AI/machine learning
Even as industry faces policy and tariff uncertainty, companies view spending on digital transformation as a driver of efficiency.
The Tela artificial intelligence assistant is designed to analyze data and adapt upstream workflows in real time.
In this third work in a series, the authors conduct transfer-learning validation with a robust real-field data set for hydraulic fracturing design.
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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. -
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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In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.
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From optimizing drilling performance to enhancing worker safety, computer vision can change how the industry works.
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A recent survey conducted by Rackspace Technology reveals new attitudes about using the cloud, including a change from using the public cloud to using private, on-site clouds or a hybrid of the two.
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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.
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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.
