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Multipoint Statistics to Generate Geologically Realistic Networks

Multipoint Statistics to Generate Geologically Realistic Networks. Hiroshi Okabe supervised by Prof. Martin J Blunt Petroleum Engineering and Rock Mechanics Research Group Department of Earth Science and Engineering Imperial College London. Contents. Introduction

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Multipoint Statistics to Generate Geologically Realistic Networks

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  1. Multipoint Statistics to Generate Geologically Realistic Networks Hiroshi Okabe supervised by Prof. Martin J Blunt Petroleum Engineering and Rock Mechanics Research Group Department of Earth Science and Engineering Imperial College London Multipoint Statistics to Generate Geologically Realistic Networks

  2. Contents • Introduction • Background / Motivation / Objectives • Brief overview of current reconstruction method • Our methodology: Multiple-point statistics model • Preliminary results for sandstone • Future work Multipoint Statistics to Generate Geologically Realistic Networks

  3. Introduction • Background • Flow modelling of Sandstone – successfully predicted • A shortage of pore-scale network structures • Carbonate – beyond the resolution of Micro-CT • Necessary to find another approach in order to generate a pore space representation – a multiple-point statistical technique Multipoint Statistics to Generate Geologically Realistic Networks

  4. Introduction (cont.) • Motivation -why carbonates? • A significant amount of the world’s hydrocarbon reserves are located in carbonate formations. • Particular interest to the petroleum industry. • Objectives • Develop a statistical methodology to generate geologically realistic networks asinput for pore-scale modelling Multipoint Statistics to Generate Geologically Realistic Networks

  5. Brief overview of current reconstruction method • Almost all the targets have been sandstones. • Reconstruction approaches • Stochastic reconstruction • Gaussian field reconstruction • Simulated annealing reconstruction • Process based reconstruction - sedimentation, compaction and diagenesis model Multipoint Statistics to Generate Geologically Realistic Networks

  6. Results generated by published methods MicroCT Process-based Gaussian-field Simulated Annealing (Biswal B., Manwart C., Hilfer R., Bakke S. and Oren, P.-E., 1999) Multipoint Statistics to Generate Geologically Realistic Networks

  7. Percolation probabilities- a quantitative characterization of the connectivity Let K (r, L) denote a cube of sidelength L centered at the lattice vector r. Percolation probabilities are measured by changing L of a cube. (Biswal B.et al, 1999) Multipoint Statistics to Generate Geologically Realistic Networks

  8. Our methodology -Multiple-point statistics model • Process-based method – more realistic but difficult for most carbonates • Traditional two-point statistics – fail to reproduce the long-range connectivity • Introduce multiple-point statistical technique to pore-scale modelling • Start on sandstone before tackling carbonates Multipoint Statistics to Generate Geologically Realistic Networks

  9. Multiple-point statistics • Use of training images • At the field scale, typical for petroleum geostatistics, is the scarcity of hard data, then training data sets such as outcrops are borrowed. • In pore-scale modelling, 2D thin-sections can provide multiple-point statistics that describe the relation between multiple spatial locations. Multipoint Statistics to Generate Geologically Realistic Networks

  10. Process of reconstruction • Overview • pattern extraction • pattern recognition • pattern reproduction training image (2D thin-section) or template Multipoint Statistics to Generate Geologically Realistic Networks

  11. Pattern extraction u4 u1 u2 u? u3 ? Probability 75% matrix, 25% pore Multipoint Statistics to Generate Geologically Realistic Networks

  12. Expanded templates Multipoint Statistics to Generate Geologically Realistic Networks

  13. Pattern recognition & reproduction u1 u2 u4 u? u3 1 1 u? u? 1 2 u? 1 1 1 2 2 u? u? 1 1 u? 2 2 u? 2 u? 3 Infer cpdf from training image reproduction If this pattern is missing in the training image, drop furthest away datum Multipoint Statistics to Generate Geologically Realistic Networks

  14. Preliminary results for sandstone Fontainebleau SS(MicroCT) Realization Multipoint Statistics to Generate Geologically Realistic Networks

  15. Percolation probabilities of realizations Multipoint Statistics to Generate Geologically Realistic Networks

  16. Future work • Need further study: noise, preserving porosity, suitable template • Expand sample size • Carbonates • The statistical and direct imaging methods can be used interchangeably Multipoint Statistics to Generate Geologically Realistic Networks

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