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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This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models. -
As you read the examples in this section, you will see that a change is already under way in that the methods that are being used are increasingly not oil-and-gas-specific but instead follow patterns that are being used in other industries.
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This digital deal is helping to make augmented reality a new reality for oil and gas operations.
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The operator piloted a new well-completion design combining inflow-control valves (ICVs) in the shallow reservoir and inflow-control devices (ICDs) in the deeper reservoir, both deployed in a water-injector well for the first time in the company’s experience.
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The two technology startups aim to bring scale to the visual side of oilfield automation with a new deal that will cover 90% of US energy assets.
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The company plans an innovative application of oceanographic instrumentation to maximize recovery at its Johan Sverdrup oil field in the North Sea.
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The need to understand the future trends of the oil industry has never been greater than it is today.
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In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells.
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The authors detail the development of a technique based on surface-to-borehole controlled-source electromagnetics (CSEM), which exploits the large contrast in resistivity between injected water and oil to derive 3D resistivity distributions, proportional to saturations, in the reservoir.
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In this paper, the authors describe a project to design, field trial, and qualify an alternative solution for real-time monitoring of the oil rim in carbonate reservoirs that overcomes these disadvantages.
