Data & Analytics
Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.
Terra Drone will support Aramco's operations and Saudi Arabia's technological and economic progress.
Organic data governance emphasizes flexibility, stakeholder engagement, and a culture that values data integrity.
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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. -
The authors of this paper discuss how intelligent completions in the Gulf of Mexico have allowed for increased production, reduction in operating expenses, enhanced overall reservoir recovery, and improved safety and environmental performance.
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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.
