Digital Transformation
As the energy sector rapidly evolves to address climate change, tools such as the En-ROADS Climate Solutions Simulator are essential for young professionals seeking to understand the complexities of the transition and make informed, impactful decisions.
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…
Digital transformation in the oil and gas industry is likened to a major home renovation—requiring a clear vision, skilled collaboration, patience, and investment in lasting solutions. Though the process is challenging, the end goal is an improved, future-ready operation.
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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. -
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.
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These seven open-source simulators are available for free use and are among the best available in the industry.
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Dan Jeavons, Shell’s VP of computational science and digital innovation, discussed the findings in MIT’s recent report on digital technology’s impact on a net-zero emissions future with Aman Srivastava, TWA deputy editor in chief.
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Join us for the final installment in our four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study.
