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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AI can transform our work, but it demands the highest accuracy. Anything less than perfect in oil and gas and other heavy-asset industries is unacceptable. -
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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Digitalization and automation of the drilling process drive the need for an interoperability platform in a drilling operation, where a shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite.
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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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A comprehensive, digitized water-management application has been designed to streamline and enhance the monitoring and management of water resources used in hydraulic fracturing.
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This case study uses distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope, highlighting the versatile potential of DTS in long-term monitoring and establishing a workflow that makes the most of that potential.
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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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ExxonMobil developed an automated system to identify nonproductive and underperforming Permian Basin wells and prioritize high-volume wells to return to production.
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This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.
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This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
