AI/machine learning
Analysis by the energy research firm sees the value of artificial intelligence growing for exploration and production companies, but the company said increased investment will be necessary.
Chevron and Halliburton describe how they built and deployed the fully autonomous closed-loop fracturing system that enables subsurface-driven optimization.
Aramco says it has saved $770 million over the past 3 years from the $70 million it has invested over the same period in corrosion management technologies.
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This paper introduces a novel optimization framework to address CO2 injection strategies under geomechanical risks using a Fourier neural operator-based deep-learning model. -
The energy-focused LLM project by Aramco Americas, SPE, and i2k Connect has entered the testing phase and is on track for licensing to operators later this year.
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The agreement aims to bring the efforts of both companies together to advance digital-enabled carbon-free floating power generation.
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A roundtable discussion during CERAWeek pointed to the necessity of a mindset shift for the oil and gas industry to tap into AI’s true potential.
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This paper presents the application of a new automatic geosteering method that combines probabilistic interpretation with artificial intelligence for look-ahead decision-making, representing an innovative advancement in automated geosteering frameworks.
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Experts speaking at CERAWeek 2025 lauded industry's shift from focusing on the energy transition to prioritizing oil production.
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Data quality and mission clarity matter more than ever, according to experts speaking at this year’s International Petroleum Technology Conference in Kuala Lumpur.
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The massive system brings advanced capabilities for simulation, AI, and data analysis to drive breakthroughs in cancer research, materials discovery, energy technologies, and many other fields.
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The new burner, created with the help of machine learning and additive manufacturing, promises high methane destruction efficiency and combustion stability even in windy conditions.
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Transitioning to a low-carbon economy demands large-scale CO2, natural gas, and hydrogen storage. In this context, the application of AI/ML technology to uncover geochemical, microbial, geomechanical, and hydraulic mechanisms related to storage and solve complicated history-matching and optimization problems, thereby enhancing storage efficiency, has been prominently …
