_ Because of the influx of large data from production-monitoring systems, operators are getting creative in dealing with the vast amounts of data. In the process, they learn the state of their operations by extracting knowledge and taking advantage of the useful, newfound information to divulge new insights. Innovative technologies are deployed to replace any continuous, manual processing of real-time data, while the analysts can spend most of their precious time investigating and taking corrective actions in their operations. With the advancements of generative artificial intelligence (AI) and large language models, the use of agentic AI to autonomously deal with vast, heterogeneous data has been observed in complex, distributed environments such as offshore oil and gas production. Greener, energy-efficient hardware also has been notably pursued to reduce environmental footprints while functioning as production monitoring units. A digital solution has been presented in paper OTC 36110 to monitor system performance and identify optimization opportunities in gas lifted wells by orchestrating an intuitive, efficient surveillance system. This routine is based on real-time data analysis and surveillance-by-exception methodology. The methodology combines historical analysis, real-time visualization, and an operational envelope. Historical production and injection data, such as lifting gas rates and production pressures, is used to define an optimized, operational envelope. Comparing this data with the operational envelope framework, the real-time data is visualized with tailored plots to monitor a system of active wells. Any observed deviations reaching preset thresholds, potential problems, or alerts can be detected and addressed promptly to prevent costly disruptions later. Experiences of field evaluation of a nonradioactive multiphase flowmeter (MPFM) have been shared in paper SPE 229654 to highlight a significant advancement in hydrocarbon measurement. Accurate determination of individual flow rates of oil, water, and gas from a commingled stream requires the use of an iterative mass-balance algorithm that reconciles disparate sensor inputs. Evidence of reliable measurements with a nonnuclear MPFM through verification against the corresponding measurements with a conventional test separator in versatile production environments, including heavy-oil and wet-gas applications, has been presented. This study reports the superior economic and environmental benefits of the nonnuclear unit. Agentic AI that consists of a collection of task-specialized AI agents operating autonomously yet collaboratively to achieve a common goal has been introduced in paper SPE 226728. An agent belonging to such a system encompasses machine-learning models, rule-based logic, domain-specific knowledge, and real-time data processors. Being capable of transforming complex-data interpretation, enabling intuitive human/machine interaction, and automating knowledge-intensive tasks, the agentic AI framework has been designed for offshore production surveillance and intervention. The agent uses an independent AI agent for specific tasks while interacting conversationally with users. The study reveals evidence of substantial savings in human time and efforts on specific initiatives. Summarized Papers in This March 2026 Issue OTC 36110 - Real-Time Production-Optimization Routine Deployed for Gas Lifted Wells by Ricardo Y. Tavara La Chira, David E. Bueno, SPE, and Bruno Leite, SPE, Repsol Sinopec Brasil SPE 229654 - Field Tests Evaluate Nonradioactive Multiphase Flowmeters by Ruwayshid Almurayshid, SPE, Hamoud R. Alshammari, SPE, and Eldar Sadikhzada, Saudi Aramco, et al. SPE 226728 - Multiagent AI Improves Offshore Production Surveillance and Intervention by Dushyant S. Shekhawat, Joy Barua, and Karan Bhatia, SPE, SLB, et al. Recommended Additional Reading at OnePetro: www. onepetro. org. SPE 223982 - The Application and Performance Monitoring of Retrofit Autonomous-Inflow-Control-Device Completions in Viscous Oil Horizontal Wells Within a Low-Cost North American Land Asset: A Case Study by Morteza Akbari, TAQA Well Completions, et al. URTeC 4257497 - Interpretation of Production-Inflow Profiles From Distributed-Temperature-Sensing Data and Integration With Completion Observations in Vaca Muerta Wells by Alexei A. Savitski, Shell, et al. SPE 226481 - A Practical Solution To Capture Subsurface Complexity To Match Production Performance at Field F off the Coast of Sarawak, Offshore Malaysia by L. A. Bidin, Petronas, et al.
N.M. Anisur Rahman (Sun,) studied this question.