Digital Oil Field
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 the first supercomputer to generative AI, JPT has followed the advancement of digital technology in the petroleum industry. As the steady march of innovation continues, four experts give their views on the state and future of data science in the industry. -
The authors integrated azimuths and intensities recorded by fiber optics and compared them with post-flowback production-allocation and interference testing to identify areas of conductive fractures and offset-well communication.
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This paper addresses the challenges related to well control and the successful implementation of deep-transient-test operations in an offshore well in Southeast Asia carried out with the help of a dynamic well-control-simulation platform.
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The digital twin aims to allow Petrobras to optimize system settings to maximize production, increase recovery, and reduce risk.
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For today’s oil and gas companies, digital twins offer untapped potential to decarbonize the leading source of their emissions—field production.
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This paper presents the concept of a supervisory and advisory system dedicated to support the detection of abnormal events and to provide guidelines for fluid treatment.
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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The authors of this paper propose an automated approach to sand prediction and control monitoring that improved operational efficiency by reducing time spent on manual analysis and the decision-making process in a Myanmar field.
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We must admit that the oil field is still in the early days of its digital journey. It’s time to give serious thought to the expectation/reality gap, the cultural differences between the way we’ve always done things and the way that digital is changing us, and the pain points that may trip us up unless we’re careful.
