Abstract Artificial Intelligence (AI) is rapidly transforming the oil and gas (O instead, they can operate across the entire O&G value chain, from upstream exploration to downstream refining and distribution, making decisions and executing operations in real time. A pioneering example of this shift is ADNOC's recently launched ENERGYai platform — the industry's first large-scale deployment of Agentic AI. ENERGYai integrates specialized autonomous agents across ADNOC's operations, performing tasks such as seismic interpretation, reservoir management, production optimization, and refining process monitoring. These agents combine domain-specific intelligence with the reasoning capabilities of large language models (LLMs), such as OpenAI's GPT, to enhance decision-making and workflow integration. By leveraging commoditized foundation models and AI orchestration frameworks, ENERGYai demonstrates how O&G companies can achieve significant operational efficiencies, reduce downtime, and improve safety. Agile AI-driven teams are increasingly able to outperform traditional legacy systems by responding to real-time data and conditions, creating a competitive edge through automation and intelligent system coordination. This white paper explores the transformational potential of Agentic AI across upstream, midstream, and downstream segments. Using ENERGYai as a real-world case study, we delve into its applications in seismic data analysis, production forecasting, and autonomous process control. We also examine how combining LLMs with autonomous agents accelerates AI adoption and enables broader organizational agility. However, the adoption of Agentic AI is not without challenges. Data quality, QA/QC protocols, inconsistent data standards (e.g., the need for alignment with the OSDU Data Platform), and concerns around trust and explainability remain key hurdles. This paper addresses how leading O&G players are navigating these issues to unlock the full value of autonomous agents while ensuring operational transparency, governance, and stakeholder trust. In summary, Agentic AI represents a transformative shift in the digitalization of O&G, offering a scalable, intelligent, and adaptive future for energy operations.
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Jessen et al. (Mon,) studied this question.
www.synapsesocial.com/papers/6909452d8f2297dc13532c19 — DOI: https://doi.org/10.2118/229240-ms
Hans Jessen
Mike Roshchin
Inception Institute of Artificial Intelligence
Abu Dhabi National Oil (United Arab Emirates)
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