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Deploying artificial intelligence across an enterprise requires thinking beyond the pilot.
While new imaging capabilities are rejuvenating old data so explorers can discover hydrocarbons, finding and developing reserves is increasingly complicated.
The journal is the Society of Petroleum Engineers’ flagship peer-reviewed scholarly publication covering all aspects of petroleum engineering research.
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SPE members who are in Houston next month can join thousands of peers with a complimentary exhibition pass to ATCE 2025. -
From 26 to 27 August, industry executives, policymakers, financiers, researchers, and technologists will gather in Malaysia to explore the full potential of CCUS.
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This month’s column recaps the top five objectives for the 2025 SPE President and introduces a new discussion on the future of the SPE name.
The collaboration has announced Closed Loop Fracturing, which combines real-time subsurface data with automated surface control.
The collaboration will see TGS’ software platform implemented throughout the carbon value chain at the Northern Lights project.
The company, as part of joint industry projects, has released an updated version of its computational fluid dynamics software that it says will aid in developing technology for the energy transition.
The companies are joining forces to work on large-scale industrial data collection across TotalEnergies’ operational sites, aiming to use continuous, real-time data collection to optimize performance.
The Energy and AI Observatory aims to use up-to-date information on energy demand from data centers to determine how artificial intelligence is optimizing the energy sector.
The company is making available its data on ocean and weather conditions in an effort to boost transparency and innovation.
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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This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness. -
With the right infrastructure and interoperability, subsea resident robotics could unlock more frequent, cost-effective inspections—and a new standard for offshore efficiency. -
This study aims to thoroughly assess the influence of various completions, fracturing stimulation, and intrinsic reservoir properties affecting the productivity of 10 major unconventional plays while uncovering insights and trends unique to each play. -
The authors present an efficient workflow using an embedded discrete fracture model to simulate carbon-dioxide flow by use of conductive faults.
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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 study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data. -
The use of real-time wireless downhole pressure gauges proved a valuable alternative to workover operations in two onshore fields in Iraq. -
This paper describes a risk-based self-verification process conducted through a bespoke software application.
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Through realistic simulations, virtual reality allows for an unprecedented level of immersion and interactivity that traditional training methods cannot offer.
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Emerging solutions could solve current subsea pain points, while a new taxonomy system could clarify the capabilities of the expanding domain of underwater vehicles.
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Bad vibes are being addressed by contractors as operators push to go faster, deeper, and longer with unconventional wells.
