Data & Analytics
The emerging vision is an industry where engineers focus on judgment, risk assessments, and accountability.
This paper presents the large-scale deployment and operational integration of an advanced, automated rheometer in the Ghawar gas field, one of the world’s most prolific and technically complex hydrocarbon developments.
This study demonstrates the transformative potential of physics-informed neural networks as a hybrid framework for multiphase flow measurement.
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Early adopters of large language models praise the technology’s promise to advance upstream research and software development while also offering cautionary notes. -
Devon Energy along with petroleum engineering consulting and software firm Whitson and cloud computing company Snowflake developed a system to monitor the dynamics of 5,000 wells.
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This paper presents an approach using artificial neural networks to predict the discharge pressure of electrical submersible pumps.
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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 article describes how Oxy developed an in-house application to handle critical production operations tasks including surveillance, optimization, downtime entry, and well testing. The application, called Nexus, allows well analysts and production engineers to manage twice the number of wells they were able to manage just 5 years ago.
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SEG and SPE join forces to offer access to a robust research portal that harnesses the power of AI and ML.
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This article explores the effect of quantum computing on data science and AI, looking at the fundamental concepts of quantum computing and the key terms used in the field. It also covers the challenges that lie ahead for quantum computing and how they can be overcome.
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The Taiwanese company is upgrading its ships in an effort to improve its ability to work with companies pursuing offshore renewable energy.
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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 authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.
