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 application continuously uses machine-learning algorithms to aggregate historical and real-time data across production operations. Additionally, the application uses a hybrid approach that draws on physics-based and data-driven AI models to generate predictions and prescribed actions. -
The report on unmanned aerial vehicles in industry presents overarching themes and concerns, including navigating regulations, finding the right technology, and finding ways to educate the public about drone operations.
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This paper shows how high-frequency, real-time drilling data from wired drillpipe has helped optimization of drilling performance and achievement of additional improvements in the New Mexico Delaware Basin.
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This paper describes how new technology was implemented and deployed through a downhole acoustic network through a sequence of runs in complex North Sea wells under managed-pressure conditions.
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This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.
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In my inaugural column as editor of the Well Testing Technology Focus feature, I want to shine a light on the notable trend among operators of seizing on the tremendous untapped potential that exploration and appraisal wells represent for far-field reservoir characterization and connectivity.
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This paper describes the use of a downhole temperature-sensor array during a commingled drillstem test (DST) to determine the density of produced fluids accurately.
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This paper applies a new constrained multiwell deconvolution algorithm to two field cases: a gas reservoir with two producers and an oil reservoir with three producers and one injector.
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The complete paper describes the shortcomings of traditional well testing methods and the methodology and results of applying wireline-conveyed IPTT in a light-oil reservoir offshore Norway.
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The complete paper presents a drilling semantic framework that allows software solutions to achieve automatic and versatile self-configuration.
