Improving Offshore Platform Production With Artificial Intelligence

P. Herve
{"title":"Improving Offshore Platform Production With Artificial Intelligence","authors":"P. Herve","doi":"10.4043/31073-ms","DOIUrl":null,"url":null,"abstract":"\n The oil and gas sector is facing a changing market with new pressures to which it must learn to adapt. One of the biggest changes in expectations is the increased focus being placed on carbon emissions. Many consumers, investors, and lawmakers see reforms to the oil and gas industry as one of the most important avenues toward reducing carbon emissions and curbing climate change, and accordingly, a large number of companies have already made ambitious pledges towards carbon neutrality.\n New technologies may offer the best avenue for oil and gas companies to reduce their carbon emissions and meet those neutrality goals. Digital technologies—and in particular, artificial intelligence—can aid in decarbonization even with relatively small investments, primarily by enabling large increases in efficiency and reducing unscheduled downtime and the need for flaring. This paper discusses how artificial intelligence-powered predictive maintenance can be applied to reduce carbon emissions, and a case study illustrating a real-world deployment of this technology.","PeriodicalId":10936,"journal":{"name":"Day 2 Tue, August 17, 2021","volume":"150 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, August 17, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4043/31073-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The oil and gas sector is facing a changing market with new pressures to which it must learn to adapt. One of the biggest changes in expectations is the increased focus being placed on carbon emissions. Many consumers, investors, and lawmakers see reforms to the oil and gas industry as one of the most important avenues toward reducing carbon emissions and curbing climate change, and accordingly, a large number of companies have already made ambitious pledges towards carbon neutrality. New technologies may offer the best avenue for oil and gas companies to reduce their carbon emissions and meet those neutrality goals. Digital technologies—and in particular, artificial intelligence—can aid in decarbonization even with relatively small investments, primarily by enabling large increases in efficiency and reducing unscheduled downtime and the need for flaring. This paper discusses how artificial intelligence-powered predictive maintenance can be applied to reduce carbon emissions, and a case study illustrating a real-world deployment of this technology.
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
利用人工智能改善海上平台生产
油气行业正面临着一个不断变化的市场,它必须学会适应新的压力。期望的最大变化之一是人们越来越关注碳排放。许多消费者、投资者和立法者将石油和天然气行业的改革视为减少碳排放和遏制气候变化的最重要途径之一,因此,许多公司已经做出了雄心勃勃的碳中和承诺。新技术可能为石油和天然气公司提供减少碳排放和实现这些中和目标的最佳途径。数字技术,特别是人工智能,即使投资相对较少,也可以帮助实现脱碳,主要是通过大幅提高效率,减少计划外停机时间和燃除需求。本文讨论了如何应用人工智能驱动的预测性维护来减少碳排放,并通过一个案例研究说明了该技术在现实世界中的部署。
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
Pazflor Subsea Separation, Ten Years After Research on Robust and Efficient Approach for Extreme Mooring Tension Estimation for FLNG and FOWT Repurposing Oil & Gas Wells and Drilling Operations for Geothermal Energy Production Diverless Revolution - Initiatives and Preliminary Results in the Replacement of Human Diving in Submarine Inspection and Maintenance Operations Cost Effective, Digital, Fail-Safe Production Tree and Wellhead Actuator System
×
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