AI/machine learning
Known as Arena, the collaboration aims to improve decision-making across increasingly complex reservoirs and growing volumes of subsurface data.
Robotic and autonomous systems are transforming energy operations, but their full value emerges only when paired with data science, engineering analytics, and AI/ML to turn data into decisions. The transition of the SPE Robotic & Autonomous Systems Technical Section into the SPE Data Science & Engineering Analytics Technical Section helps bridge this critical technolo…
A credible custody-transfer record depends on far more than the accuracy of a single meter. It is the product of an integrated measurement chain. While meter performance supports measurement-system integrity, commercial confidence also depends on traceability, governance, and authorization.
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YPF’s data analytics experts are eagerly seeking partnerships with oilfield operations experts who can help blend elegant data analysis with the messy reality of oil production. -
What is the effect of the reservoir type on the application of AI and ML in reservoir and production modeling?
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Seismic imaging provides vital tools for the exploration of potential hydrocarbon reserves and subsequent production-planning activities. The acquisition of high-resolution, regularly sampled seismic data may be hindered by physical or financial constraints.
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With recent advances in AI being enabled through access to so much big data and cheap computing power, there is incredible momentum in the field. Can big data really deliver on all this hype, and what can go wrong?
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The industrial Internet of Things (IoT) is changing the way the oil and gas industry operates, but are companies leveraging it to its full potential? What strategies are being employed to handle the obstacles to implementation? Finding value in people plays a role in integrating IoT into operations.
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The artificial-intelligence application BHC3 Reliability provides early warning of production downtime and process risk to improve operational productivity, efficiency, and safety.
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Using machine learning (ML), image recognition, and object detection, the use of ML on algorithms to recognize objects and describe their condition were investigated—offering new possibilities for performing inspection and data gathering to evaluate the technical condition of oil and gas assets.
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The combination of digital technologies will enable Chevron—and, eventually, other companies—to process, visualize, interpret, and glean insights from multiple data sources, the companies said.
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Equinor is working on a natural language processing tool that could combine data sources and help planners anticipate the issues that affect onsite operational safety.
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Blending smart-proxy models with data-driven models to create hybrid models is not always the best idea for physics- and engineering-related applications.
