Digital Transformation
AI is beginning to transform well management by helping engineers predict electrical submersible pump failures before they happen, optimize drawdown more efficiently, and generate reliable forecasts even when data is scarce or noisy.
The oil and gas industry's shift to smart fields—driven by automation, AI, and real-time data—requires petroleum engineers to master digital technologies alongside traditional skills.
Tiger Skid, a custom-built cyber-physical training and testing platform, simulates real-world energy systems and industrial processes vulnerable to cyber-physical attacks.
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The portal includes SPE resources like OnePetro, PetroWiki, JPT, Energy Stream, and SPE journals which can be easily searched using i2k Connect's AI-driven technology. -
Experts from various fields met to discuss the role of AI in the energy transition and its challenges including high energy consumption and carbon-intensive infrastructure requirements.
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Join TWA Editorial Board member Md Imtiaz as he interviews ONGC’s Western Offshore Asset Executive Director Ravi Shankar.
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As video game technology has evolved, so have the ways in which this technology can be used in the oil and gas industry.
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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.
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Tune in 28 October for a discussion with SPE Technical Directors about the future of data science for professionals in the energy sector.
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Prajakta Kulkarni, SPE, has spearheaded the development of a global digital platform to optimize pricing, strategy, and sales in the industry. With a background in petroleum engineering, she identified a digital gap in the industry, leading her to create a platform that enhances data-driven decision-making, streamlines operations, and integrates AI technologies to imp…
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Explore how data science has become essential across diverse sectors, how people can learn about data science, and how engineers can transition into this 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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Explore the challenges associated with fiber-optics data analysis and how recent advances in technology can be leveraged to maximize the value of the data.
