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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The authors of this paper describe the development of a continuous monitoring solution throughout its deployment. -
This paper introduces a measurement system that is agile for transport and can be installed anywhere with a small footprint while delivering reasonably accurate results.
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The authors of this paper present an autonomous directional-drilling framework built on intelligent planning and execution capabilities and supported by surface and downhole automation technologies.
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The opening ceremony highlighted maximizing production sustainably to meet global demand, integration of simulation and optimization in a single platform with automation, and energy security.
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The authors of this paper present a method of retrieving downhole data that is a practical and inexpensive alternative to wireline or slickline logging and permanently installed sensors.
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Industry leaders are harnessing the power of data to improve efficiencies, eliminate nonproductive time, and reduce Capex.
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The authors discuss a study based on twistoffs experienced with bottomhole assembly components during drilling operations and provide recommendations for reduction or elimination of these incidents.
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This paper shares details of 2 years of monitoring the first commingled updip smart water injector drilled in the Piltun area of the Piltun-Astokhskoye offshore oil and gas field.
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This paper discusses a waterflood optimization system that provides monitoring and surveillance dashboards with artificial-intelligence and machine-learning components to generate and assess insights into waterflood operational efficiency.
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At the company’s annual conference, leaders from its Automations Solutions business laid out a three-pronged effort to improve automation architecture—intelligent fields, the edge, and the cloud.
