Seismic Bayesian evidential learning: Estimation and uncertainty quantification of sub-resolution reservoir properties
This work addresses the challenge of efficiently estimating reservoir properties like net-to-gross and fluid saturations from seismic data for geophysical applications, representing an incremental advancement in domain-specific methods.
The authors tackled the problem of estimating sub-resolution reservoir properties from seismic data without solving high-dimensional inverse problems, developing a Bayesian evidential learning framework that directly relates seismic data to reservoir properties and demonstrated its efficacy on synthetic and offshore delta datasets with uncertainty quantification.
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.