Data & Analytics
After years of market shocks, technological breakthroughs, and rising uncertainty, ATCE 2026 will provide new insights on how industry leaders and technical experts are preparing for the next era of the upstream business.
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.
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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. -
This paper presents the operator’s learnings in evaluating the routine of a floating production, storage, and offloading asset crew to identify scenarios for the application of robotics in day-to-day offshore activities.
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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 describes the suite of cloud-based digital twin tools that the operator has developed and is integrating into its operations, providing online, real-time calculation of scale risk and deployed barrier health to manage risk on a well-by-well basis.
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There is no shortage of quantum technology applications in the upstream industry, from supply chain to well placement, making now the time to take it seriously.
