Decarbonization

Model-Reduction and Data-Assimilation Approach Enhances Carbon-Dioxide Plume Tracking

The authors introduce a novel framework combining dynamic mode decomposition, a data-driven model-reduction technique, with direct data assimilation to streamline the calibration of carbon-dioxide plume evolution models.

Fig. 1—The dynamic model used in this study has 546,920 cells. The map highlights the spatial distribution and structural configuration of the Illinois Basin used for CO2-storage simulations.
Fig. 1—The dynamic model used in this study has 546,920 cells. The map highlights the spatial distribution and structural configuration of the Illinois Basin used for CO2-storage simulations.
Source: SPE 221411.

Addressing climate change through carbon capture and storage (CCS) technologies requires advanced computational methodologies for subsurface carbon-dioxide (CO2) storage monitoring. This study focuses on the Illinois Basin Decatur Project (IBDP), a CCS demonstration pilot aimed at CO2 injection into a deep saline reservoir. A novel framework combining dynamic mode decomposition (DMD), a data-driven model-reduction technique, with direct data assimilation is introduced to streamline the calibration of CO2 plume evolution models. This approach enhances rapid tracking and overcomes the computational challenges of traditional high-fidelity numerical reservoir simulations known as the full-order model (FOM).

Introduction

DMD represents a superior approach for flow in porous media compared with most reduced-order models because of its ability to capture complex flow dynamics effectively.

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