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

TL;DR

This study demonstrates an open-source, image-based history matching workflow for CO$_2$ storage using the FluidFlower benchmark data. It integrates high-resolution experimental observations with a reservoir simulator (OPM Flow) and a differential-evolution HM framework (Everest) via the pofff toolkit, employing the Wasserstein distance as the objective. The HM calibrates 24 parameters across six sands on a coarse 2 cm grid, achieving good agreement with experimental CO$_2$ distributions and outperforming several benchmark results in dense and sparse-data metrics. The work emphasizes reproducibility through open-source software, highlights grid-dependence and segmentation sensitivity as key considerations, and provides a scalable workflow for future experimental campaigns in geological carbon storage research.

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.

Figures (12)

• Figure 1: The FluidFlower benchmark geometry. Figure courtesy of ferno2024
• Figure 2: Photographs from the CO$_2$ injection experiment (C1) showing contours of dissolved CO$_2$ (green) and gaseous CO$_2$ (yellow). a) Injection into the lower well only. b) Injection into both the lower and upper wells. c) Post-injection phase after both wells were shut down
• Figure 3: (a) Corner-point grid representation of the FluidFlower geological model. (b) Measured thickness map of the FluidFlower
• Figure 4: Spatial distribution of dissolved CO$_2$ from the OPM simulation results in case SPE11a, which closely resembles the FluidFlower experimental system
• Figure 5: (a-c) Photographs from the FluidFlower experiment C2 at one, three, and five days, respectively. (d-e) Spatial maps of CO$_2$ concentration from the corresponding simulation results. (f-g) Contour plots comparing experimental observations and segmented simulation outputs at the same time intervals