Data & Analytics
This article explores the integration of hydrogen into existing natural gas infrastructure and introduces practical solutions, including the application of machine learning models, to support decision-making and infrastructure adaptation in the energy transition.
Artificial intelligence is transforming—not replacing—petroleum engineering. As AI-driven, data-centric methods replace traditional deterministic models, engineers must adapt by acquiring skills in data science, algorithmic thinking, and software tools. The industry’s evolution raises a critical question: Will petroleum engineers evolve with these changes or risk beco…
This research developed a clear framework for assessing and selecting fit-for-purpose software. The study focuses on the role of a data-driven approach in the decision process, with application to operational software systems in the oil and gas industry.
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The data include high-resolution spatial layers for environmental research, monitoring, and geospatial analysis. -
The energy-focused LLM project by Aramco Americas, SPE, and i2k Connect has entered the testing phase and is on track for licensing to operators later this year.
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Although 92% of energy companies say they plan on digital technology investments, only 27% currently retrain and reskill existing employees to meet the upcoming demand, according to a recent survey from Ernst & Young.
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The company is investing $1 billion to establish the Engineering and Innovation Excellence Center (ENGINE) in Bengaluru, India.
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The department was established in October with $3 million in funding from Anuradha and Vikas Sinha and aims to advance data science education, research, and career development.
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Yujing Du made history as she was named The University of Tulsa's first female petroleum engineering faculty member in January. In this Q&A, she discusses the role of petroleum engineering in the global energy transition, diversity in STEM, and strategies for supporting women in the energy sector.
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On long-term trends, most things in the world are getting better, but gradual improvements don't make the news.
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Subsurface modeling and history matching are critical steps for driving decisions. Generative artificial intelligence can support these efforts by incorporating various sources of information and allowing for low-dimensional parameterization for history matching.
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TWA editors met with Satyam Priyadarshy, a technology fellow and chief data scientist at Halliburton, about how young professionals can prepare themselves for the application of data science in their work and how to approach problems and challenges.
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Join TWA Editorial Board member Mani Bansal as he interviews Ashish Fatnani, an industry professional with 12 years of experience working for companies including Halliburton, ONGC, and Mercedes Benz.
