Digital Transformation
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…
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.
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. -
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.
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Continue our journey with Part 3 of this four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study.
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Join us for Part 2 of a four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study.
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Grab a pen and paper and settle in for Part 1 of a four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study.
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The future of oil and gas technologies seems promising and full of opportunities from a young professional’s perspective. The floor is wide open for disruptive inventions that can significantly optimize operations.
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Data validation is not a direct out-of-the-box process and requires planning and even budgeting, but high-quality data can save your time, money, and effort.
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