Data & Analytics
Autonomous drilling through managed pressure drilling (MPD) at the Atlantis field has given the operator confidence to scale the method.
The cloud platform provider said the initiative is designed to help energy companies manage and analyze large-scale operational data.
Major increases in hydrocarbon production require both incremental and revolutionary technologies, industry leaders said during the SPE Hydraulic Fracturing Technology Conference.
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The AIoT has the potential to transform industries and society, and it is already starting to have an impact. This article will explore the principles of AIoT, its benefits, and its current use. -
Supervised learning has many commercial applications; however, such learning lacks the capability to generate new insights and knowledge. In contrast, unsupervised learning discovers the inherent structures in unlabeled data, thereby helping generate new insights and actionable knowledge from large volumes of data.
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Schneider Electric University has been designed to help data center professionals expand their skills by offering free guidance on the latest technology, sustainability, and energy efficiency initiatives.
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Researchers from Skoltech have trained a neural network to recognize rock samples in core box images efficiently. The process has sped up analysis by up to 20 times and made it possible to automate the description of rock samples.
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Chief digital and information officer Sandeep Gupta's innovative use of technology has enabled the company to cut costs, reduce time to first oil, and manage decline in production.
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HiberHilo will be used to monitor remote oil and gas wells in Papua New Guinea, providing real-time performance and safety data.
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Companies now have 24 hours to report hacks and are poised to get more flexibility to design their defenses.
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The paper investigates estimation of optimal design variables that maximize net present value for life-cycle production optimization during a single-well CO2 huff ‘n’ puff process in unconventional oil reservoirs.
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The paper describes an approach to history matching and forecasting that does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance.
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This paper evaluates learnings from the past 30 years of methods that aim to quantify the uncertainty in the subsurface using multiple realizations, describing major challenges and outlining potential ways to overcome them.
