Pore-scale modelling of carbonates 1 Hiroshi Okabe Petroleum Engineering and Rock Mechanics Research Group Department of Earth Science and Engineering Imperial 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.

Pore-scale modelling of carbonates 4 Introduction - Objectives - Prediction of transport properties, such as relative permeabilities 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

Pore-scale modelling of carbonates 5 Carbonates Overview Sedimentology Diagenesis Heterogeneity and anisotropy

Pore-scale modelling of carbonates 6 -Overview Most of the world’s giant hydrocarbon fields are carbonate 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.

Pore-scale modelling of carbonates 7 -Sedimentology Particularly sensitive to environmental changes. Rapid but easily inhibited. Temperature variations  biogenic activity  sediment 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 skeletal grains than on external influences.

Pore-scale modelling of carbonates 8 -Diagenesis Particularly sensitive to post-depositional diagenesis, including dissolution, cementation, recrystallization, dolomitization, and replacement by other minerals. Burial compaction fracturing and stylolithification.

Pore-scale modelling of carbonates 9 -Heterogeneity and anisotropy Carbonates are characterized by different types of porosity 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.

Pore-scale modelling of carbonates 10 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

Pore-scale modelling of carbonates 11 Brief overview of reconstruction method (2/3) Information from 2D thin-sections Binary phase function Void-phase autocorrelation function Simulated annealing

Pore-scale modelling of carbonates 12 - Brief overview of reconstruction method (3/3) - T he 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

Pore-scale modelling of carbonates 13 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.

Pore-scale modelling of carbonates 14 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 create a predicted thin section.

Pore-scale modelling of carbonates 15 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

Pore-scale modelling of carbonates 16 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.