AI/machine learning
Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.
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.
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This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq. -
This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
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This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.
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Implemented for the first time offshore, the technology uses artificial intelligence to operate wells autonomously.
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The planned long-term partnership aims to digitally transform Aker BP’s subsurface workflows in an effort to lower costs, shorten planning cycles, and increase production.
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This paper outlines how one company uses digital technologies to manage HSE risks in project delivery, developing an artificial intelligence (AI) predictive model to predict HSE risks and incidents based on historical incident data.
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The RoboWell technology for well control will be available globally through Halliburton’s Landmark iEnergy hybrid cloud.
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Automated workflow unifies geological, completion, and production data to inform speedier, better investment decisions for nonoperated assets.
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Both new and old vessels are benefiting from automation processes that can improve operational efficiency, predict downtime, and debottleneck workflows using a flurry of crucial data points.
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The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
