Improving Sequential Simulation with a Structured Path Guided by Information Content |
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| Authors: | Yuhong Liu and Andre Journel |
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| Institution: | (1) Stanford Center for Reservoir Forecasting, Department of Geological and Environmental Sciences, Stanford University, Stanford, California, 94305 |
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| Abstract: | Multiple-point simulation is a newly developed geostatistical method that aims at combining the strengths of two mainstream geostatistical methods: object-based and pixel-based methods. It maintains the flexibility of pixel-based algorithms in data conditioning, while enhancing its capability of reproducing realistic geological shapes, which is traditionally reserved to object-based algorithms. However, the current snesim program for multiple-point simulation has difficulty in reproducing large-scale structures, which have a significant impact on the flow response. To address this problem, we propose to simulate along a structured path based on an information content measure. This structured path accounts for not only the information from the data, but also some prior structural information provided by geological knowledge. Various case studies show a better reproduction of large-scale structures. This concept of simulating along a structured path guided by information content can be applied to any sequential simulation algorithms, including traditional variogram-based two-point geostatistical algorithms. |
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| Keywords: | random path multiple-point geostatistics post-processing snesim long-range continuity |
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