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Seismic Estimation of Sub-resolution Reservoir Properties with Bayesian Evidential Learning: Application to an Offshore Delta Case
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Petroleum Geostatistics 2019, Sep 2019, Volume 2019, p.1 - 5
Abstract
Summary
We present a Bayesian framework that facilitates estimation of and uncertainty quantification of sub-resolution reservoir properties from seismic data. Our workflow exploits learning the direct relation between seismic data and reservoir properties by the evidential learning approach to efficiently estimate sub-resolution properties. The major focus of this paper is on a real case study of seismic characterization of reservoir layers significantly below the seismic resolution. We employ deep neural networks as summary statistics within Approximate Bayesian Computation to estimate posterior uncertainty of reservoir net-to-gross from 3D pre-stack seismic data without an explicit inversion.
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