Drilling activities in the oil and gas industry have been reported over decades for thousands of wells on a daily basis, yet the analysis of this text at large-scale for information retrieval Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing Julio Hoffimann, Youli Mao, Avinash Wesley, and Aimee Taylor´ Abstract—Drilling activities in the oil and gas industry have been reported over decades for thousands of wells on a daily basis, yet the analysis of this text at large-scale for informa- Advanced Drilling Techniques Horizontal Drilling. Horizontal drilling starts with a vertical well that turns horizontal within the reservoir rock in order to expose more open hole to the oil. These horizontal "legs" can be over a mile long; the longer the exposure length, the more oil and natural gas is drained and the faster it can flow. More The internet of things, sensor data and applications associated with machine learning in oil & gas allow for information to be accessed across multiple touchpoints. Benjamin Beberness, vice president and global head of the Oil and Gas Industry Business Unit at SAP, highlighted the importance of machine learning in the oil & gas industry. The proposed natural-language processing (#NLP) techniques in this paper, allow unstructured data to be searched, organized, and mined, allowing engineers to leverage the underlying insights without having to read through entire databases. “Natural Language Processing Techniques on Oil and Gas Drilling Data” set out how Maana and Chevron trained a machine to understand how drillers describe problems they encountered in operations. This enables well planning engineers to get a better understanding of potential risks associated with drilling a well by seeing how often a problem
1 Oct 2017 Natural-Language-Processing Techniques for Oil and Gas Drilling Data of hypothesized and realized risks to oil wells described in two data
Recent advances in search, machine learning, and natural language processing have made it possible to extract structured information from free text, providing a new and largely untapped source of insights for well and reservoir planning. However, there are major challenges involved in applying these techniques to data that is messy and/or This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181015, “Natural-Language-Processing Techniques on Oil and Gas Drilling Data,” by M. Antoniak, J. Dalgliesh, SPE, and M. Verkruyse, Maana, and J. Lo, Chevron, prepared for the 2016 SPE Intelligent Energy International Conference and Exhibition, Aberdeen, 6–8 September. We look at some of the use-cases where AI is being applied for data search and data discovery in the energy and oil and gas sectors below. Natural Language Processing Techniques for Oil and Gas Drilling Data. The oil and gas industry is usually divided into three major operational sectors: upstream, midstream, and downstream. Upstream involves The proposed natural-language processing (#NLP) techniques in this paper, allow unstructured data to be searched, organized, and mined, allowing engineers to leverage the underlying insights without having to read through entire databases. SPE 181015 Natural Language Processing Techniques on Oil and Gas Drilling Data by M. Antoniak, Maana, et al. OTC 27577 Assessment of Data-Driven Machine-Learning Techniques for Machinery Prognostics of Offshore Assets by Ping Lu, American Bureau of Shipping, et al. SPE 181037 Big Data Analytics for Prognostic Foresight by Moritz von Plate Drilling activities in the oil and gas industry have been reported over decades for thousands of wells on a daily basis, yet the analysis of this text at large-scale for information retrieval
20 Feb 2015 ML for energy applications differs dramatically from consumer web applications. with the optimal drilling and completion techniques for economically domains, including natural language processing, computer vision, web
“Natural Language Processing Techniques on Oil and Gas Drilling Data” set out how Maana and Chevron trained a machine to understand how drillers describe problems they encountered in operations. This enables well planning engineers to get a better understanding of potential risks associated with drilling a well by seeing how often a problem
