A practical and efficient approach for Bayesian reservoir inversion: Insights from the Alvheim field data

TL;DR

This paper presents a practical Bayesian reservoir inversion method that significantly reduces computational costs using adaptive regression splines, FFT, and advanced sampling, validated on real Alvheim field data.

Contribution

It introduces a computationally efficient Bayesian inversion framework combining regression splines, FFT, and preconditioned sampling for reservoir characterization.

Findings

34-fold acceleration in computational efficiency

Effective application to Alvheim field data

Enhanced sampling in high-dimensional spaces

Abstract

Stochastic reservoir characterization, a critical aspect of subsurface exploration for oil and gas reservoirs, relies on stochastic methods to model and understand subsurface properties using seismic data. This paper addresses the computational challenges associated with Bayesian reservoir inversion methods, focusing on two key obstacles: the demanding forward model and the high dimensionality of Gaussian random fields. Leveraging the generalized Bayesian approach, we replace the intricate forward function with a computationally efficient multivariate adaptive regression splines method, resulting in a 34 acceleration in computational efficiency. For handling high-dimensional Gaussian random fields, we employ a fast Fourier transform (FFT) technique. Additionally, we explore the preconditioned Crank-Nicolson method for sampling, providing a more efficient exploration of high-dimensional parameter spaces. The practicality and efficacy of our approach are tested extensively in simulations and its validity is demonstrated in application to the Alvheim field data.