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

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 221411, “Accelerated Calibration and CO2 Plume Tracking at the Illinois Basin Decatur Project: A Dynamic Mode-Decomposition and Data-Assimilation Approach,” by James Omeke, SPE, Kassem Alokla, SPE, and Dimitrios Voulanas, SPE, Texas A&M University, et al. The paper has not been peer reviewed. _ 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. DMD excels in identifying key dynamic features, spatial and temporal scales, and localized regions crucial for flow behavior. It offers a data-driven method that can reproduce flow phenomena accurately with high fidelity, even in multiphase and multiscale heterogeneous porous-media scenarios. Additionally, advancements such as sparse DMD and local DMD enhance the accuracy and efficiency of DMD models. The adaptability and robustness of DMD in capturing intricate flow structures make it a preferred choice for studying flow dynamics in porous media during CO2 storage. The foundation of DMD lies in its ability to approximate the Koopman operator. The DMD technique and the Koopman operator are detailed in the complete paper. Workflow Description The steps of the workflow are detailed in the complete paper, including associated equations. Identifying permeability as the principal uncertainty in multilevel pressure measurements, the authors conducted six simulation cases using various permeability multipliers within the IBDP. These simulations, integral to the history-matching process of the used FOM, were designed to span the expected range of uncertainty. Each simulation was specifically influenced by a chosen permeability multiplier. This subsection of the complete paper details the following steps: - Reconstruction of the reduced-order model (ROM) of each FOM case using DMD - Interpolation of DMD modes and eigenvalues for pressure-trend prediction - Kalman filter optimization of permeability multipliers - History-matching of the FOM using the optimized permeability multiplier and construction of the ROM