Technology
As video game technology has evolved, so have the ways in which this technology can be used in the oil and gas industry.
Five key themes to AI's success including standardization, automation, integration, scalability, and continuous improvement can provide a clear roadmap for effective AI deployment, addressing challenges and driving sustainability across the subsurface energy sector.
TWA editor in chief and University of Oklahoma alumnus Aman Srivastava interviews the professors and students in the university's petroleum engineering department about the cutting-edge research YPs are participating in.
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As AI continues to evolve, the need for energy-powered data centers is on the rise. Data center developers who can make this transition toward a more efficient and greener system will anchor themselves as key players in this growing industry. -
Change is inevitable, but the resistance to it, especially in cultural and monetary contexts, can be profound. The deep-rooted dependency on crude oil and internal combustion engines exemplifies this inertia, raising questions about how we might transition to new energy sources without overhauling our entire infrastructure.
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A 2D model of catenary trajectory design is presented. The solutions of the catenary design are in closed form and do not demand thorough numerical estimations. A traditional arc well design is also included to compare the hookload with the catenary trajectory well design.
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This article provides an overview of common damage caused by vibrations, the most prominent vibration modes, mitigation methods, and design principles used to diminish vibration effects.
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The oil and gas industry is one of the few examples on a global scale of how the need for further production improvement pushes professionals to customize existing models. Physical models are often reworked in order to expand their use in various industries. This article examines ten equations that help drive the petroleum industry.
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Lithium-rush miners are flocking to Arkansas more than 100 years after an oil discovery in the Smackover Field.
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In the final part of this three-part series, we extend our learning of Part 2 to the multivariate model and train a single model to predict three outcomes: oil, gas, and water.
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This article focuses on the introduction of one of the flow-network-based models called GPSNet that has growing popularity in the literature and shows promising results during our proof-of-concept applications.
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In Part 2 of this three-part series, we dive into a practical example using the production data of Equinor’s Volve field data set.
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In Part 1 of this three-part series, we use long short-term memory (LSTM), a machine learning technique, to predict oil, gas, and water production using real field data.
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