Digital oilfield
The authors propose a hybrid virtual flow and pressure metering algorithm that merges physics-based and machine-learning models for enhanced data collection.
This paper discusses the concept, applications, and continual evolution of a new 3D temperature and spectral-acoustics modeling and logging approach.
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.
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The authors of this paper write that 4D data are an integral part of a reservoir-management program and, together with geological and production history data, are being used to update reservoir models to further the goals of field development. -
The authors of this paper describe the development of a continuous monitoring solution throughout its deployment.
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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.
