quantitative character is at ion of carbonate pore systems by digital image analysis

8/8/2019 Quantitative Character is at Ion of Carbonate Pore Systems by Digital Image Analysis

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ABSTRACT

A new method of digital image analysis can quan-tify pore parameters over more than three orders ofmagnitude, from a submicron to a millimeter scale.This porosity characterization does not requireknowledge of lithology, age, burial depth, or diage-nesis of the sample. The method is based on digitalanalyses of images from thin sections at variablemagnifications taken under an optical microscope(OM) and under an environmental scanning elec-tron microscope (ESEM). The results help explainvariations in permeability for carbonate sampleswith a variety of complex pore structures. Theanalyses, however, can be done on any thin sec-tions of other rock types.

The OM images provide macroporosity informa-tion, whereas the ESEM images yield information onmicroporosity. The boundary between macroporosi-ty and microporosity is defined at a pore area of 500m2, which translates to a pore length of approxi-mately 20 m, which is roughly the thickness of athin section and thus the resolution of the OM. Thedigitized thin-section images are binarized into amacropore and a matrix phase (OM) or a microporeand a solid phase (ESEM). A standard digital imageanalysis program is used to detect all individual

pores and to measure pore area and pore perimeBased on these analyses, one can calculate for esample the amount of macroporosity, the amounmicroporosity within the matrix (intrinsic microrosity), the shapes of the macropores (perimeover area), and the pore size distribution.

Comparison of total porosity determined frplugs indicates that macroporosity and microporty values based on this methodology match the pdata, confirming the validity of the method. Tcombination of macroporosity and microporodata yields pore size distribution and pore shainformation that can explain the distributionphysical properties, in particular permeabilityparameter sensitivity analyses using neural nworks, permeability appears to be mainly controby the macropore shape in high-permeability sples, and by the amount of intrinsic microporositythe low-permeability samples.

INTRODUCTION

Pore space in sedimentary rocks is a crucial tor in applications that range from hydrocarbreservoir characterization to hydrologic and enronmental issues. In addition to traditional desctive and qualitative porosity evaluations, thexists a need for quantitative methods that (1) chacterize the various aspects of pore space and enable a quantitative assessment of the distributof porosity and other physical properties. In tstudy, we present a new method of digital imanalysis that quantifies macroporosity and microrosity, in particular of sedimentary rocks, usimages of highly polished thin sections.

Digital image analysis is a well-establishmethod of quantifying pore space from imagesthin sections (e.g., Ehrlich et al., 1984, 1991aMcCreesh et al., 1991; Gerard et al., 1992). Thstudies used images of thin or polished sectifrom either the optical microscope or the electmicroscope to separate the rock into a pore ansolid phase. Most optical images are converted tbinary image using an impregnation of the p

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Copyright 1998. The American Association of Petroleum Geologists. Allrights reserved.

1Manuscript received March 17, 1997; revised manuscript receivedJanuary 12, 1998; final acceptance April 23, 1998.

2University of Miami, Comparative Sedimentology Laboratory, 4600Rickenbacker Causeway, Miami, Florida 33149. Present address: SwissFederal Institute of Technology ETH, Geological Institute, Sonneggstr. 5,8092 Zrich, Switzerland; e-mail: [email protected]

3Services Techniques Schlumberger, 50 av. Jean Jaurs, 92541Montrouge, France.

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University of Miami, Comparative Sedimentology Laboratory, 4600Rickenbacker Causeway, Miami, Florida 33149.Most of this study was done at the Schlumberger-Doll Research

Laboratory in Ridgefield, Connecticut, where numerous people helped uswith this project. We thank T. Ramakrishnan for providing access to part ofthe case study data set, and Bil l Kenyon, Alberto Malinverno, R.Ramamoorthy, and Larry Schwartz for discussing the various topics. AlainRabaute tested and improved the image analysis method. A special thanks toAbbygail Matteson, who helped with the database and core measurements,and to Wave Smith, who performed most petrophysical analyses. The paperprofited from thorough reviews of Jeffrey Dravis, Jerry Lucia, and RobertEhrlich.

Quantitative Characterization of Carbonate Pore Systemby Digital Image Analysis1

Flavio S. Anselmetti,2 Stefan Luthi,3 and Gregor P. Eberli4

AAPG Bulletin, V. 82, No. 10 (October 1998), P. 18151836.


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space with blue-dyed epoxy (Crabtree et al., 1984),as it was performed in this study. Alternatively,coating the epoxy with fluorescent dye providesanother possibility to detect impregnated porosity(Yanguas and Dravis, 1 985; Ruzyla and Jezek,1987). Most methods, however, are somehow lim-ited because they use images only at a finer or larg-er scale, depending on the applied microscope

technique. Microporosity can be detected by opti-cal microscopy with fluorescent techniques(Yanguas and Dravis, 1985 ) or diffuse plane-polarized light (Dravis, 1991); however, the resolu-tion with these optical methods is not highenough to sufficiently characterize or quantifymicroporosity. In this study, we present a newmethod of digital image analysis that covers a widerange of pore sizes from submicron to millimeterscales. By combining optical microscopy (OM) andenvironmental scanning electron microscopy(ESEM), we are able to characterize pore spacewith sizes varying by more than three orders ofmagnitude in the same sample. Such a range of the

resulting pore size distribution could so far bereached only by the more complicated and time-consuming mercury injection method (see, forexample, Dullien, 1981; Pittman, 1992).

To use the method, only one epoxy-impregnatedand polished thin section per sample is needed,because the ESEM does not require the section tobe coated. Having access to both types of micro-scopes, the whole investigation is thus inexpen-sive. In addition, the image analyses program weused (NIH Image) is freely available as publicdomain software. ESEMs are becoming morewidespread; however, if this microscope type isunavailable, the method can be applied by usingtwo thin sections, with one section coated to allowinvestigation in a conventional electron micro-scope. Digital image acquisition takes approximate-ly 15 minutes per sample, with additional timeneeded for the image analyses and statistical evalua-tions; however, analysis can be done simultaneous-ly with the data from all samples, and the proce-dures can be automated using standard spread-sheet programs.

This analytical approach has been used on a car-bonate data set because carbonates have a morecomplex pore geometry than other sedimentaryrocks, such as siliciclastics. This complexity wasrecognized by several early studies that classifiedcarbonate textures (Archie, 1952; Dunham, 1962;Folk, 1962) and pore types (Choquette and Pray,1970; Lucia, 1983, 1995). The classificationschemes were intended to relate composition anddiagenesis to a predictable process to solve prob-lems in carbonate reservoirs, which had some puz-zling production behavior when compared tomore predictable siliciclastic reservoirs (Nurmi et

al., 1990; Marzouk et al., 1995). One of the mainreasons for the large variety of pore sizes andgeometries is the high diagenetic potential of car-bonate rocks (Schlanger and Douglas, 1974), whichcommonly results in fast and intense alterations ofcarbonate sediments that strongly affect the porecharacteristics. Another problem addressed in earli-er studies was evaluating microporosity in carbon-

ates (Pittman, 1971), because optical methods pro-vided no tool to investigate this microporosity. Theneed for an improved methodology in carbonateshas led to a series of studies that classified the vari-ous pore types and that related them to the widesuite of petrophysical parameters (Kieke andHartmann, 1974; Lucia, 1983, 1995; Ehrlich et al.,1991b; Anselmetti and Eberli, 1993; Melim et al., inpress); however, no quantitative method tha twould result in a complete quantitative characteri-zation of carbonate pore systems is available yet.

Despite the observed variety in primary and sec-ondary macroporosity and microporosity, we donot attempt to characterize and classify these car-

bonate samples by pore types and then comparethem to petrophysical properties, an approachused in other studies (Lucia 1983, 1995; Melim etal., in press); however, we do characterize thesesamples entirely with quantitative observationsfrom digital image analysis of ESEM and OM thin-section images, thus avoiding any subjective andqualitative descriptions. These quantitative analy-ses will be compared with the lithology and petro-physical data, in particular the permeability datafrom the same samples. This approach has theadvantage of avoiding any descriptive bias by thegeologist, but also has a disadvantage in that quanti-tative data obtained by image analysis are depen-dent on thin-section selection and fields of viewunder the microscope.

The general complexity of carbonate pore space,the open questions on the role of microporosity,and the need for quantitative reservoir characteriza-tions make carbonate rocks an ideal medium onwhich to apply the new method for pore spacecharacterization through digital image analysis. Inthe following sections, we describe the sampledatabase, explain the methodology, discuss theresults, and finally correlate the result with perme-ability measurements from the same samples.

SAMPLE DATABASE

Carbonate rock samples collected for this evalua-tion have a wide range of bulk porosities, poretypes, and rock textures. Rock age, mineralogy, andorigin were minor considerations. A sample set wascompiled with 11 samples from cores drilled onGreat Bahama Bank (sample numbers begin with

1816 Digital Image Analysis


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the prefix BA) (Anselmetti and Eberli, 1993;Melim et al., in press), 4 samples from cores ofCretaceous oil reservoirs in the Middle East (sam-ple numbers begin with ME), and 2 samples fromoutcrops of the carbonate platform of theMontagna della Maiella (sample numbers beginwith MA) in central Italy (Eberli et al., 1993). Allof these samples are pure carbonates and lack sig-

nificant content of quartz or clay. Most of the sam-ples represent extreme carbonate lithologies, sothat the methodology could be tested on highlyvariable carbonate textures and fabrics that yield alarge variety of porosity and pore types. Table 1summarizes the key characteristics of these sam-ples. Figure 1 illustrates the four main macroporetypes (defined as pore area > 500 m2) found inthese samples (see also Table 1). The major macro-pore types encountered in carbonates are interpar-ticle pores, which include intergrain and intercrys-tal pores (Lucia, 1983), intraparticle pores, anddissolution moldic pores. Figure 2 shows twoexamples of microporosity (defined as pore area

< 500 m2), which illustrate the variability ofmicropore types in the investigated samples.

IMAGE ACQUISITION

Images of the analyzed thin-section sampleswere acquired with both the OM (Toshiba CCDcamera) and the ESEM (Electroscan 2020 model).This allowed us to analyze pore lengths rangingfrom over 1 mm to less than 1 m, or over threeorders of magnitude. Such a procedure is possibleonly if an ESEM is used, because the ESEMrequires only a simple conducting pathway fromthe surface of the slide to the ground; it does notrequire any sample coating or preparation otherthan polishing. Consequently, the thin section canbe switched from an OM to an ESEM and back invery little time. This allows, in the same sample,macroporosity to be analyzed with the OM,whereas the micropore space is analyzed with theESEM. This method can easi ly be extended toinclude pores that are too large to be covered bythe OM. By analyzing core photographs or scans,x-radiographs, or borehole images (FormationMicroScanner) (Nurmi et al., 1990), which alldisplay large-scale pores with sizes of up to 10 cm,one could widen the pore recognition by anothertwo orders of magnitude.

As an example, Figure 3 shows sample BA611 atsix magnifications, with three OM and three ESEMviews. The center of the images is in the same sam-ple location in all views. It is not easy to recognizethe same features on both methods simply becausethe OM uses light transmission and thus penetratesthe entire 2030 m of the thin section, whereas

the electron beam of the ESEM penetrates osome tens of nanometers (for the secondary etron detector used in this study). The ESEM imtherefore is almost a perfect two-dimensional cwhereas the OM averages over the thicknessthe thin section and displays some shelveffects (Crabtree et al., 1984). Figure 4 illustrathe difference between the two methods fo

thin section from the same sample (BA611), takat the same magnification under both the OM athe ESEM.

The images are digitized generally as 640 48-bit gray-scale images, but additionally, for the ocal microscope, 8-bit and 24-bit color versionthe same view also are acquired. These images processed to extract the pore space and its propties. The OM images cover an area of approximly 2 1.5 mm, whereas the ESEM images havsize of about 60 40 m.

DIGITAL IMAGE ANALYSIS

Although we recognize all the various ptypes, as shown in Figures 1 and 2, we treat pore space during the steps of digital image ansis in a strictly statistical manner; therefore, avoid labeling the various pore types, characteing them instead quantitatively through relevpore size and geometric parameters.

Binarization

The digital images acquired in this study are ptitioned (binarized) into a pore phase and a solidmatrix phase using a standard image processpackage (NIH Image version 1.56). Our standapproach is first to impregnate the sample wblue-dyed epoxy, and then to prepare highly pished thin sections (see also Crabtree et al., 19Ehrlich et al., 1991a; Gerard et al., 1992). The Oimages allow us to characterize the macropspace by separating it from the matrix; therefooptical images are not binarized into a pore ansolid phase, but into a macropore and a matphase, with the matrix still containing opticundetectable microporosity (Figures 5, 6). TESEM images display the micropores within matrix (Figure 7). The ESEM in this study was uto analyze only the intrinsic matrix properties to separate these analyses from the pure macpore information of the OM. As a consequenspecial care was taken to obtain ESEM images wout any disturbing macropores (Figure 7).

The OM images are binarized by assigning pphase to all pixels containing blue color tones. Busually does not appear as a natural color in

Anselmetti et al. 18


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Table1.

Source,SedimentType,Permeability,andPorosityDatafor17CarbonateSamplesS

electedforThisStudy* D

igitalImageA

nalysis

Main

Total

Intrinsic

mac*

*

Macrop

ore

Porosity

mac*

*

mic

**

Shape

Perm.

Sample

Source

Age

SedimentType

Type

(%)

(%)

(%)

()

(md)

BA932

BahamasDrillingProject

Pliocene

?(microsucrosicdolomite)

Intercrystal

50

41

0

4.0

3160

BA1016


Miocene

?(microsucrosicdolomite)

Intercrystal

38

23

0

2.4

466

MA123

Maiellaou

tcrops,Italy

Cretaceous

Rudistgrainstone

Intergrain

33

14

9.2

2.2

296

BA1131


Pliocene

Bio

clasticgrainstone

Intergrain

23

10

12

2.0

234

BA611


Pliocene

Bio

clasticpackstone

Moldic

54

41

26

3.1

181

MA178

Maiellaou

tcrops,Italy

Cretaceous

Rudistgrainstone

Intergrain

29

20

20

2.2

172

ME16

MiddleEa

stdrillcores

Cretaceous

Peloidalgrainstone

Intergrain

14

12

7.9

2.3

38.7

BA2184


Miocene

Glo

bigerinidwackestone

Intraparticle

41

2.4

36

11.3

BA1758


Pliocene

Glo

bigerinidpackstone

Intraparticle

44

10

27

2.0

10.4

ME11

MiddleEa

stdrillcores

Cretaceous

Peloidalpackstone

None

29

0.0

7

27

10.2

BA526


Pliocene

Bio

clasticpackstone

Moldic

39

28

13

1.7

7.6

8

ME14

MiddleEa

stdrillcores

Cretaceous

Mu

dstone

Moldic

29

4.8

26

2.0

6.5

7

BA1043


Pliocene

Bio

clasticwackestone

None

45

0.9

8

36

1.4

0

BA212


Pleistocene

Peloidalgrainstone

Moldic

24

26

0

1.4

0.7

9

ME10

MiddleEa

stdrillcores

Cretaceous

Mu

dstone

Moldic

12

2.5

9.4

0.0

8

BA694


Pliocene

Bio

clasticwackestone

Moldic

3.7

2.8

2.7

0.0

2

BA1052


Pliocene

Peloidalpackstone

None

1.5

0.0

0

0.0

1

*Totalporositywasde

terminedon2-cm-diameterplugsbyhelium

po

rosimetry.

Macroporosity,

intrinsicmicroporosity

,andmacroporeshapevaluesareobtainedthroughdigitalimageanalysis.

Permeabilitywasmeasuredfrom

flowofhelium

throughthecoreplugs.

**mic=microporosity,mac=macroporosity.

mic 50,000 m2. The three largestclasses coincide with macropores, whereas thefour smallest classes cover the micropores, asdefined in this study. Based on the individualpores in the pore size classes, we then calculate arelative porosity contribution n for each of theseven pore size classes by equations 4 and 5. Forthese calculations, one has to consider that theimage area (field of view) of the four smaller sizeclasses excludes macropores (Figure 5), so that

tot mac mic mac

= +

( )1

mic mic tot ESEM imageA A= ( )

mac mac tot OM imageA A= ( )


Figure 4Comparison of micrograph image from an optical microscope (OM) (top) and from an environmentalscanning electron microscope (ESEM) (bottom) for the same view of sample BA611. The OM sees through the wholethickness of the thin section (2030 m) and images several layers of micritic calcite crystals. In contrast, theESEM penetrates only the uppermost thin-section layer, imaging only some tens of nanometers.


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the value in equation 5 has to be corrected formacsimilar to equation 3.

For macropores (size classes eg):

(4)

For micropores (size classes ad):

(5)

where n is the fractional amount of porosity con-tributing to the total porosity by pores from size

classn

.If the fields of view of ESEM and OM were chosenrepresentatively, then the sum of all these contribu-tions (n) should equal the total porosity from pluganalyses. To make the resulting pore size distributionconsistent with the whole-plug analyses, we normal-ize the distribution such that n coincides with thetotal porosity measured on the core plugs. This nor-malization does not change the shape, and thus theimportant characteristics, of the pore size distribu-tions because all size classes are multiplied by thesame normalization factor. In most instances thesenormalizing corrections were minor, and 13 of the 16normalization factors lay within 0.7 and 1.3. As men-

tioned, one could simply increase the number ofimages or even the number of thin sections to obtaina better match with plug or whole-core data.

Pore Shape Analyses

Not only the amount of porosity, but also the poregeometry, affects the physical properties of rocks

(Ehrlich et al., 1991b; Anselmetti and Eberli, 1993;Lucia, 1995; Melim et al., in press). Elongatedpores, such as cracks, have different effects onphysical properties than do round pores, such asdissolution molds (Brie et al., 1985; Wilkens etal., 1991). In this study, we use the ratio of poreperimeterPto pore areaA as a simple (and only)shape parameter. A circular pore (in 2-D) has thelowest ratio between perimeter and area, where-as an elongated pore or a crack has a higherperimeter/area value (Figure 8). This 2-D perime-ter/area value is expected to correlate with theconnectivity of the sample because complicated,branching pore geometries are more likely toform a connected pore network. To make thisperimeter/area value, , dimensionless we use thesquare root of the area, and, for convenience, wenormalize it such that it becomes 1 for a circle, or

(6)

A spherical pore is circular in 2-D and thus has avalue of = 1.0. No smaller values are possible,although in practice they occur due to the discretenature of the digitized image. An ideal interparticle

pore in between spherical grains has a value of=1.9, and cracks may have values of> 5 (Figure 8).The average value ofof one sample is calculated byweighing the individual by the pore size:

(7)

=

( )A

A

i i

i

i

i

=P

A2

n n tot ESEM image mac A A= ( ) ( ) 1

n n tot OM imageA A= ( )


Matrix

View in OM(approx. 2 x 1.5 mm)

View in ESEM(approx. 60 x 45 m)

Micropores (500 m )2

Thin section ofplug

2.5 cm

Figure 5Schematic drawings showing the concept of porosity quantification by digital image analysis as used inthis study. The carbonate porosity is separated into macropores and micropores. The optical microscope (OM)image (center) is used to analyze the macropores (area > 500 m2 or size > 20 m) scattered in a microporousmatrix. The micropores (area < 500 m2 or size < 20 m) within this matrix are analyzed with environmental scan-ning electron microscope (ESEM) images (right) for a field of view that does not contain any macropores. To quan-tify the pore space for the whole size range, macropore and micropore data sets were combined.


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Figure7

Fourexam

plesofdigitalimageanalysisofm

icroporosityshowninrough-surfa

ceenvironmentalscanningelectro

nmicroscope(ESEM)images

(top),flatESEMimagesofpolishedthinsections(middle),andbinarizedmicroporosityim

ages(bottom).Porosityisblack,so

lidphaseiswhite.(A)Sample

BA526.

Micriteofam

oldicpackstonewithlargecalcitecrystals(53

0mlength);mic=13%

.(B)SampleME14.

Mudstonewithmicritic,roundedcalcitecrys-

talsthatarebetween1and5minlength;mic=26%.

(C)SampleBA1043.

Micriteofbioclasticwackestoneconsistingofpreservedaragoniteneedleswith

crystallengthoflessthan2m.

mic=36%.

(D)SampleBA694.

Densemicriteofawackeston

ewithmicritecrystalsizebelowim

agingresolution(

quantitative character is at ion of carbonate pore systems by digital image analysis

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