Data & Analytics
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.
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From optimizing drilling performance to enhancing worker safety, computer vision can change how the industry works. -
This paper presents a complete digital workflow applied to several greenfields in the Asia Pacific region that leads to successful deep-transient-testing operations initiated from intelligent planning that positively affected field-development decisions.
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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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These papers provided insights and advances into field-operations automation, machine-learning-assisted petrophysical characterization, and fluid-distribution analysis in unconventional assets.
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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.
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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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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.
