AI/machine learning
The DOE-backed EGS-Twin project aims to simulate geothermal production systems, helping operators better predict performance and maximize output.
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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New funding for a chatbot technology, or smart assistant, represents the latest development in the Norwegian operator’s drive toward digitalization. -
AGI stands for artificial general intelligence, a hypothetical computer program that can perform intellectual tasks as well as, or better than, a human. AGI will make today’s most advanced AIs look like pocket calculators.
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Dubbed the technology of the decade, AI has been the catchphrase on every futurist’s tongue. From customer support chatbots to smart assistants, AI has begun to transform numerous industry verticals.
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Machine-learning methods have gained tremendous attention in the last decade. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention. This is not very different from the notion of automatic history matching.
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While both are raw resources, data is reusable. A CERAWeek panel said infrastructure and cultural change are needed to drive the transformative value of data.
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HSE data is ever growing, especially in high-risk industries. So, how can you apply data science and artificial intelligence to help see beyond current practices and gain even greater insights into safety data?
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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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This paper discusses a project with the objective of leveraging prestack and poststack seismic data in order to reconstruct 3D images of thin, discontinuous, oil-filled packstone pay facies of the Upper and Lower Wolfcamp formation.
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Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function.
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In 2015, the United Nations ratified the 2030 Sustainable Development Goals. Technology will be critical in the pursuit of these ambitious targets, but the pace and scale of change creates risks that humanity must take very seriously.
