Data & Analytics
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 software that the duo is working on aims to optimize and automate the moving of drilling rigs. -
Sustainability data management is a relatively new discipline that requires a unique set of tools, processes, and procedures to meet corporate demands.
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Initial forays into using digital twins across its major fields has inspired the multinational hydrocarbon exploration and production company to further adopt the technology across its entire portfolio.
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The USV will be equipped with a remotely operated vehicle that is capable of operating in water up to 1500 m deep and tools to perform subsea operations.
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The Abu Dhabi National Oil Company said it plans to deploy uncrewed aerial vehicles equipped with the latest imaging technology for detailed inspections of site assets and infrastructure.
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The robot company said it plans to integrate 3D at Depth’s LiDAR inspection technology after the all-stock acquisition.
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents an approach to optimize the location of wellhead towers using an algorithm based on multiple parameters related to well cost.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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The authors of this paper propose an automated approach to sand prediction and control monitoring that improved operational efficiency by reducing time spent on manual analysis and the decision-making process in a Myanmar field.
