AI/machine learning
The companies said they plan to start deploying digital twin technologies in Oman this year.
This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.
This paper reviews the motivation and development of response-based forecasting from the perspective of the authors, reviewing examples and processes that have served as validation and led to modeling refinement.
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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. -
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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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 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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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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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.
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Chevron and ExxonMobil are working on deals to use natural gas and carbon capture to power the technology industry's AI data centers, executives with the companies said.
