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
{"title":"ENHANCING CARBON CAPTURE AND STORAGE EFFICIENCY IN THE OIL AND GAS SECTOR: AN INTEGRATED DATA SCIENCE AND GEOLOGICAL APPROACH","authors":"Wags Numoipiri Digitemie, Ifeanyi Onyedika Ekemezie","doi":"10.51594/estj.v5i3.947","DOIUrl":null,"url":null,"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. \nKeywords: Carbon, Storage, Efficiency, Capture, Oil & Gas, Predictive, Modeling.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":" 15","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Science & Technology Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51594/estj.v5i3.947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Optimization of microgrid operations using renewable energy sources Project management tools in renewable energy integration: A review of U.S. perspectives Reviewing the role of bioenergy with carbon capture and storage (BECCS) in climate mitigation Advances in rock physics for pore pressure prediction: A comprehensive review and future directions Next-Generation strategies to combat antimicrobial resistance: Integrating genomics, CRISPR, and novel therapeutics for effective treatment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1