Data & Analytics
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.
As upstream operators move beyond isolated experiments, the hardest part of the AI journey is not building a model, it is making it stick.
Rodriguez reflects on engineering growth, systems thinking, and how strong technical foundations remain valuable across evolving industries.
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The 2-day event will explore the evolving role of quantum computing in oil and gas applications. -
The event, taking place on 2 April, will explore the theme "Beyond Automation: AI as the Catalyst Reshaping the Oil and Gas Industry."
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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.
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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.
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AI-driven analytics and digital platforms are reshaping offshore operations, enabling smarter, faster decision-making.
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Venezuela’s oil recovery will depend on restoring disciplined, reliable day-to-day operations by stabilizing existing assets, fixing operational failures, and using practical tools to rebuild predictable production.
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The free, virtual program is designed to help participants build data-driven capabilities for the energy industry.
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Digital transformation in oil and gas depends less on adopting advanced technologies and more on maturing data so people and processes can reliably convert raw information into aligned, asset-level value.
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As global electricity demand surges, driven by AI, digitalization, and electrification, offshore innovation, spanning oil and gas, renewables, nuclear, and data infrastructure, will be critical to delivering a cleaner, more resilient energy future.
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Agentic AI can enhance subsurface workflows when its autonomy is deliberately designed around physics, data integrity, and accountable decision-making through architectures that separate reasoning, computation, interpretation, and validation.
