Data & Analytics
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.
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.
Texas A&M Researchers To Develop AI-Powered Drilling System To Accelerate Critical Mineral Discovery
Backed 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. -
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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Rodriguez reflects on engineering growth, systems thinking, and how strong technical foundations remain valuable across evolving industries.
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How 7 decades of AI research, including the emergence of agentic AI, are reshaping oil, gas, renewables, and the grid.
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In an industry that rarely slows down, memory can be a powerful engineering tool. Not in terms of nostalgia, but in perspective. Many of the activities that constituted daily operations have been so deeply transformed that new generations of engineers may never have experienced them before.
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Machine learning is transforming equipment reliability by enabling predictive maintenance, improving safety, and reducing costly downtime across drilling, production, pipelines, and CCUS operations.
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This article explores how AI is transforming oil and gas operations, including its real impact on methane reduction, predictive maintenance, energy efficiency, and whether it truly delivers measurable sustainability gains or just adds complexity.
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AI is evolving into a practical tool that helps geoscientists and engineers work faster, evaluate more opportunities, and manage subsurface uncertainty.
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Digital drilling technologies are enabling a shift toward more predictive, efficient, and sustainable operations.
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