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Quantifying Asymmetric Parameter Interactions in Sensitivity Analysis: Application to Reservoir Modeling

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Abstract

In this paper, a new generalized sensitivity analysis is developed with a focus on parameter interaction. The proposed method is developed to apply to complex reservoir systems. Most critical in many engineering applications is to find which model parameters and parameter combinations have a significant impact on the decision variables. There are many types of parameters used in reservoir modeling, e.g., geophysical, geological and engineering. Some parameters are continuous, others discrete, and others have no numerical value and are scenario-based. The proposed generalized sensitivity analysis approach classifies the response/decision variables into a limited set of discrete classes. The analysis is based on the following principle: if the parameter frequency distribution is the same in each class, then the model response is insensitive to the parameter, while differences in the frequency distributions indicate that the model response is sensitive to the parameter. Based on this simple idea, a new general measure of sensitivity is developed. This sensitivity measure quantifies the sensitivity to parameter interactions, and incorporates the possibility that these interactions can be asymmetric for complex reservoir modeling. The approach is illustrated using a case study of a West Africa offshore oil reservoir.

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Acknowledgments

The authors would like to thank Chevron for permission to use the WCA model, and Alexandre Boucher of AR2Tech for his support in the use of SGems for modeling the petrophysical properties of WCA. The authors would also like to thank the sponsors of the Streamsim/Stanford HM JIP, the Swiss National Science Foundation (ENSEMBLE project), and the Stanford Center for Reservoir Forecasting for their financial support. The Matlab code for DGSA is available at https://github.com/SCRFpublic.

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Authors and Affiliations

  1. Streamsim Technologies, Inc, 204 Ramona St, Palo Alto, CA, 94301, USA

    Darryl Fenwick

  2. Department of Energy Resources Engineering, Stanford University, Stanford, CA, 94305, USA

    Céline Scheidt & Jef Caers

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  1. Darryl Fenwick
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  2. Céline Scheidt
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  3. Jef Caers
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Correspondence to Céline Scheidt.

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Fenwick, D., Scheidt, C. & Caers, J. Quantifying Asymmetric Parameter Interactions in Sensitivity Analysis: Application to Reservoir Modeling. Math Geosci 46, 493–511 (2014). https://doi.org/10.1007/s11004-014-9530-5

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  • DOI: https://doi.org/10.1007/s11004-014-9530-5

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