Intelligent operations have been a focus for many parts of our industry for close to 2 decades. Our understanding of what this means, how it can be realized, and the boundaries of what is possible have advanced significantly in this period.
Initial industry focus was around acquiring more real-time data (e.g., pressures and temperatures) along the flow path from reservoir to the point of sale and optimizing overall field performance. From the initial, simpler integrated-asset models, we have advanced to integrated work flows that enable optimal management of very large and complex fields and their equally complex surface facilities (papers SPE 196564 and SPE 197361).
Another area of focus was using smart well completions to optimize production and injection reactively and proactively. Downhole technology and capabilities have matured significantly since the original installations in the late 1990s. The first paper highlighted here, SPE 195935, describes learnings from more than 100 intelligent well completions in presalt fields located in the ultradeepwater Santos Basin offshore Brazil. The industry continues to push the boundary on how this technology can be applied further (papers SPE 195850 and SPE 195361).
The future of intelligent operations in our industry is being driven by advances from other sectors that have been embraced for petroleum applications. Foundational changes already taking place include advances in the type and volume of data being acquired (e.g., distributed-temperature- and distributed-acoustic-sensor data through fiber) and how the data are used. Paper SPE 194127 describes how smarter gauges with microelectromechanical-system inertial measurement sensors can enable more-accurate wellbore-trajectory estimation. We are leveraging the Internet of things, artificial intelligence, and machine-learning technologies capable of handling large volumes of data to analyze and optimize all aspects of upstream operations in real time, as seen in the next two papers highlighted in this section.
In paper SPE 194084, electrical engineers, computer scientists, and drillers collaborate to use deep-learning techniques for real-time classification of cutting volumes at the shale shakers using a video feed from the rig. All of the technology advances also come together in paper OTC 29531, where live data streams from multiple sources, including wave states, motion sensors, and pressure and temperature gauges, are used with machine learning to develop a live fatigue counter for the risers on a floating production, storage, and offloading vessel and establish that facility life extension is possible.
However, especially in these uncertain times, frequent hand washing with soap might indeed be the most intelligent operation of all. Stay safe!
This Month's Technical Papers
Fatigue Prediction for Extended Riser Life and Improved Vessel-Response Analysis
Intelligent Completion Installations Instrumental in Brazilian Presalt Development
Deep-Learning Techniques Classify Cuttings Volume of Shale Shakers
Recommended Additional Reading
SPE 197361 An Integrated Wells-to-Process-Facility Model for the Greater Burgan Oil Field by Sameer Faisal Desai, Kuwait Oil Company, et al.
SPE/IADC 194127 Intelligent Wellbore Path Estimation Using Multiple Integrated Microelectromechanical Systems Sensors by Huan Liu, University of Calgary, et al.
| Keshav Narayanan, SPE, is a principal reservoir engineer with BHP in the Houston office. He has more than 22 years of experience in reservoir management, optimization of reservoir performance, and reserves estimation. Narayanan has worked on a variety of greenfield and brownfield projects in the US, Europe, and the Middle East. He holds a BS degree in chemical engineering from the Indian Institute of Technology, Madras, and an MS degree in petroleum engineering from The University of Texas at Austin. In addition to serving on the JPT Editorial Committee, Narayanan also currently serves on program subcommittees for SPE’s 2020 Annual Technical Conference and Exhibition. He can be reached at www.linkedin.com/in/keshav-narayanan-328576. |
