Data & Analytics
AI and machine learning are transforming drilling execution, detecting downhole problems earlier, automating reporting, and closing the loop between the wellbore and the well plan through real-time geosteering.
Robotic and autonomous systems are transforming energy operations, but their full value emerges only when paired with data science, engineering analytics, and AI/ML to turn data into decisions. The transition of the SPE Robotic & Autonomous Systems Technical Section into the SPE Data Science & Engineering Analytics Technical Section helps bridge this critical technolo…
How AI-powered well planning is transforming field development by accelerating trajectory design, reducing drilling risk, optimizing costs, and helping engineers deliver safer, more efficient wells.
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2026 TWA Energy Influencer Muhammad Karimi shares how he transformed academic research into a globally deployed energy technology, offering insights on innovation, entrepreneurship, oilfield digitalization, and the energy transition. -
Discover how AI leader Fatai Anifowose transformed skepticism into innovation, sharing insights on artificial intelligence in energy, leadership, continuous learning, and the skills shaping the future of the industry.
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Utkarsh Sinha, 2026 TWA Energy Influencer, is driving innovation at the intersection of reservoir physics and AI, combining research, patents, and industry leadership to develop practical machine learning solutions that improve reservoir and production engineering decision-making.
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How a PhD researcher at The University of Tulsa is helping reshape the future of assisted history matching.
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Proxy models are transforming reservoir management by enabling engineers to rapidly evaluate and optimize thousands of operating scenarios, helping improve CO2-EOR, carbon storage, geothermal, and underground gas storage decisions while keeping high-fidelity reservoir simulation at the core of validation.
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In this interview, Sushma Bhan, energy data, AI leader, and technical director of the SPE Data Science and Engineering Analytics Technical Section, shares lessons from 3 decades at Shell, discussing digital transformation, the future of AI in upstream operations, leadership development, and career advice for the next generation of energy professionals.
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Texas A&M Researchers To Develop AI-Powered Drilling System To Accelerate Critical Mineral DiscoveryBacked by a $3.5 million US Department of Energy grant, Texas A&M researchers are developing an AI-enabled drilling system that can identify critical minerals in real time, reducing exploration time and costs for rare earth element deposits.
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Operational drift in oil and gas starts with small, undocumented deviations that compound over time, making real-time visibility, digital workflows, and frontline execution data critical for preventing safety incidents, compliance failures, and costly operational disruptions.
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Discover how AI and machine learning are transforming oil and gas field development by reducing subsurface uncertainty, optimizing development decisions, and maximizing long-term reservoir value from concept selection through production.
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As upstream operators move beyond isolated experiments, the hardest part of the AI journey is not building a model, it is making it stick.
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