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
Pore-scale modelling of carbonates 6
-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.