Digital oilfield
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Intelligent completions could improve many of the world’s oil and gas wells, but not all are suited to the technology. There is another option.
The paper describes the deployment of fiber-optic monitoring of CO₂ injection and containment in a carbonate saline aquifer onshore Abu Dhabi.
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This paper addresses the challenges of integrating huge amounts of data and developing model frameworks and systematic workflows to identify opportunities for production enhancement by choosing the best candidate wells. -
This paper described the use of digital twin technology to identify design modifications required at an onshore site and visualize proposed offshore assets for a Trinidad and Tobago development.
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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.
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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.
