Data & Analytics
The paper describes the deployment of fiber-optic monitoring of CO₂ injection and containment in a carbonate saline aquifer onshore Abu Dhabi.
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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This study introduces a cleanup- and flowback-testing approach incorporating advanced solids-separation technology, a portable solution, equipment automation, improved metallurgy, and enhanced safety standards. -
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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Located 230 km south of Abu Dhabi, the onshore Shah field produces around 70,000 B/D of crude.
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A new report from GlobalData provides an overview of the digitalization efforts within the industry and their potential to transform operations.
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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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This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.
