In today’s oil and gas operations, surveillance technologies have undergone a revolution, reshaping how facilities, wells, and reservoirs are monitored. These advancements not only have increased the scale at which these technologies are deployed but also have led to an unprecedented influx of data. The sheer volume of data, however, poses a significant challenge to traditional analytical methods, overwhelming their capacity to derive actionable insights effectively.
To address this challenge, the industry is rapidly advancing toward automated solutions powered by artificial-intelligence (AI) -driven analytics. These systems automate data ingestion and use machine-learning algorithms to sift through massive data sets, identifying anomalies and prioritizing actionable insights. By automating routine surveillance tasks, engineers can focus on critical actions that deliver substantial operational benefits.
For example, in paper IPTC 23912, operators successfully optimized production operations by harnessing real-time field data through smart systems, effectively managing operations with complex near-critical fluids. Similarly, in paper SPE 218470, researchers proposed a novel workflow integrating virtual flowmetering and permanent downhole gauge data for pattern recognition to enhance real-time monitoring and decision-making in petroleum and geothermal industries.
Nevertheless, ensuring effective surveillance of operational assets requires a strategic approach. A valuable tool in crafting such strategies is the value of information (VOI) assessment. This method systematically evaluates how acquiring specific information can influence decision-making and operational outcomes. For instance, paper SPE 215318 highlights a field operator’s systematic approach to VOI assessment, aiming to optimize daily operations and guide future development activities.
In essence, while surveillance technologies have inundated operators with unprecedented data flows, advancements in automation and AI-driven analytics offer the promise of unlocking this data’s true potential. By embracing these technologies, the oil and gas industry can navigate the complexities of the modern energy landscape with greater agility, precision, and cost‑effectiveness.
This Month’s Technical Papers
Solution Enables Well Surveillance in Stacked Reservoirs With Near-Critical Fluids
Automated Workflow Uses Pressure,Rate Measurements for Well Monitoring
Value-of-Information Methods Assess Surveillance Data From Fields to LNG Plants
Recommended Additional Reading
SPE 215119 Surveillance, Analysis, and Optimization During Active Drilling Campaign by Yanfen Zhang, Chevron, et al.
OTC 35413 New Opportunities in Well and Reservoir Surveillance Using Multiple Downhole Pressure Gauges in Deepwater Injector Wells by Piyush Pankaj, ExxonMobil, et al.
OTC 34863 Digital Twin for Oil-Rim Management Using Early Warning System and Exception-Based Surveillance, Offshore Malaysia by M. Mahamad Amir, Petronas, et al.
Muhammad Navaid Khan, SPE, is senior specialist for reservoir engineering at the Abu Dhabi National Oil Company (ADNOC). With nearly 2 decades of experience developing a diverse portfolio of Middle Eastern fields, he currently manages ADNOC’s Integrated Reservoir Management Program. Khan holds a master’s degree in petroleum engineering from Heriot-Watt University. He has been actively involved with SPE, serving as a mentor, section chair, technical judge, and author of technical papers. Currently, Khan chairs SPE’s Integrated Reservoir Management Technical Section and is a member of the JPT Editorial Review Board. His contributions were recognized with the 2015 SPE Regional Service Award for the Middle East and North Africa Region. Khan can be reached at mnavaid@adnoc.ae.