Digital Transformation
Agentic AI could help upstream oil and gas operations reduce emissions by enabling real-time methane detection, optimizing flaring and energy use, and improving carbon capture efficiency.
This article examines how domain experts can use no-code ML platforms to explore decision-relevant problems, validate hypotheses, quickly build prototypes, and engage more effectively with data science teams when solutions transition toward production.
AI-driven analytics and digital platforms are reshaping offshore operations, enabling smarter, faster decision-making.
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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. -
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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"Digital gives you superpower...volunteering makes you a superstar," says Josh Etkind while sharing his insights on digital transformation and his vision as SPE Gaia chair.
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The enthusiasm for AI practice is growing rapidly across all industries. This article gives a brief overview of AI's key elements.
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A key part of the energy ecosystem is the trading of commodities and the freight that transports them. Throughout this process, optionality allows traders to find opportunities and create value for their respective businesses.
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The SPE Research Portal uses artificial intelligence technology, fortified by industry knowledge, to address the long-term challenges of finding and analyzing information in unstructured data.
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In the ongoing digital transformation in the industry, it's not enough if we only adapt and improve data-processing capabilities; we should also empower human interaction, study, engagement, and collaboration through the use of that data.
