Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

2026-02-25

AI Summary

This study addresses the high computational cost of high-fidelity numerical simulations of rock–fluid interactions, which hinders their application in multi-query tasks such as uncertainty quantification and optimization. To overcome this limitation, the authors develop eight deep learning surrogate models, including dual-neural-network-based reduced-order models and single-network image-to-image architectures with grid-size invariance. A novel grid-size invariance framework is introduced, enabling the models to generalize to computational grids larger than those used during training. Notably, this work presents the first application of UNet++ in this domain. Experimental results demonstrate that the proposed approaches significantly reduce training memory consumption while maintaining high prediction accuracy under non-stationary solid-field conditions, outperforming both conventional reduced-order models and standard UNet baselines.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces. Conventional high-fidelity numerical models require a high resolution to obtain reliable results, resulting in huge computational expense. This restricts the applicability of these models for multi-query problems, such as uncertainty quantification and optimisation, which require running numerous scenarios. As a cheaper alternative to high-fidelity models, this work develops eight surrogate models for predicting the fluid flow in porous media. Four of these are reduced-order models (ROM) based on one neural network for compression and another for prediction. The other four are single neural networks with the property of grid-size invariance; a term which we use to refer to image-to-image models that are capable of inferring on computational domains that are larger than those used during training. In addition to the novel grid-size-invariant framework for surrogate models, we compare the predictive performance of UNet and UNet++ architectures, and demonstrate that UNet++ outperforms UNet for surrogate models. Furthermore, we show that the grid-size-invariant approach is a reliable way to reduce memory consumption during training, resulting in good correlation between predicted and ground-truth values and outperforming the ROMs analysed. The application analysed is particularly challenging because fluid-induced rock dissolution results in a non-static solid field and, consequently, it cannot be used to help in adjustments of the future prediction.

Problem

• rock-fluid interaction
• surrogate models
• computational expense
• multi-query problems
• porous media

Innovation

• grid-size-invariant
• surrogate modeling
• rock-fluid interaction
• UNet++
• reduced-order models