Rapid Time-Lapse Monitoring of Geological Carbon Storage

Abstract

Abstract Precision monitoring of the subsurface carbon-dioxide plume ensures long-term, sustainable geological carbon storage at a large scale. Electrical resistivity tomography (ERT) can accurately map the evolution of the CO2 saturation during geological carbon storage. To better monitor the CO2 plume migration in a storage reservoir, we develop an unsupervised spatiotemporal clustering to process the CO2 saturation maps derived from the ERT measurements acquired over 80 days. Using dynamic time wrapping (DTW) Kmeans clustering, four distinct clusters were identified in the CO2-storage reservoir. The four clusters exhibit Davies-Bouldin (DB) index of 0.71, Calinski-Harabasz (CH) index of 262791, and DTW-silhouette score of 0.58. Unlike traditional clustering methods, the DTW K-means incorporates a temporal distance metric. Compared to DTW KMeans, traditional clustering methods, such as agglomerative and meanshift clustering, exhibit a lower performance with DB index of 0.95 and 1.01, respectively, and CH index of 131593 and 69438, respectively. Statistical analysis indicates that contrast stretching and fast-Fourier transform are strong geophysical signatures of the spatiotemporal evolution of CO2 plume. We also identified a strong correlation between injection flow rate and the spatial evolution of high CO2 content. A tensor-based feature extraction was critical for capturing both the temporal and spatial components relevant to the evolution of CO2 plume in storage reservoir.