AI/machine learning
Even as industry faces policy and tariff uncertainty, companies view spending on digital transformation as a driver of efficiency.
The Tela artificial intelligence assistant is designed to analyze data and adapt upstream workflows in real time.
In this third work in a series, the authors conduct transfer-learning validation with a robust real-field data set for hydraulic fracturing design.
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Sensors, robots, and artificial intelligence have made their way into a number of areas within the industry, including pipeline inspections. Shell has begun to examine the innovative technologies that could shift the inspection paradigm. -
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.
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When Google claimed quantum supremacy, IBM challenged it. Nonetheless, the development is really important for the future of artificial intelligence.
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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 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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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?
