Machine Learning-Driven Quantification of CO 2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

Ikponmwosa Iyegbekedo, Ebrahim Fathi (), Timothy R. Carr and Fatemeh Belyadi
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Ikponmwosa Iyegbekedo: Department of Petroleum and Natural Gas Engineering, West Virginia University, Morgantown, WV 26506, USA
Ebrahim Fathi: Department of Petroleum and Natural Gas Engineering, West Virginia University, Morgantown, WV 26506, USA
Timothy R. Carr: Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA
Fatemeh Belyadi: Obsertelligence LLC, Aubrey, TX 76227, USA

Energies, 2024, vol. 17, issue 17, 1-17

Abstract: This study utilizes machine learning to quantify CO 2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO 2 injection metrics, this work aims to predict the temporal evolution of subsurface CO 2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO 2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO 2 saturation plume extension is significant for real-time monitoring and management of CO 2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO 2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Keywords: machine learning; CO 2 plume dynamics; microseismic data; Illinois Basin Decatur Project (IBDP); carbon sequestration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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