Boosting Barlow Twins Reduced Order Modeling for Machine Learning-Based Surrogate Models in Multiphase Flow Problems

Abstract

We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT-ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT-ROM builds upon Barlow Twins reduced order modeling that leverages self-supervised learning to effectively handle linear and nonlinear manifolds by constructing well-structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT-ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT-ROM, we conduct three three-dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT-ROM excels at predicting non-wetting phase saturation (e.g., oil or $\mathrm{C}{\mathrm{O}}_{\mathrm{2}}$ saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT-ROM showcases robustness when faced with limited input parameter space during GCS testing.