工作流程
石油工程
上游(联网)
自动化
分析
石油工业
数字化转型
多学科方法
过程(计算)
工程类
大数据
下游(制造业)
组分(热力学)
商业智能
计算机科学
数据科学
系统工程
运营管理
知识管理
机械工程
数据挖掘
电信
万维网
环境工程
物理
操作系统
社会学
热力学
数据库
社会科学
作者
David Castiñeira,Hamed Darabi,Xiang Zhai,Wassim Benhallam
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2020-01-01
卷期号:: 107-141
被引量:2
标识
DOI:10.1016/b978-0-12-820028-5.00004-7
摘要
The oil and gas (O&G) industry is facing intense pressure to improve operational efficiencies as oil and gas prices continue to fluctuate. Many O&G companies are now rushing into a digital transformation race where digital technologies would be used to create new—of transform traditional—processes. This chapter focuses on the exploration and production part of the O&G industry (the upstream sector), which is primarily concerned with finding and producing crude oil and natural gas. It argues that the current analytical workflow and decision-making process are suboptimal and inadequate for full digital transformation. Higher levels of machine intelligence and automation are needed to bring extreme efficiency to O&G operations. Furthermore, augmentation (e.g., ability to infuse engineering experience into advanced analytics and data-driven solutions) is an absolute necessary component of any smart reservoir management framework, and, therefore, it is a critical element of the digital transformation itself. This chapter proposes specific methods for a more intelligent automation of the upstream business. It presents several (multidisciplinary) case studies that demonstrate the value of automated data processing, systematic engineering and geological workflows, and predictive analytics.
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