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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The application continuously uses machine-learning algorithms to aggregate historical and real-time data across production operations. Additionally, the application uses a hybrid approach that draws on physics-based and data-driven AI models to generate predictions and prescribed actions. -
The report on unmanned aerial vehicles in industry presents overarching themes and concerns, including navigating regulations, finding the right technology, and finding ways to educate the public about drone operations.
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Artificial intelligence and emerging technologies such as virtual personal assistants and chatbots are rapidly making headway into the workplace. Research and advisory company Gartner predicts that, by 2024, these technologies will replace almost 69% of the manager’s workload.
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Recently, the hype around artificial intelligence and machine learning caused several people to ask me how much of a project is actual machine learning. Based on man-hours spent on the project, I estimate that only about 5% of the effort is spent directly on data-science-related activities.
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This paper shows how high-frequency, real-time drilling data from wired drillpipe has helped optimization of drilling performance and achievement of additional improvements in the New Mexico Delaware Basin.
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This paper describes how new technology was implemented and deployed through a downhole acoustic network through a sequence of runs in complex North Sea wells under managed-pressure conditions.
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This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.
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In my inaugural column as editor of the Well Testing Technology Focus feature, I want to shine a light on the notable trend among operators of seizing on the tremendous untapped potential that exploration and appraisal wells represent for far-field reservoir characterization and connectivity.
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This paper describes the use of a downhole temperature-sensor array during a commingled drillstem test (DST) to determine the density of produced fluids accurately.
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This paper applies a new constrained multiwell deconvolution algorithm to two field cases: a gas reservoir with two producers and an oil reservoir with three producers and one injector.
