Agentic AI for Oilfield Optimization: Bridging Expert Workflows and Generative Intelligence in Multi-Field Facilities

Abstract This paper presents the deployment of Agentic AI within ADNOC's Artificial Intelligence Production System Optimization (AiPSO), a strategic initiative aimed at transforming upstream oilfield operations through intelligent automation. At its core, AiPSO embeds domain-specific generative agents into engineering and optimization workflows, enabling autonomous diagnostics, scenario modeling, and decision support. These agents interact with a field-wide digital twin powered by hybrid physics/ML models and a knowledge graph that contextualizes data from IT, OT, and ET domains. The system goes beyond rule-based automation by introducing agents that reason over constraints, simulate outcomes, and proactively recommend actions delivering conversational intelligence grounded in engineering logic and real-time operational data. These capabilities are tightly integrated with foundational workflows including artificial lift diagnostics, MPFM validation, and injection optimization, ensuring adoption and value from day one. Aimed to be deployed across 25 fields, AiPSO will enable uplift in production capacity with minimal CAPEX while reducing decision latency and enhancing operational transparency. The phased architecture of AiPSO ensures scalability, trust, and explainability key to industrial AI. This paper outlines how Agentic AI transforms traditional workflows into intelligent systems, positioning ADNOC's upstream assets for autonomous operations and redefining how human-machine collaboration evolves in energy production