Digital Oil Field
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.
This paper presents the deployment of an artificial intelligence-enabled autonomous gas lift optimization system, integrating real-time centralized advanced process control with cloud-based analytics to enhance artificial gas lift performance in producer wells.
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This paper presents various functionalities and benefits of a monitoring tool developed for and used with all critical flowmeters in the operator’s production system. -
This paper presents a complete digital workflow applied to several greenfields in the Asia Pacific region that leads to successful deep-transient-testing operations initiated from intelligent planning that positively affected field-development decisions.
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These papers provided insights and advances into field-operations automation, machine-learning-assisted petrophysical characterization, and fluid-distribution analysis in unconventional assets.
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This study introduces a cleanup- and flowback-testing approach incorporating advanced solids-separation technology, a portable solution, equipment automation, improved metallurgy, and enhanced safety standards.
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Located 230 km south of Abu Dhabi, the onshore Shah field produces around 70,000 B/D of crude.
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A new report from GlobalData provides an overview of the digitalization efforts within the industry and their potential to transform operations.
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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.
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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.
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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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ExxonMobil developed an automated system to identify nonproductive and underperforming Permian Basin wells and prioritize high-volume wells to return to production.
