AI/machine learning
The Norwegian major said it is using artificial intelligence for predictive maintenance throughout its facilities and for interpretation of seismic data from the Norwegian continental shelf.
This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.
The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.
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New strides in computer vision, well controls indicators, and BOP alignment were showcased at the recent Offshore Technology Conference. -
Gautam Swami, manager of corporate R&D at NOV and SPE member, shares his experiences in building a career in oil and gas R&D, discusses how innovation is shaping the industry, and offers guidance to young professionals.
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Aramco’s latest MOUs focus on driving innovation and growth across oil, gas, and downstream sectors.
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This article presents a comparative study evaluating four machine-learning approaches, including three deep-learning methods, for forecasting gas and condensate production over a 5-year horizon.
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After a successful trial phase of ENERGYai, AIQ has been tapped to roll out the technology across ADNOC’s upstream operations.
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Weatherford and AIQ say they aim to enable the energy sector to unlock efficiencies, boost productivity, and reduce operational costs by combining their strengths.
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This article is the second in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Madhava Syamlal, CEO and founder of QubitSolve, discusses the present and future of quantum computing.
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This paper presents an immersive platform that enables multidisciplinary teams and management to make decisions, connecting professionals to demonstrate and share findings in a way that capitalizes on artificial intelligence and cognitive capabilities.
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The paper showcases the digital journey of a brownfield where digital solutions are enhancing recoverable volume, production, and process efficiency while minimizing losses and maximizing the return of investment.
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The rapid development of oil and gas intelligent operations depends on artificial intelligence, automation, and data analytics to achieve optimal conditions in oil and gas operations.
