AI/machine learning
The Energy and AI Observatory aims to use up-to-date information on energy demand from data centers to determine how artificial intelligence is optimizing the energy sector.
This article is the third in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Zikri Bayraktar, a senior machine learning engineer with SLB’s Software Technology and Innovation Center, discusses the expanding use of artificial intelligence in the upstream sector.
This article presents a results-driven case study from an ongoing collaboration between a midstream oil and gas company and Neuralix Inc.
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Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do. -
This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
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Machine learning and a decade of gas composition records helped the operator identify wells that were most likely to produce paraffins.
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The companies plan to develop new artificial-intelligence-powered processes and workflows to optimize oil and gas production.
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Routine status reporting often presents a challenge because of its intimidating and time-consuming nature for both employees and supervisors. With large language models, a system was developed to generate coherent artificial-intelligence-driven reports. The goal is to enhance the understanding of overall insights and reduce the time required for individual report read…
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This paper aims to emphasize the importance of decision-making based on quantitative monitoring outputs, from both a business perspective and an ecosystem-service perspective, in future offshore projects.
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Experts at SPE’s Annual Technical Conference and Exhibition say that despite AI’s great potential, it’s important to be realistic about AI’s capabilities and to remember that successful projects solve specific business problems.
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New and evolving artificial lift technology is helping operators improve production rates.
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This paper delves into the evolving landscape of drilling automation, emphasizing the imperative for these systems to go beyond novelty and deliver quantifiable financial value.
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This paper describes a new application that leverages advanced machine-learning techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads.
