Surrogate Model For Field Optimization Using Beta-VAE Based Regression

TL;DR

This paper introduces a beta-VAE based regression surrogate model for reservoir simulation optimization, offering interpretability and uncertainty quantification to improve decision-making in oilfield development.

Contribution

It proposes a novel surrogate modeling approach using beta-VAE for interpretable latent representations and probabilistic layers for uncertainty quantification in reservoir simulations.

Findings

The surrogate provides interpretable latent space representations.

Uncertainty quantification guides when full simulations are needed.

Beta-VAE balances factor disentanglement and reconstruction quality.

Abstract

Oilfield development related decisions are made using reservoir simulation-based optimization study in which different production scenarios and well controls are compared. Such simulations are computationally expensive and so surrogate models are used to accelerate studies. Deep learning has been used in past to generate surrogates, but such models often fail to quantify prediction uncertainty and are not interpretable. In this work, beta-VAE based regression is proposed to generate simulation surrogates for use in optimization workflow. beta-VAE enables interpretable, factorized representation of decision variables in latent space, which is then further used for regression. Probabilistic dense layers are used to quantify prediction uncertainty and enable approximate Bayesian inference. Surrogate model developed using beta-VAE based regression finds interpretable and relevant latent representation. A reasonable value of beta ensures a good balance between factor disentanglement and reconstruction. Probabilistic dense layer helps in quantifying predicted uncertainty for objective function, which is then used to decide whether full-physics simulation is required for a case.