Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. We show an example where, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO₂ leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.. Authors: Hanna, Alexander C. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000317624568); Whiting, Jonathan M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000194364670); Huang, Brian Z. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Appriou, Delphine [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000192830578); Yang, Xianjin [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000158419769). DOE Contract: AC05-76RL01830; AC52-07NA27344. Subjects: 58 GEOSCIENCES; carbon sequestration; genetic algorithms; gravity surveys; optimization; subsurface monitoring design