One of the fastest evolving trends in the oil and gas industry, intelligent operations is a set of evolving technologies. The evolution of intelligent operations has progressed from real-time monitoring to advanced sensor technologies, sophisticated data analysis, the use of cloud computing, and the integration of artificial intelligence (AI) to optimize and improve operations, thereby enabling better-informed decision-making and ultimately maximize the value of all assets across the entire upstream oil and gas value chain.
Present-day intelligent operations is heavily reliant on large amounts of field measurement data. This typically consists of real-time and continuous field instrumentation, supervisory control and data acquisition, distributed control systems, and databases. All this data is then loaded into a common platform that enables the application of advanced analytics and machine-learning techniques to process the data and uncover trends and patterns in near-real time so that the operator can see at all times when everything is operating under normal or abnormal conditions and be able to predict impending equipment failures.
This enables operators to be able to take their operations to a new level of performance. For example, using AI with an asset such as a well that is continuously monitored can generate regular operational recommendations for the best way to operate the well to maximize well production and reduce operational downtime.
Uncertainty management and digitalization is a key development area in the oil and gas industry. The oil and gas industry is undergoing a significant shift with the advent of intelligent operations. This transformation is enabling upstream operations to move away from a reactive and manual mode of operation toward a more efficient, safe, and optimal state of operation.
Summarized Papers in This May 2026 Issue
SPE 224078 Well-Intervention Operation Planning Becomes a Digital Workflow by Ted Brueren, Equinor, and Stefan Dinger, Stimline Digital
IPTC 24751 AI-Based Technology Enables Real-Time GOR Control in Oil-Rim Reservoir Management by Ahmad Khanifar, SPE, Sai Ravindra Panuganti, SPE, and M. Imran Iskandar B. Ibrahim, Petronas, et al.
SPE 224634 Study Reviews Intelligent Risk-Monitoring Technology for Workover Operations by Shaohui Zhang, SPE, Weihe Huang, SPE, and Zehao Lv, PetroChina, et al.
Recommended Additional Reading
OTC 35768 BR-Loop: Bridging the Gap Between Geosciences and Reservoir Engineering by H.A. Cotrim, Petrobras, et al.
SPE 224864 Takatuf Dashboard: Leveraging Data Analytics and Business Intelligence in the Oil and Gas Industry To Improve PDO Operations and Engineering Efficiency by Fatema Al Ajmi, Petroleum Development Oman, et al.
SPE 225112 Deployment of Innovative Mathematical Decision Model Leveraging Operability and Trouble-Shooting Enhancement for Remote Facilities by S.M. Ahmed, ADNOC, et al.
Cenk Temizel, SPE, is an energy professional with 20 years of experience. He worked at Saudi Aramco; Aera Energy, a Shell/ExxonMobil affiliate; Halliburton; and SLB in the Middle East, the US, and the UK. Before joining the industry, Temizel was a teaching/research assistant at the University of Southern California and Stanford University. He serves as a technical reviewer for petroleum engineering journals and a member of conference committees. Temizel has authored approximately 150 publications in reservoir management, production optimization, enhanced recovery processes, machine learning, and intelligent fields, and holds several US patents. He is the recipient of the SPE Regional Reservoir Description and Dynamics Award. Temizel holds a BS degree from Middle East Technical University and an MS degree from the University of Southern California, both in petroleum engineering.