ENHANCING CARBON CAPTURE AND STORAGE EFFICIENCY IN THE OIL AND GAS SECTOR: AN INTEGRATED DATA SCIENCE AND GEOLOGICAL APPROACH

Wags Numoipiri Digitemie, Ifeanyi Onyedika Ekemezie
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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.
提高石油天然气行业的碳捕集与封存效率:数据科学与地质学综合方法
本文提出了一个新颖的框架,将数据科学技术与地质见解相结合,以优化石油和天然气行业的碳捕集与封存(CCS)流程。通过利用机器学习算法、地理空间数据分析和预测建模,该研究旨在确定碳封存的最佳地质构造、预测碳封存能力并最大限度地减少对环境的影响。该研究将应对数据异质性、可扩展性和地质变量复杂性等挑战,旨在为化石燃料领域的可持续碳管理提供全面的解决方案。关键词碳、存储、效率、捕获、石油和天然气、预测、建模。
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