Performance of an open-source image-based history matching framework for CO$_2$ storage

TL;DR

This paper demonstrates an open-source workflow for history matching of CO$_2$ storage using high-resolution images and the OPM Flow simulator, achieving good data-model agreement and showcasing the effectiveness of the Wasserstein distance as a matching metric.

Contribution

It introduces a novel open-source Python workflow that integrates high-resolution experimental data with standard simulation tools for CO$_2$ storage history matching.

Findings

Good agreement between simulation and experimental data after history matching.

Wasserstein distance effectively matches multi-phase flow data.

Workflow is reproducible and extendable via a single input file.

Abstract

We present a history matching (HM) workflow applied to the International FluidFlower benchmark study dataset, which features high-resolution images of CO$_2$ storage in a meter-scale, geologically complex reservoir. The dataset provides dense spatial and temporal observations of fluid displacement, offering a rare opportunity to validate and enhance HM techniques for geological carbon storage (GCS). The combination of detailed experimental data and direct visual observation of flow behavior at this scale is novel and valuable. This study explores the potential and limitations of using experimental data to calibrate standard models for GCS simulation. By leveraging high-resolution images and resulting interpretations of fluid phase distributions, we adjust uncertain parameters and reduce the mismatch between simulation results and observed data. Simulations are performed using the open-source OPM Flow simulator, while the open-source Everest decision-making tool is employed to conduct the HM. After the HM process, the final simulation results show good agreement with the experimental CO$_2$ storage data. This suggests that the system can be effectively described using standard flow equations, conventional saturation functions, and typical PVT properties for CO$_2$-brine mixtures. Our results demonstrate that the Wasserstein distance is a particularly effective metric for matching multi-phase, multi-component flow data. The entire workflow is implemented in a Python package named pofff (Python OPM Flow FluidFlower), which organizes all functionality through a single input file. This design ensures reproducibility and facilitates future extensions of the study.