ity has to be plotted along the x-axis if the usual for-

mulae are to be employed (Jensen et al., 2000).All plots

presented here are plottedwith porosity along the x-axis.However, the given that porosity cutoffs are derived

from plots with inverted axes (porosity on the y-axis),permeability is plotted as a logarithmic value (log k).

The scatter of data points around the porosity-

permeability trendlines can be the result of several

factors:

� Although plugs dominated by only one pore type

have been selected,most samples do have other pore

types that may affect porosity and permeability.� Pore types observed in thin sections may not be

representative of the entire plug, although this has

been minimized through the visual evaluation of the

plugs.


Lønøy 1399

� Three pore-size groups have been defined, where-

as in reality, there is a continuous range of pore

sizes.� Various degrees of porosity patchiness exist.� Analytical error, especially at lower permeabilities

(repeat measurements of some plugs show minor

differences in porosity and permeability).

Interparticle Pore-Type Classes

The best relation between porosity (f) and log k for allinterparticle pore types is a logarithmic trendline

(Figure 8a–c). R2 (coefficient of determination) ranges

between 0.79 and 0.88. The effect of both pore size and

porosity distribution can be clearly seen in the figures,

with a significant increase in k/f when pore size in-

creases and/or porosity distribution becomes patchier.

If 1 md is used as the critical flow parameter, the poros-

ity cutoff for interparticle microporosity (10–50-mmpore diameter) is reduced from 15.3% at uniform po-

rosity distribution to 8.1%, when the porosity is patch-

ily distributed. Similarly, for meso- (50–100-mm pore

diameter) and macroporosity (>100-mm pore diame-

ter), the porosity cutoff is reduced from 11.1 to 5.8 and

8.2 to 5.5%, respectively, for the corresponding 1-md

permeability.

Two of the studied samples (data points encircled

in Figure 8c) have isopachous, pore-lining cements

(Figure 1d) and a lower permeability than expected for

the pore type and porosity values. These cements are

typical of early-marine diagenesis in high-energy beach

rock and hardgrounds (e.g., Moore, 1977; James and

Choquette, 1990) and are thought to have occluded

pore throats and reduced permeability while having

only a minor effect on porosity (e.g., Moore, 2001;

Budd, 2002). The effect of interparticle pore-lining

cements on porosity-permeability relationships is also

documented from siliciclastic sediments (Aase et al.,

1996; Bloch et al., 2002) and affects fine-grained sedi-

mentsmore than coarse-grained sediments (Bloch et al.,

2002). Interparticle pores partially cemented by pore-

lining cements are thus expected to have a lower k/fthan seen in Figure 8a–c.

Intercrystalline Pore-Type Classes

Intercrystalline pores are similar to interparticle pores

in that they generally showa logarithmic best fit between

porosity and log k (Figure 8d–f). An exception is the

uniformly distributed mesoporosity, which shows a

clear linear trend.Why this pore type shows a different

trendline relation is unclear. R2 ranges between 0.79

and 0.94. No data are available for patchily distributed

macroporosity.

As for interparticle pores, pore size and porosity

distribution have a clear control on k/f (Figure 8d–f). Asignificant increase in k/f exists when pore size in-

creases and/or porosity distribution becomes patchier.

If 1 md is used as the critical flow parameter, the po-

rosity cutoff for intercrystalline microporosity (10–

20-mm pore diameter) is reduced from 22.4% at uni-

form porosity distribution to 18.6%, when the porosity

is patchily distributed. Similarly, for mesoporosity (20–

60-mm pore diameter), the porosity cutoff is reduced

from 16.9 to 9.1%. Uniformly distributed macropo-

rosity (>60-mm pore diameter) has a porosity cutoff

of 5.4%.

Mudstone Micropore Classes

Chalk shows a second-order polynomial best fit be-

tween porosity and log k, whereas chalky micropores

with uniform distribution show a linear relationship

(Figure 9a, b). R2 ranges between 0.80 and 0.96 for

mudstone micropore classes. If 1 md is used as the

critical flow parameter, the porosity cutoff is 25.8%

for Cretaceous chalk, 31.3% for Tertiary chalk, and

27.0% for chalky microporosity with uniform distri-

bution. The only sample with patchy distribution of

chalky micropores shows a significantly higher k/fthan for samples with a uniform porosity distribution

(Figure 9b).

Moldic Pore-Type Classes

Moldicmacropores show a third-order polynomial best

fit between porosity and log k (Figure 9c). Micropores

show both linear and third-order polynomial best-fit

trendlines, but for simplicity, the linear trendline was

chosen. R2 is 0.90 for macropores and 0.86 for mi-

cropores. If 1 md is used as the critical flow param-

eter, the porosity cutoff is 13.1% for macropores and

16.2% for micropores. Differences in k/f for the two

pore-type classes result from a slight overall reduc-

tion in pore-throat diameters, as confirmed by mer-

cury capillary-pressure measurements, and is prob-

ably related to the presence of intramold matrix in

moldic micropores.


Intraparticle Porosity

Intraparticle pores show a linear best fit between

porosity and log k, with an R2 = 0.86 (Figure 9d). If

1md is used as the critical flow parameter, the porosity

cutoff is 14.1%.

Vuggy Porosity

Vuggy pores show a linear best fit between porosity and

log k (Figure 9e), with a low coefficient of determina-

tion (R2 = 0.50). If 1 md is used as the critical flow

parameter, the porosity cutoff is 6.2%.

The poor relationship between porosity and

permeability is most likely related to a low sample-to-

vug size ratio (many vugs aremore than 1 cm [0.4 in.] in

diameter). It may also be argued that the scatter in data

points results from a variable degree of connectivity

between the vugs, but such an explanation could not be

confirmed by the present data set.

Mixed Pore Types

As discussed previously, the data presented here in-

clude samples that are strongly dominated by only one

pore type. The selected samples were thus chosen

from a significantly larger data set, many of which

have a mixed pore-type composition. Samples with

mixed pore types plot between the endmembers, but

tend to be biased toward the pore type of highest

permeability.

Comparison to Other Classification Systems

All samples in the present database were classified in

accordance with the Choquette and Pray (1970), Lucia

(1983, 1995, 1999), and the new classification system

presented here to evaluate which system has the best

predictive properties for porosity and permeability.

The coefficient of determination (R2) was used as a

measure, with higher R2 indicating better correlation

between porosity and permeability. The results are

presented in Tables 1 and 2 and Figure 10.

Interparticle pores, intercrystalline pores, and

mudstone micropore systems of the new classification

system give R2 = 0.79–0.96 in porosity-permeability

crossplots (Figures 8, 9a, b). Classified according to

the Lucia (1983, 1995, 1999) (Figure 10a–c) and

Choquette and Pray (1970) classification systems, R2

was reduced to 0.62–0.79 (Lucia) and 0.50–0.70

(Choquette and Pray). Chalk is overrepresented in the

data set of Lucia pore-type class 3, and equal propor-

tions of mudstone micropores, intergrain pores, and

intercrystal pores would thus probably give a lower

coefficient of determination for class 3 than given in

Figure 10c. Several reasons why the new classification

system yields a significantly better correlation between

porosity and permeability exist:

1. The effect of patchy porosity distribution on the

porosity-permeability relationship (Figure 8)

2. The split between interparticle and intercrystal

porosity (as in Choquette and Pray, 1970) and the

introduction of mudstone microporosity (the effect

of this can be seen in Figure 10a–c)

3. The use of pore-size differentiation instead of par-

ticle size and sorting differentiation (samples in the

studied data set show widely different pore sizes

within each of Lucia’s, 1995, 1999, interparticle

classes because of the variable extent of interparti-

cle and intercrystalline cementation and allochem

sorting)

To evaluate the influence of points 2 and 3 on the

porosity-permeability relationship, samples with

patchy porosity distribution were excluded from the

data set. Using the Lucia (1983, 1995, 1999) classifi-

cation system, this gaveR2 = 0.77 for interparticle class 1,

R2 = 0.74 for interparticle class 2, and R2 = 0.68 for

interparticle class 3. This is a significant improvement

for class 2, but all classes still have a considerably lower

coefficient of determination than for those in the new

classification system (Figures 8, 9a, b).

Intraparticle,moldic (bothmicro- andmacromoldic),

and some of the low-porosity vuggy pores in the new

classification system are classified as separate-vug

pores in the Lucia (1983, 1995, 1999) classification sys-

tem. The separate-vug pore system has an R2 = 0.86

(Figure 10d), which is comparable to R2 values for in-

traparticle pores (R2 = 0.86), moldic micropores (R2 =

0.86), andmoldicmacropores (R2 = 0.90).However, as

seen in Figure 9c and d, different pore types may have

important differences in porosity cutoffs for the same

permeability. In some hydrocarbon fields, this change in

cutoff may have an important effect on net/gross ratios.

Vuggy pores in the new classification system, ex-

cept for some low-porosity samples, are classified as

touching-vug pores in the Lucia (1983, 1995, 1999)

classification system. The classification systems give

Lønøy 1401

comparable coefficients of determination (R2 = 0.50 in

the new classification system; R2 = 0.45 in the Lucia

classification system).

IMPLICATION FOR RESERVE ESTIMATES

Carbonate reservoirs commonly have a wide range of

porosities, and net/gross (N/G) is commonly calculat-

ed by applying a porosity cutoff. Porosity cutoff ranges

significantly for different pore types in the data set

described here, from 5.4 to 31.3% at 1-md permeabil-

ity. Pore-type definition is thus crucial for hydrocarbon

reserve calculation.

A Devonian hydrocarbon field in Russia will be

used to illustrate the effect of pore-type definition on

reserve calculation. For simplicity, the reserve esti-

mates were calculated based on data from a single well

(regional variation across the field was not considered).

Furthermore, porosity and permeability data were

derived from core plugs, assuming that the plugs were

representative for the reservoir section. Reserves were

calculated by applying a variable porosity cutoff, de-

fined bed by bed, based on pore type.

The reservoir section is dominated by interparticle

micro- and mesoporosity. Core and thin-section studies

show that the porosity has a patchy distribution and

is related to the dissolution of interparticle cement.

Failure to recognize the patchy distribution of the in-

terparticle porosity may have a severe effect on re-

serve estimates because porosities commonly are above

the cutoff values for patchily distributed porosity but

Figure 10. (a–c) Data from pore systemsof this study plotted as interparticleclasses 1–3 of Lucia (1983, 1995, 1999).In these plots, the complete spread ofdata corresponds to the coefficient of de-termination shown in the back text boxes(R2 values of 0.79, 0.62, and 0.68, inplots a–c, respectively). The blue and reddata points in (a–c) demonstrate theeffect of differentiating the data intointercrystalline and intergrain pore types(following the approach used by Cho-quette and Pray, 1970). The effect on thecoefficients of determination is shownin red and blue text boxes. The plots showthat the distinction between intercrystal-line and intergrain pore types varies as afunction of grain size and porosity, thegreatest differentiation being within clas-ses 2 and 3. The figure in (c) shows theneed for incorporating mudstone micro-porosity pore types into any classificationscheme. The figure in (d) plots both thetouching-vug and separate-vug pore typesof Lucia. These pore types correspondto intraparticle, moldic (both micro- andmacromoldic) and vuggy pores of thisstudy. For all plots, the trendline yieldingthe highest R2 value was chosen.


below the cutoff for uniformly distributed porosity,

using a 1-md permeability cutoff (Figure 11). Calcu-

lations show that estimated reserves are 370% higher

for the patchy pore distribution compared to the uni-

form pore distribution (Figure 11).

This example illustrates the importance of pore-

type evaluation in reserve estimation. In many cases,

pore-type evaluation can be one of the most important

factors in the economic evaluation of a prospect or field.

CONCLUSIONS

A new pore-type classification system that better com-

bines sedimentologic and diagenetic features and flow-

related properties has been developed. This system

significantly improves porosity to permeability rela-

tionships for individual pore types compared to pre-

vious classification systems and yields coefficients

of determination (R2) mostly in the range of 0.79–

0.96.

Porosity cutoffs derived fromporosity-permeability

crossplots show significant variability among the dif-

ferent pore types, ranging from 5.4 to 31.3% at 1-md

permeability. This has a major impact on reserve cal-

culations because it strongly affects net/gross.

A simple model run on a Devonian hydrocarbon

reservoir indicates that estimated reserves may vary by

several hundred percent because porosity cutoffs will

likely vary by pore type. Knowing the dominant pore

types and their distribution in the reservoir are thus

critical in field evaluation, both in the exploration and


Lønøy 1403

production phases. Pore type could be one of the most

important parameters in the economic evaluation of a

prospect or field.

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Figure 11. Pore-typecontrol on reserve calcu-lations in a 35-m (114-ft)-thick Devonian reservoir,Russia. Vertical lines onporosity-permeability cross-plot represent porositycutoffs at 1-md permeabil-ity and are based onglobal trendline curves(Figure 8a, b). Markedtrendlines are based on theDevonian reservoir anddiffer slightly from theglobal trendlines. The tableshows differences in cal-culated net-to-gross andaverage phi for patchyversus uniform pore distri-butions based on core-plug data. BP = interpar-ticle porosity, N/G =net/gross pay.


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Making sense of carbonate pore systems