AI/machine learning
AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution.
The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.
EQT is benchmarking its way to basin-leading productivity and relying on partnerships and new technology to turn KPIs into operational reality.
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This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning. -
In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.
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Almost every day, petroleum engineers are coming to realize that they’ve got an arsenal of good ideas on how to leverage large, messy data sets to add value to their businesses. Those who have enlisted in the Analytics Army have progressed from siloed digitalization attempts to well-concerted digital transformation strategies that reflect high levels of organizational…
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The grant was awarded by the Scottish Funding Council in partnership with Scottish Enterprise to assist in developing an AI demonstrator to optimize subsea decommissioning.
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Undocumented orphaned wells pose hazards to both the environment and the climate. Scientists are building modern tools to help locate, assess, and pave the way for ultimately plugging these forgotten relics.
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As we turn the page on our 75th anniversary, JPT’s recent visit to the UAE offers a front-row seat of what some of the industry’s biggest players see coming.
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The USGS has said up to 19 million tons of lithium resource is contained in the briny waters of the Smackover formation in Arkansas.
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Subject-matter experts from industry and academia advanced distributed fiber-optic sensing technologies and their implementation in flow measurement during a special session.
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This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
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Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do.
