人工智能支持信息发现和分析在一个SPE研究门户

E. Schoen, Reid G. Smith, John Boden
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引用次数: 2

摘要

数字化油气作业产生的原始数据量的持续增长已经得到了广泛的记录(Spath, 2014)。可能不太明显的是,该行业每年也在创作更多的非结构化内容——解读、学习、案例研究等。这并不奇怪,因为轶事证据表明,大多数决策都是基于非结构化数据(Quaadgras & Beath, 2011) (Garland, 2017) (Palkowsky, 2005) (Haines, Shaughnessy, & Briggs, 2006) (Hollingsworth & Schey, 2017)。在石油工程师协会,每年发表的新论文数量一直遵循指数增长曲线,大约每10-11年翻一番,这可以追溯到20世纪50年代初。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Supports Information Discovery and Analysis in an SPE Research Portal
Ongoing growth in the volume of raw data generated by digitized oil and gas operations has been widely documented (Spath, 2014). What may be less apparent is that the industry is also authoring dramatically more unstructured content—interpretations, learnings, case studies, etc.—on an annual basis. This is not surprising, as anecdotal evidence suggests that most decisions are taken on the basis of unstructured data (Quaadgras & Beath, 2011) (Garland, 2017) (Palkowsky, 2005) (Haines, Shaughnessy, & Briggs, 2006) (Hollingsworth & Schey, 2017). At the Society of Petroleum Engineers, the number of new papers published annually has followed an exponential growth curve that doubles approximately every 10-11 years, beginning as far back as the early 1950's.
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