The energy transition and global geopolitical situation present numerous oil and gas operators with opportunities to accelerate production growth and monetize hydrocarbon resources. While rapid production growth comes with proportional risks, it becomes crucial for reservoir surveillance engineers to quickly identify, derisk, and mitigate subsurface performance uncertainties. In this demanding scenario, the need for high-frequency reservoir performance surveillance is more critical than ever. Fortunately, advanced technologies have emerged as a refuge for subsurface engineers facing these challenges.
One such technology is fiber optics, which has matured significantly over the past two decades, allowing for real-time well-performance overviews in various applications. This capability enhances well-management efficiency and enables prompt corrective actions, as demonstrated in papers SPE 200088 and SPE 200806.
Along similar lines, the dynamic nature of the industry has also driven subsurface practitioners to think innovatively and apply existing technologies for out-of-the-box solutions. For instance, tracers, traditionally used for understanding subsurface connectivity, are now being used to calibrate surface networks and optimize gas lift injection performance. Such unconventional approaches have been explored in paper SPE 207431, highlighting their effectiveness. In addition to tracers, technologists are turning rock cuttings, often considered mere waste, into valuable information. As demonstrated by paper SPE 206214, these insights aid in elucidating depositional sequences and fluid-migration paths, thereby facilitating better field development decisions.
Furthermore, technology groups are working on innovative solutions that use advanced sensing and artificial-intelligence (AI) -based technologies. As a result, interventionless downhole pressure and temperature surveying are no longer distant dreams (paper IPTC 22255). In another example, the power of AI has elevated sensing intuition, optimizing reservoir and fluid characterizing operations and leading to improved drilling-cost efficiency and the quality of well delivery (paper SPE 210091).
Embracing advanced technologies is vital for oil and gas operators to optimize production, reduce risks, and make informed decisions in the evolving energy landscape. From real-time fiber optic monitoring to unconventional tracer applications and AI-driven solutions, reservoir surveillance engineers now have an array of tools at their disposal to drive the industry toward a more efficient and sustainable future.
This Month’s Technical Papers
Study Reviews Two Decades of Surveillance Using Distributed Acoustic Sensing
Unconventional Use of Tracer Technology Provides Flow Insights for EOR, IOR
Focused Reservoir Fluid Sampling Uses Artificial Intelligence Technology
Recommended Additional Reading
SPE 200806 An Industry Overview of Downhole Monitoring Using Distributed Temperature Sensing: Fundamentals and 2 Decades Deployment in Oil and Gas Industries by Mohammad Soroush, RGL Reservoir Management, et al.
SPE 206214 Reservoir Architecture and Fluid Connectivity in an Abu Dhabi Oil Accumulation by Erik Tegelaar, Triple EEE, et al.
IPTC 22255 Sensor Ball: Field Deployment of Autonomous and Untethered Surveillance by Mohamed Larbi Zeghlache, Saudi Aramco, et al.
SPE 211543 The Sensor Ball Revolutionizes Wireline Operations by Nasser M. Al-Hajri, Saudi Aramco, et al.
Muhammad Navaid Khan, SPE, is the senior specialist for advanced reservoir solutions at the Abu Dhabi National Oil Company (ADNOC). He has nearly two decades of expertise in diverse Middle Eastern fields. He holds a master’s graduate degree in petroleum engineering from Heriot-Watt University. At ADNOC, he leads digital transformation initiatives for efficient integrated reservoir management. Khan has been involved with SPE as a mentor, section chair, technical judge, and author of technical papers. He was honored with the 2015 SPE Regional Service Award for the Middle East and North Africa Region. Khan is a member of the JPT Editorial Review Board and can be reached at mnavaid@adnoc.ae.