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Ensemble-based Kernel Learning to Handle Rock-physics-model Imperfection in Seismic History Matching: A Real Field Case Study
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Petroleum Geostatistics 2019, Sep 2019, Volume 2019, p.1 - 5
Abstract
Summary
Model imperfection is ubiquitous in geophysical data assimilation problems. A common approach to accounting for model errors is to treat them as random variables following presumed distributions. While such a treatment renders certain algorithmic convenience, its underpinning assumptions may often be invalid in practice. In this study, we adopt an alternative approach, and treat the characterization of model errors as a functional approximation problem, which can be solved using a generic machine learning method, such as kernel-based learning adopted here. To enable a seamless integration of kernel-based learning into ensemble data assimilation, we also develop an ensemble-based kernel learning approach. We show that an existing iterative ensemble smoother can be naturally employed as the learning algorithm, which thus inherits all the practical advantages of ensemble data assimilation algorithms, such as derivative-free, fast implementation, and allowing uncertainty quantification and applications to large scale problems. To demonstrate the efficacy of ensemble-based kernel learning, we apply it to handle model errors in a rock physics model used in history matching real 4D seismic data from the full Norne field. Our experiment results indicate that incorporating kernel-based model error correction into 4D seismic history matching helps improve the qualities of estimated reservoir models, and leads to better forecasts of production data.
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