A Machine-Learned Near-Well Model in OPM Flow

TL;DR

This paper introduces a novel integration of neural networks into the OPM Flow reservoir simulator, enabling efficient hybrid modeling of near-well phenomena with high accuracy and low computational cost.

Contribution

It presents the first integration of neural networks into OPM Flow, allowing seamless hybrid modeling and introduces a data-driven near-well model trained on fine-scale simulations.

Findings

Neural networks can accurately replicate fine-scale near-well pressure gradients.

The integrated model achieves high fidelity at reduced computational expense.

Demonstrated effectiveness in CO₂ storage simulation scenarios.

Abstract

Recent advances in reservoir simulation increasingly utilize hybrid approaches that couple physics-based simulators with machine-learning (ML) components. ML components offer high fidelity to training data and fast inference, enabling efficient and accurate modeling of complex multi-scale or multi-physics phenomena. Modern reservoir simulators rely on automatic differentiation (AD) to support efficient and flexible strategies for nonlinear solvers, inverse problems, and optimization problems. Efficient hybrid modeling therefore requires tight integration of the ML components with the simulator's AD framework. We present the first integration of neural networks into the high-performance reservoir simulator OPM Flow. Networks are trained in TensorFlow and imported into OPM, where they are accessed as native AD functions. This presents an efficient framework for hybrid modeling and enables seamless integration in existing simulator workflows. As an application, we introduce a novel, data-driven near-well model. Near-well models are essential in reservoir simulation for accurately representing singular pressure gradients around wells. Commonly used are the Peaceman near-well model and its extensions, or local grid refinement around the wells. Peaceman-type models are limited to simplified flow regimes, whereas local grid refinement is computationally expensive. We address these limitations by training a neural network to infer a Peaceman-like well index from fine-scale ensemble simulations of the near-well region. It is then integrated into OPM Flow with the new framework. Tested on relevant examples for CO$_2$ storage, the method offers high fidelity to fine-scale results at low computational cost, demonstrating the potential of the OPM Flow-Neural Network framework for hybrid modeling.