AI/machine learning
Aurora Innovation and Detmar Logistics have inked a deal for 30 autonomous trucks that will begin hauling sand in the region next year.
Sustainability in reservoir management emerges not from standalone initiatives but from integrated, data-driven workflows, where shared models, closed-loop processes, and AI-enabled insights reduce fragmentation and make sustainable performance a natural outcome.
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In oil and gas operations, every decision counts. For more than 2 decades, SiteCom has been the trusted digital backbone for well operations worldwide, driving insight, collaboration, and efficiency.
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This study examines the implementation of a predictive maintenance method using artificial intelligence and machine learning for offshore rotating production-critical equipment. Conducted over 2 years at Murphy Oil’s deepwater platforms in the Gulf of Mexico, the project aimed to detect equipment issues early, reduce downtime, and streamline maintenance processes. -
Moving from use cases to enterprisewide AI is more than a technology challenge. It requires anchoring on value, feedback, and innovation.
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This paper focuses on the vital task of identifying bypassed oil and locating the remaining oil in mature fields, emphasizing the significance of these activities in sustaining efficient oilfield exploitation.
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The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.
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The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.
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The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.
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This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.
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Almost every day, petroleum engineers are coming to realize that they’ve got an arsenal of good ideas on how to leverage large, messy data sets to add value to their businesses. Those who have enlisted in the Analytics Army have progressed from siloed digitalization attempts to well-concerted digital transformation strategies that reflect high levels of organizational…
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In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.
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The grant was awarded by the Scottish Funding Council in partnership with Scottish Enterprise to assist in developing an AI demonstrator to optimize subsea decommissioning.
