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Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

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Abstract

Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data.

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Acknowledgments

The authors wish to thank Reza Shahalipour for providing useful data on this case study without which the present application was not feasible. The authors like to take this opportunity to express their sincere thanks to the editor and reviewers who identified areas of the present article that needed corrections or modifications.

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Authors and Affiliations

  1. PhD Student at Reservoir Engineering, Department of Petroleum Engineering, Amirkabir University of Technology, P.O. Box 158754413, Tehran, Iran

    Rouhollah Ahmadi

  2. Faculty of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran

    Ehsan Khamehchi

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  1. Rouhollah Ahmadi
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  2. Ehsan Khamehchi
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Correspondence to Rouhollah Ahmadi.

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Ahmadi, R., Khamehchi, E. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks. Nat Resour Res 22, 321–336 (2013). https://doi.org/10.1007/s11053-013-9221-9

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  • DOI: https://doi.org/10.1007/s11053-013-9221-9

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