Verification of a real-time ensemble-based method for updating earth model based on GAN

TL;DR

This paper introduces a real-time ensemble-based approach combining GANs and EnRML for updating earth models during drilling, demonstrating reliable uncertainty quantification and geosteering support.

Contribution

The study presents a novel integration of GANs with EnRML for rapid, real-time geomodel updating, addressing non-linearity and bias issues in subsurface uncertainty quantification.

Findings

EnRML effectively updates models with electromagnetic logs.

Workflow produces reliable results verified by MCMC.

Method supports real-time geosteering decisions.

Abstract

The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of geomodels, combined with Ensemble Randomized Maximum Likelihood (EnRML) for rapid updating of subsurface uncertainty. This real-time ensemble method combined with a highly non-linear model arising from neural-network modeling sequences might produce inaccurate and/or biased posterior solutions. This paper illustrates the predictive ability of EnRML on several examples where we assimilate local extra-deep electromagnetic logs. Statistical verification with MCMC confirms that the proposed workflow can produce reliable results required for geosteering wells.