Data & Analytics
This paper presents a smart safety monitoring system to prevent accidents in environments with moving machinery at use on various global rigs.
The Energy and AI Observatory aims to use up-to-date information on energy demand from data centers to determine how artificial intelligence is optimizing the energy sector.
The company is making available its data on ocean and weather conditions in an effort to boost transparency and innovation.
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With oil prices on the rise and calls for an energy transition growing louder, it's easy to get mixed signals about the future of the oil and gas business. SPE's TD for Production & Facilities Bob Pearson outlines the way forward for the discipline. -
SPE’s 2021 Open Subsurface workshop tackled the ins and outs of open source, open data, and open access.
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For the foreseeable future, going beyond the barrel will really mean maximizing returns from the barrel, including the identification and harnessing of potential gains from capital planning, asset management, and operations.
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The goal of this paper is to aid oilfield security planning and design processes through improved recognition of the cyber-physical security effects arising from the implementation of the industrial Internet of Things.
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Thousands of satellite images were scrutinized by monitoring company Kayrros to identify ultra-emitters of methane, greenhouse-gas sources that cannot be detected by terrestrial monitors. Up to 150 methane plumes a month were seen, some spreading for hundreds of kilometers.
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TAQ Energy, an oilfield abandonment service company, and Engage Mobilize announced a partnership to develop a cloud-based operational and financial platform.
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Accenture was selected by Aker BP to develop a data factory in collaboration with Cognite. The cloud-based project has the goal of digitalizing the full lifecycle of the company’s operations to cut costs, improve productivity, and lower its carbon footprint.
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The authors describe an integrated multiscale data methodology involving machine-leaning tools applied to the Late Jurassic Upper Jubaila formation outcrop data.
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This paper describes the application of a synthetic seismic-catalog-generation method followed by application of a neural network on a seismic data set for an oil-producing field in the North Sea.
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In this work, a methodology to detect interference from long-term pressure and flow-rate data is developed using multiresolution analysis in combination with machine-learning algorithms.
