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Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties

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Abstract

We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties. The theoretical framework we develop allows incorporation of non-linear statistical models for seismic estimation problems. Uncertainty quantification is performed with approximate Bayesian computation. With the help of a synthetic example of estimation of reservoir net-to-gross and average fluid saturations in sub-resolution thin sand reservoir, several nuances are foregrounded regarding the applicability of unsupervised and supervised learning methods for seismic estimation problems. Finally, we demonstrate the efficacy of our approach by estimating posterior uncertainty of reservoir net-to-gross in sub-resolution thin sand reservoir from an offshore delta dataset using 3D pre-stack seismic data.

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Acknowledgements

This work is supported by the sponsors of the Stanford Center for Earth Resources Forecasting (SCERF), and support from the Dean of the Stanford School of Earth, Energy, and Environmental Sciences, Professor Steve Graham. We thank Edison E&P for providing the dataset for the real case application. We offer special thanks to Dr. Fabio Ciabarri for his advice and assistance regarding various aspects of the real dataset. We would also like to thank Professor Jef Caers for insightful discussions regarding the methodology.

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

    Anshuman Pradhan & Tapan Mukerji

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  1. Anshuman Pradhan
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  2. Tapan Mukerji
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Correspondence to Anshuman Pradhan.

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Pradhan, A., Mukerji, T. Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties. Comput Geosci 24, 1121–1140 (2020). https://doi.org/10.1007/s10596-019-09929-1

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