Data Conditioning for Subsurface Models with Single-Image Generative Adversarial Network (SinGAN)

TL;DR

This paper introduces a comprehensive data conditioning and quality assessment framework for subsurface models using Single-Image GANs, enhancing the reliability of geological pattern reproduction and uncertainty analysis.

Contribution

It proposes new minimum acceptance criteria and multi-scale conditioning checks for GAN-based subsurface modeling, improving model plausibility and geological consistency.

Findings

Effective static and dynamic data conditioning checks

Improved reproduction of geological features and patterns

Guidelines for deep learning model conditioning in subsurface modeling

Abstract

The characterization of subsurface models relies on the accuracy of subsurface models which request integrating a large number of information across different sources through model conditioning, such as data conditioning and geological concepts conditioning. Conventional geostatistical models have a trade-off between honoring geological conditioning (i.e., qualitative geological concepts) and data conditioning (i.e., quantitative static data and dynamic data). To resolve this limit, generative AI methods, such as Generative adversarial network (GAN), have been widely applied for subsurface modeling due to their ability to reproduce complex geological patterns. However, the current practices of data conditioning in GANs conduct quality assessment through ocular inspection to check model plausibility or some preliminary quantitative analysis of the distribution of property of interests. We propose the generative AI realization minimum acceptance criteria for data conditioning, demonstrated with single image GAN. Our conditioning checks include static small-scale local and large-scale exhaustive data conditioning checks, local uncertainty, and spatial nonstationarity reproduction checks. We also check conditioning to geological concepts through multiscale spatial distribution, the number of connected geobodies, the spatial continuity check, and the model facies proportion reproduction check. Our proposed workflow provides guidance on the conditioning of deep learning methods for subsurface modeling and enhanced model conditioning checking essential for applying these models to support uncertainty characterization and decision making.