We are seeing an uptrend in the instrumentation of legacy wells across the world, as costs lower and business cases become obvious. Several interesting technology applications have been showcased lately about retrofitting instrumentation and control technology in legacy wells. Advances on the digital front, where data science and engineering analytics are becoming more embedded in regular production monitoring and optimization processes, have been widespread.
On the gas lift side, developments in surface controllable gas lift valves, which can be deployed during the completion phase, or retrofitted in existing mandrels through well intervention, have proceeded apace. This technology can bring significant improvement in the monitoring and management of wells because the operating envelope can be significantly expanded.
Other technology developments involve the use of alternative data. As the saying goes, somebody’s noise is somebody else’s data. A use case of alternative data for rod pumps uses edge computing to process electric measurements and extract features using signal processing and machine learning. This can be used to create synthetic dynamometer cards used to optimize the wells in real time and predict failures ahead of time. Similar techniques could be applicable for electric submersible pumps.
Distributed acoustic sensing (DAS) has many applications, but it is challenging to fully exploit the capability because of the sheer amount of raw data coming from these sensors. The data need to be compressed using feature-extraction algorithms. Each use case may require data over different regions of the frequency spectrum covered by the sensors, so how can feature extraction be set to account for all present and future use cases? Significant advances in inflow profiling have been achieved over the past few years, correlating DAS signals against flow-loop measurements and transient simulations. These technologies are very promising, especially now that wet connect and pumpdown technology for fiber optics is gaining more attention.
Digital technology for orchestrating production-optimization and reservoir-management work flows has been increasingly embedding machine-learning functionality. Despite these advances, it remains a challenge to maintain these digital solutions over the long term. The most-successful companies in this area ensure the systems are tightly integrated with business-critical work flows, such as integrated activity planning, loss management, locked-in potential management, reservoir-surveillance planning, well-work-opportunity identification, production forecasting, and production back allocation. This involves a significant management of change process. It takes time and effort, but the rewards are worth it.
This Month’s Technical Papers
Study Reviews Downhole Wireless Technologies and Improvements
Distributed Fiber-Optic Sensing Enhances Flow Diagnostics in Gas Condensate Well
Electric Parameters Assist Rod-Pump Condition Diagnosis, Production Metering
Recommended Additional Reading
SPE 207879 Expert Advisory System for Production Surveillance and Optimization Assisted by Artificial Intelligence by Carlos Mata, ADNOC Upstream, et al.
SPE 201313 Production Rate Measurement Optimization Using Test Separator and In-Well Sound Speed by Ö. Haldun Ünalmis, Weatherford
SPE 203119 Wireless Completion Monitoring and Flow Control: A Hybrid Solution for Extended Capabilities by Marcel Bouman, Emerson Automation Solutions
Carlos Mata, SPE, is a senior digital oilfield specialist at ADNOC Upstream. During the past 15 years, he has held roles in the areas of production support, drilling, well integrity, reservoir management, and production optimization. Mata’s latest focus has been on modeling and work-flow automation for production and reservoir management. He currently leads several digital transformation projects at ADNOC. Mata previously worked for Halliburton, Maersk Oil, and North Oil Company. He has worked in multiple assignments in Brazil, the US, the UK, Denmark, Qatar, and the UAE. Mata holds a BS degree in mechanical engineering from Universidad Simón Bolívar and an MS degree in petroleum engineering from Imperial College London. Mata is a member of the JPT Editorial Review Committee and can be reached at cmata@adnoc.ae.