AI/machine learning
SLB will use artificial intelligence-based software to help ensure the delivery of 18 ultradeepwater wells.
This paper describes a deep-learning image-processing model that uses videos captured by a specialized optical gas imaging camera to detect natural gas leaks.
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
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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. -
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 …
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The authors propose a hybrid virtual flow and pressure metering algorithm that merges physics-based and machine-learning models for enhanced data collection.
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The service giant shares new details about its automated fracturing spreads that slash human operator workload by 88%.
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The trial phase of the agentic program used AI agents and combined large-language-model technology with data collected from more than 15% of ADNOC’s onshore and offshore wells.
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SLB said it plans to integrate INT’s technology into its digital data and artificial intelligence platforms.
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Chevron’s announcement comes on the heels of ExxonMobil’s announcement in December of a similar project to deliver natural gas-fueled electricity to US data centers.
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The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.
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These papers provided insights and advances into field-operations automation, machine-learning-assisted petrophysical characterization, and fluid-distribution analysis in unconventional assets.
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In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.
