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 support vessel operator has invested in project-management software and in making connections using Starlink satellites. -
European oil and gas company Aker BP has agreed to a software-as-a-service collaboration with software firm Aize.
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The Norwegian data company has launched a 3D seismic survey in the Equatorial Margin area.
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The industry is balancing brains and bots as it squeezes out barrels of oil production.
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Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…
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SponsoredDive into TAQA’s digitalization and deep learning initiatives that are shaping the company's new approach to its Journey Management System. This innovative concept minimizes transportation-related risks in a period of rapid operations expansion.
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The company says the field has achieved a 25% production capacity increase through the user of advanced digital technology.
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The new frontier of production improvement combines surveillance techniques and analysis to determine which variables boost output.
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This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq.
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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
