Accelerated Calibration and CO2 Plume Tracking at the Illinois Basin Decatur Project: A Dynamic Mode Decomposition and Data Assimilation Approach

Abstract Addressing climate change through carbon capture and storage (CCS) technologies necessitates advanced computational methodologies for subsurface 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. We introduce a novel framework combining Dynamic Mode Decomposition (DMD), a data-driven model reduction technique, with direct data assimilation 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). Using DMD, we analyzed five distinct FOM simulation cases of the IBDP with varied permeability in the Mt. Simon section to develop reduced-order models (ROMs). These ROMs utilize three state variables: reservoir pressure, CO2 plume saturation, and bottom-hole pressure (BHP) from a CO2 injection well alongside multi-level pressures from a monitoring well derived from the FOM and the actual field data respectively. Initial FOM simulation cases assessed the impact of permeability multipliers on pressure responses. We then transformed these into ROMs using DMD, preserving essential dynamics. Linear interpolation between permeabilities and DMD outputs—modes and eigenvalues—established relationships for rapid BHP prediction under different scenarios. Employing a Kalman filter, we optimized a global permeability multiplier, using the ROMs, to align measured and simulated BHP values, ensuring model calibration. The final calibrated FOM was further decomposed to a DMD-based ROM, enabling quick, accurate predictions, significantly reducing computational time from hours to minutes. Utilizing an ROM derived through DMD, we achieved an order of 160 reduction in computational time (from 8 hours to just 3 minutes) for a 3-year historical CO2 injection period modeled with 547,000 cells of the FOM. The ROM demonstrated remarkable fidelity, with a mean absolute error of 1.46 psi for pressure and 3.7e-05 for CO2 plume saturation, effectively capturing the dynamics of the full-order model. This substantial decrease in computational time illustrates an advantageous trade-off between speed and accuracy, optimizing the potential for long-term forecasting and monitoring of CO2 sequestration. Incorporating the IBDP as a case study, this research contributes a significant advancement to reservoir simulation practices, offering a potent, efficient tool for CCS monitoring. By integrating DMD for ROM construction with precise data assimilation-based calibration, the study provides a comprehensive solution for swift and accurate subsurface CO2 plume tracking, essential for the successful implementation of CCS projects and the broader effort to mitigate climate change impacts.