Employing Smart Flow Control Valves for Fast Closed-Loop Reservoir Management
- Mohamed Ahmed Elfeel (Schlumberger) | Trevor Tonkin (Schlumberger) | Shingo Watanabe (Schlumberger) | Hicham Abbas (Schlumberger) | Frode Bratvedt (Schlumberger) | Gordon Goh (Schlumberger) | Varma Gottumukkala (Schlumberger) | Marie Ann Giddins (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 12-15 November, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- Digital oil fields, Manara, Reservoir management, Smart wells, Reactive/Proactive Optimization
- 12 in the last 30 days
- 286 since 2007
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Traditional reservoir management relies on irregular information gathering operations such as surface sampling and production logging followed by one or several treatment operations. The availability of both diagnosis and the prescribed remedial operations can cause severe delays in the reservoir management cycle, increasing unplanned down-time and impacting cash flow. These effects can be exacerbated in remote and offshore fields where well intervention is time-intensive.
A new, innovative, all-electric, flow control valve (FCV) is now a reality for smart completions. This can support any well penetration scenario including multiple zones per lateral in maximum reservoir contact wells and multi-trip completion in extended reach wells. Each zone is equipped with a permanent intelligent flow control valve, allowing real-time reservoir management and providing high-resolution reservoir control. Valve actuation is semi-instantaneous and field data has shown that the frequency of updating such valves is at least 50 times compared to conventional valves, enabling near continuous closed-loop reservoir management. However, such a high frequency optimization demands computational efficiency as it challenges existing optimization applications, particularly when multiple realizations are considered to account for reservoir uncertainty.
In this paper, we present a framework to support field-wide implementation of smart FCVs and hence maintaining a fast closed-loop reservoir management. The framework consists of history matching using Ensemble Kalman Filters (EnKF) where smart FCV data is assimilated to condition a suite of representative reservoir models at a relatively high frequency. Thereafter, a reactive optimizer utilizing a non-linear programming method is applied with the objectives of maximizing instantaneous revenue by determining the optimal positions of the downhole valves under user defined rate, pressure drop, drawdown and setting constraints. This optimization offers production control planning suggestions with the intent of immediate to short-term gain in oil production based upon the downhole measurement and the performance of the near wellbore model. At the same time, a proactive optimizer can be used to determine valve-control settings for longer term objectives such as delaying water/gas breakthrough. The objective of this optimization is equalization of the water/gas front arrival times based upon generation of streamlines and time-of-flight (TOF) analysis. Both modes of optimization are performed efficiently such that a single optimization run is sufficient per geological realization. We use the OLYMPUS reference model, a water flooding case, to demonstrate the workflow. The reactive optimization shows an increase of 25% in the net present value through minimizing water production and increasing injection efficiency, while proactive optimization delays water breakthrough time by 2-4 years. The paper showcases the effectiveness of complementary workflows where high frequency reactive and proactive optimizations support a near continuous closed-loop reservoir management.
|File Size||1 MB||Number of Pages||16|
Abuahmad, Y., Saleh, R., Bouldin, B., Turner, R., & Ali, B. A.-S. (2016). Drilling for the Next Generation of Multilateral Completion Systems. Offshore Technology Conference. doi: 10.4043/27033-MS
Addiego-Guevara, E., Jackson, M. D., & Giddins, M. A. (2008). Insurance Value of Intelligent Well Technology Against Reservoir Uncertainty. Society of Petroleum Engineers. doi: 10.2118/113918-MS
Al-Dossary, F. M., Garcia, J., Fallatah, H. I., Alghamdi, A. A., & Al-Anazi, R. S. (2012). Production Gain and Optimization Through the implementation of the Highest Smart Completion in Saudi Aramco, Case Study. Society of Petroleum Engineers. doi: 10.2118/150369-MS
Alhuthali, A.H., Oyerinde, D., and Datta-Gupta, A. (2007). Optimal Waterflood Management Using Rate Control. SPE Res Eval & Eng 10 (5): 539–551. SPE-102478-PA. doi: 10.2118/102478-PA.
Alhuthali, A.H., Datta-Gupta, A., Yuen, B., and Fontanilla, J.P. (2010). Field Applications of Waterflood Optimization via Optimal Rate Control With Smart Wells. SPE Res Eval & Eng 13 (3): 406–422. SPE-102478-PA. doi: 10.2118/118948-PA.
Al-Khelaiwi, F. T., Zarea, M. A., Al-Khamis, M. N., Al-Ghamdi, A. A., & Al-Amri, M. A. (2014). Intelligent-Field Technologies on a Mass Scale: Change for Efficiency Improvement. International Petroleum Technology Conference. doi: 10.2523/IPTC-17330-MS
Basak, D., & Gurses, S. (2015). Advanced Intelligent Completion Solution for Extended Reach Wells. Society of Petroleum Engineers. doi: 10.2118/176768-MS
Camilleri, L. A. P., & Macdonald, J. (2010). How 24/7 Real-Time Surveillance Increases ESP Run Life and Uptime. Society of Petroleum Engineers. doi: 10.2118/134702-MS
Chan, K. S., Masoudi, R., Karkooti, H., Shaedin, R., & Othman, M. B. (2014). Production Integrated Smart Completion Benchmark for Field Re-Development. International Petroleum Technology Conference. doi: 10.2523/IPTC-17220-MS
Dilib, F. A., & Jackson, M. D. (2012). Closed-loop Feedback Control for Production Optimization of Intelligent Wells under Uncertainty. Society of Petroleum Engineers. doi: 10.2118/150096-MS
Erlandsen, S. M. (2000). Production Experience From Smart Wells in the Oseberg Field. Society of Petroleum Engineers. doi: 10.2118/62953-MS
Grebenkin, I. M., & Davies, D. R. (2012). A Novel Optimisation Algorithm for Inflow Control Valve Management. Society of Petroleum Engineers. doi: 10.2118/154472-MS
Holmes, J. A., Barkve, T., & Lund, O. (1998). Application of a Multisegment Well Model to Simulate Flow in Advanced Wells. Society of Petroleum Engineers. doi: 10.2118/50646-MS
Ilamah, O., & Waterhouse, R. (2018). Field-Scale Production Optimization with Intelligent Wells. Society of Petroleum Engineers. doi: 10.2118/190827-MS
Johansen, K., Jensen, L. J. K., Marsden, C., & Bakshi, S. (2013). Optimizing Water Injection in a Mature Chalk Field by Application of Streamline Simulation. SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA. doi: 10.2118/166336-MS.
Le-Roch, J. F., Fernagu, J., Marvillet, C., Sofyan, M., & Briz-Quintero, X. (2016). Use of Flow Control Valves to Increase Potential and Reserves on a Mature Field. Society of Petroleum Engineers. doi: 10.2118/181387-MS
Mubarak, S., Dawood, N., & Salamy, S. (2009, January 1). Lessons Learned from 100 Intelligent Wells Equipped with Multiple Downhole Valves. Society of Petroleum Engineers. doi: 10.2118/126089-MS
Mubarak, S. M., Pham, T. R., Shamrani, S. S., & Shafiq, M. (2008). Case Study: The Use of Downhole Control Valves To Sustain Oil Production From the First Maximum Reservoir Contact, Multilateral, and Smart Completion Well in Ghawar Field. Society of Petroleum Engineers. doi: 10.2118/120744-PA
Perrons, R. K. (2010). Perdido: The First Smart Field in the Western Hemisphere. Society of Petroleum Engineers. doi: 10.2118/127858-MS
Rossi, D. (2016). The Digital Oilfield and Integrated Operations: Maximizing Values from Online Data. Upstream Technology Leadership Webinar Series, Schlumberger. Link: https://www.slb.com/resources/VideoListingPage/video.aspx?id=B8CA726F-0271-4C93-A3BA-8E9C7F23E747
Shestov, S., Golenkin, M., Senkov, A., Blekhman, V., Gottumukkala, V., & Bulygin, I. (2015). Real-Time Production Optimization of an Intelligent Well Offshore, Caspian Sea. Society of Petroleum Engineers. doi: 10.2118/176648-MS
Spyrou, C. E., La Rosa, A. P., Khataniar, S. K., Uzoechina, F., & Awemo, K. N. (2017) An Approach to Alternative Waterflood Designs and Operations Using Streamline Simulation: Application to an Oil Field in the North German Basin. SPE Middle East Oil & Gas Show and Conference, Manama, Kingdom of Bahrain. doi: 10.2118/183879-MS.
van der Steen, E. (2006). An evolution from Smart Wells to Smart Fields. Society of Petroleum Engineers, 2006, doi: 10.2118/100710-MS
Vasper, A., Mjos, J. E. S., & Duong, T. T. T. (2016, September 6). Efficient Optimization Strategies for Developing Intelligent Well Business Cases. Society of Petroleum Engineers. doi: 10.2118/181062-MS