Pore-scale modelling of carbonates 1

Pore-scale modelling of carbonates

Hiroshi OkabePetroleum Engineering and Rock Mechanics Research Group

Department of Earth Science and EngineeringImperial College of Science, Technology and Medicine

Pore-scale modelling of carbonates 2

Contents

Introduction Background, motivation, objectives

Carbonates Brief overview of reconstruction method Our reconstruction method

Pore-scale modelling of carbonates 3

Introduction Background

Sandstone -We have shown the capability of pore-scale modelling to predict successfully primary drainage and water flood relative permeabilities of clastic rocks with wettability variations.

Carbonate -Few studies have been conducted.

Motivation -why carbonates? A significant amount of the world’s hydrocarbon

reserves are located in carbonate formations. Particular interest to the petroleum industry.

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Introduction - Objectives - Prediction of transport properties, such as relative per

meabilities and capillary pressure.

Well defined relative permeabilities are of great importance in adequate reservoir management Representative network structure is required Wetting conditions of reservoir are vitally important

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Carbonates

Overview Sedimentology Diagenesis Heterogeneity and anisotropy

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-Overview Most of the world’s giant hydrocarbon fields are carbo

nate reservoirs. Carbonates

contain more than 50% of the world’s hydrocarbon reserves. are predominantly intrabasinal origin, primary dependence on

organic activities and susceptibility to modification by post-depositional mechanisms.

Organisms have an important and direct role in determining the reservoir quality. Processes, such as compaction, lithification and other diagenetic events result in large variations in the reservoir quality of carbonates.

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-Sedimentology

Particularly sensitive to environmental changes. Rapid but easily inhibited. Temperature variations biogenic activity sedim

ent production (strongly depth dependent). Form very close to the final depositional sites. Intrabasinal factors control facies development. Texture is more dependent on the nature of the skelet

al grains than on external influences.

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-Diagenesis

Particularly sensitive to post-depositional diagenesis, including dissolution, cementation, recrystallization, dolomitization, and replacement by other minerals.

Burial compaction fracturing and stylolithification.

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-Heterogeneity and anisotropy Carbonates are characterized by different types of porosi

ty and have unimodal, bimodal and other complex pore size distributions, which result in wide permeability variations for the same total porosity, making difficult to predict their producibility.

Anisotropic permeability

　 Vug and channel.

Mixed wettability.

0

5

10

15

20

25

0.01 0.10 1.00 10.00

Equivalent Pore Entry Radius, microns

Occ

upie

d P

ore

Vol

ume

, %

PV

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Brief overview of reconstruction method (1/3) Almost all the targets have been sandstones. Reconstruction approaches

Stochastic reconstruction Process based reconstruction

2D thin-sections BSE (Backscattered electron micrograph) Serial sectioning (Single-orientation, Multiorientation ) Pore space partitioning

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Brief overview of reconstruction method (2/3) Information from 2D thin-sections

Binary phase function Void-phase autocorrelation function

Simulated annealing

0

1rZ rZ

2

urZrZuRZ

2 u

refn

simnn ufuffE

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- Brief overview of reconstruction method (3/3) -

The Statoil method (process based method)

Thin section analysis Sedimentation Compaction Diagenesis

Quartz cement overgrowth Clay: pore lining, pore filling and pore bridging

Morphological quantities Transport properties

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Reconstruction Method

The preferred method would be to construct a three-dimensional pore-network structure directly from readily available data, such as two-dimensional thin sections.

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Our Planned Procedure (1/2)1. Take 2D thin-sections

BSE, serial sectioning

2. Image analysis: measure shape factor, inscribed radius etc. Account for orientation and constriction

factor. We have a population of pore and throat.

3. Conversion: 2D to 3D network as a guess We stochastically scatter pores and

throats on the basis of analysis.

4. Prediction: 3D to 2D Computationally cut the network to creat

e a predicted thin section.

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Our Planned Procedure (2/2)

5. Comparison Compare predicted model with experiments. Some idea of local connectivity.

6. Modification Modify the network 1) swap pores and throats, 2) change

constriction factor 3) change coordination number. Then compare with experiment again.

7. Optimization Use an optimization technique to improve the model (e.g.

simulated annealing).

8. Use network simulator to calculate transport properties

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Thin Sections

These thin sections would need to have sufficient resolution to image interparticle porosity, as well as sufficiently extensive to obtain a representative sample of vugs.