AI/machine learning
Deploying artificial intelligence across an enterprise requires thinking beyond the pilot.
This paper presents the development of a robust, physics-based, and data-driven workflow for modeling mud loss in fractured formations and predicting terminal mud loss volume and time, as well as equivalent hydraulic fracture aperture.
This work introduces a fast, methodical approach to detect liquid loading using easily available field data while avoiding traditional assumptions and to determine critical gas rates directly from field data.
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The agreement focuses on improving operational efficiency and consistency through advanced digital tools and real-time data integration. -
TotalEnergies and the French company Mistral AI are joining forces to extend the use of AI in improving TotalEnergies’ performance, especially in low-carbon energies. The partners plan to set up a joint innovation lab focused on artificial intelligence.
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Secondary and tertiary efforts are critical for sustaining the productive lives of unconventional plays.
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Foundation models are rapidly emerging as a transformative force across industries. While their effect on natural language processing and computer vision is well-documented, their potential in specialized engineering domains, particularly within the critical oil, gas, and broader energy sectors, is vast and increasingly recognized. This article explores how these powe…
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Buoy-based camera footage, analyzed by artificial intelligence, can help reduce the risk of birds colliding with offshore wind farm turbines.
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Best practices are not static; they evolve alongside advancements that redefine what is achievable.
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New strides in computer vision, well controls indicators, and BOP alignment were showcased at the recent Offshore Technology Conference.
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Gautam Swami, manager of corporate R&D at NOV and SPE member, shares his experiences in building a career in oil and gas R&D, discusses how innovation is shaping the industry, and offers guidance to young professionals.
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Aramco’s latest MOUs focus on driving innovation and growth across oil, gas, and downstream sectors.
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This article presents a comparative study evaluating four machine-learning approaches, including three deep-learning methods, for forecasting gas and condensate production over a 5-year horizon.
