AI/machine learning
CERAWeek panelists see AI as a way to leverage data and people in interpreting data for exploration, but a cultural shift at companies may still be needed.
This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.
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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.
