AI/machine learning
Schneider Electric says the deal advances its vision of creating intelligent industrial ecosystems that connect physical assets with digital insights across the asset life cycle.
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.
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Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet, with change coming at breakneck speed, the time to identify your company’s AI strategy is now. -
When Google claimed quantum supremacy, IBM challenged it. Nonetheless, the development is really important for the future of artificial intelligence.
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The sixth annual Deep Learning Summit in London saw industry leaders, academics, researchers, and innovative startups present the latest technological advancements and industry application methods in the field of deep learning.
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Deep learning is good at finding patterns in reams of data but can't explain how they're connected. Turing Award winner Yoshua Bengio wants to change that.
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The controller from Olis will be distributed and supported by iCsys and is expected to increase efficiency and decease costs.
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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.
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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.
