AI/machine learning
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.
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GlobalData’s report "Robotics in Oil and Gas" notes that, while robotics has been a part of the oil and gas industry for several decades, growing digitalization and integration with artificial intelligence, cloud computing, and the Internet of Things have helped diversify robot use cases within the industry. -
The service company said it plans to use DataRobot’s artificial intelligence capabilities in its production-optimization and well-construction digital platforms.
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Synthetic data generation is a solution that allows citizen data scientists and auto ML users to quickly and safely create and use business-critical data assets. Benefits go beyond democratizing data access, and even those with privileged data access are building synthetic data generators into their work flows.
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This paper describes a novel method based on machine learning to maintain an evergreen competency database. The tool reduces discrepancies between organizational requirements and the actual talent deployment by using unstructured corporate data.
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Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…
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Feeding better battery storage with the energy produced by cleaner sources such as solar panels and wind turbines is not a new idea. But are good ideas enough? Or could AI be the answer to unlocking the true value of the next generation of solar and energy innovations?
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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.
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So far, digital twins have focused mainly on mimicking small, well-defined systems. Integrated asset models, however, tend to address the bigger picture. In this video, Distinguished Lecturer Kristian Mogensen addresses whether we can take the best from both worlds, whether we need to, and how to go about developing such a technical solution.
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New research led by the University of Glasgow’s School of Psychology and Neuroscience presents an approach to understand whether the human brain and deep neural networks recognize things in the same way.
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Founded by former analytics experts for a large US independent, Xecta Digital Labs is proposing a new analysis method for horizontal wells. Adopting it means turning the page on some old habits.
