Abstract
This paper proposes a novel framework that integrates data science techniques with geological insights to optimize carbon capture and storage (CCS) processes in the oil and gas industry. By leveraging machine learning algorithms, geospatial data analysis, and predictive modeling, the study aims to identify optimal geological formations for carbon storage, predict carbon sequestration capacities, and minimize environmental impacts. The research will address the challenges of data heterogeneity, scalability, and the complexity of geological variables, aiming to provide a comprehensive solution for sustainable carbon management in the fossil fuel sector. Keywords: Carbon, Storage, Efficiency, Capture, Oil & Gas, Predictive, Modeling.
Cite
CITATION STYLE
Wags Numoipiri Digitemie, & Ifeanyi Onyedika Ekemezie. (2024). ENHANCING CARBON CAPTURE AND STORAGE EFFICIENCY IN THE OIL AND GAS SECTOR: AN INTEGRATED DATA SCIENCE AND GEOLOGICAL APPROACH. Engineering Science & Technology Journal, 5(3), 924–934. https://doi.org/10.51594/estj.v5i3.947
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