Data & Analytics
Working with Dell Technologies and NVIDIA, the French supermajor is targeting improved seismic processing and artificial intelligence applications.
A discussion at the inaugural executive breakfast convened by the SPE Data Science and Engineering Analytics Technical Section, held alongside CERAWeek by S&P Global and powered by Black & Veatch, tackled the challenge of value creation from artificial intelligence in the energy industry.
AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution.
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This column is intended to provide a starting point and a roadmap for professionals who want to learn data science and are struggling with the question, “Where do I start?” -
Murphy Oil has created a work flow to normalize the tags it uses when collecting data on its hydraulic fracturing stages. The work flow described here empowers decision makers, who no longer wait for hours to collect data or waste hours cleaning and preparing data for analysis.
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Aberdeen Renewable Energy Group and the European Space Agency have signed a memorandum of intent to analyze, develop, and implement space-enabled technology and services to support the renewable energy sector.
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With such uses as tracking the source of renewable energy and changing the relationship between how energy is produced and consumed, blockchain has the potential to transform the way companies collaborate and interact to accelerate the development of low-carbon energy.
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How can the famous Casino-inspired trick for data science, statistics, and all of science be done in Python?
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Over the years, I’ve noticed interesting cultural differences between industrial sectors in their approach to dealing with staff software training. Here, I’ll try to synthesize them into a major insight and expound on the implications.
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The complete paper uses 3,782 unconventional horizontal wells to analyze the effect of proppant volume and the length of the perforated lateral on short- and long-term well productivity across the Permian Basin.
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In the spectrum of artificial intelligence (AI) technologies, those adopted to date in the oil and gas industry are task-focused, narrow applications. Taking AI to the next level cannot be done by Silicon Valley alone.
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In the complete paper, the authors generate a model by using an artificial-neural-network (ANN) technique to predict both capillary pressure and relative permeability from resistivity.
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Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents, searching for basin geology concepts and extracting information about petroleum system elements.
