Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields

TL;DR

This paper introduces a Fourier Neural Operator surrogate model that accurately predicts reservoir flow behaviors across various permeability fields and well configurations, significantly speeding up reservoir simulations and management.

Contribution

The study presents a novel FNO-based surrogate that generalizes to unseen well settings and permeability fields, outperforming traditional simulators in speed and accuracy.

Findings

Achieves less than 5% maximum-mean relative error in pressure and saturation predictions.

Reduces dataset size by 75% using data augmentation techniques.

Enables predictions on unseen well configurations, improving reservoir management efficiency.

Abstract

Simulating Darcy flows in porous media is fundamental to understand the future flow behavior of fluids in hydrocarbon and carbon storage reservoirs. Geological models of reservoirs are often associated with high uncertainly leading to many numerical simulations for history matching and production optimization. Machine learning models trained with simulation data can provide a faster alternative to traditional simulators. In this paper we present a single Fourier Neural Operator (FNO) surrogate that outperforms traditional reservoir simulators by the ability to predict pressures and saturations on varying permeability fields, well locations, well controls, and number of wells. The maximum-mean relative error of 95\% of pressure and saturation predictions is less than 5\%. This is achieved by employing a simple yet very effective data augmentation technique that reduces the dataset size by 75\% and reduces overfitting. Also, constructing the input tensor in a binary fashion enables predictions on unseen well locations, well controls, and number of wells. Such model can accelerate history matching and reservoir characterization procedures by several orders of magnitude. The ability to predict on new well locations, well controls, and number of wells enables highly efficient reservoir management and optimization.