Improving Water Efficiency in the Wilmington Field Using Streamline-Based Surveillance
- Ryan R. Kwong (California Resources Corporation) | Ryan P. Kellogg (California Resources Corporation) | Marco R. Thiele (Streamsim Technologies, Inc. / Stanford University) | David M. Simmons (City of Long Beach Energy Resources)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 23-26 April, San Jose, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Streamline, Reservoir Management, Waterflood Surveillance, Wilmington Field, Waterflood Optimization
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- 111 since 2007
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This paper describes application of a streamline-based surveillance methodology to define injector-centered-patterns using streamlines, calculate pattern efficiencies, and propose target rates for injectors and producers to improve oil production, lower WOR, and reduce water cycling in the Wilmington Field.
The Wilmington Field is a faulted anticline structure in the Los Angeles Basin with alternating sand/shale sequences, which straddles offshore and onshore locations in Long Beach, California. It has been actively waterflooded since 1953 and currently injects over 1.6 million barrels of water injected (BWIPD) and produces at ~98% water cut. Due to the industrial port environment and history of subsidence, a minimum voidage replacement ratio (VRR) is required to sustain stable surface elevations. Given the scale of the Wilmington field, we focused on implementing two pilots to improve the flood performance by targeting different fault blocks/reservoirs: Fault Block 2 Tar Reservoir (Tar 2) and Fault Block 7 Ranger Reservoir (Ranger 7) which together represent 5% of total Wilmington oil production. Both surveillance models used a multi-layered numerical grid with geologic properties from existing 3D geomodels. The key objective for Tar 2 and Ranger 7 was to redistribute the current injected water volumes to improve oil production while maintaining VRR targets. Injection into the Tar 2 model was vertically refined to per-sand using pseudo-injectors in the modeling approach. Injection wells for the Ranger 7 model used a single path and injection volumes were allocated into each sand using permeability-height (kh) values. Rate changes suggested by the surveillance model for both pilot areas were made through choke adjustments and/or well shut-ins and required no pump size changes or workovers.
The Tar 2 and Ranger 7 pilots were monitored over a 6-month and 17-month period, respectively. The Tar 2 pilot area resulted in a decrease from 20% to 2 % annual oil decline rate while keeping a constant injection rate, while the Ranger 7 pilot area new rate target resulted in WOR from 52 to 47 and a decrease in annual oil decline from 15% to 5%. With the success of the waterflood management approach seen in Tar 2 and Ranger 7, a larger area in the Wilmington Field, Fault Block 6 Ranger (23% of total production) is now under a similar evaluation with the goal of reducing WOR and oil decline using the same surveillance methodology.
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Batycky, R. P., Thiele, M. R., Baker, R. O., & Chugh, S. (2008). Revisiting Reservoir Flood-Surveillance Methods Using Streamlines. Society of Petroleum Engineers. doi:10.2118/95402-PA.
Batycky, R. P., & Thiele, M. R. (2018a). Material Balance Applied to Dynamic Reservoir-Surveillance Patterns. Society of Petroleum Engineers. doi:10.2118/185713-PA
Batycky, R. P., & Thiele, M. R. (2018b). Reservoir Pattern Surveillance of Mature Floods Using Streamlines, third edition. San Francisco: Streamsim Technologies (www.lulu.com).
Hara, S. (2007), "Final Report: Class III Mid-Term Project, "Increasing Heavy Oil Reserves in the Wilmington Oil Field Through Advanced Reservoir Characterization and Thermal Production Technologies", Submitted to United States Department of Energy-National Energy Technology Laboratory. DOE Award Number DE-FC22-95BC14939.
James, D. M., Mayer, E. H., & Scranton, J. M. (1967). Use of Economic and Reservoir Models in Planning the Ranger Zone Flood, Long Beach Unit, Wilmington Field. Society of Petroleum Engineers. doi:10.2118/1855-MS
Kellogg, R. P., Chessum W., Kwong, R. (2018). Machine Learning Application for Wellbore Damage Removal in the Wilmington Field. doi: 10.2118/190037-MS
Kornberger, M., & Thiele, M. R., (2014). Experiences With an Efficient Rate-Management Approach for the 8th Tortonian Reservoir in the Vienna Basin. doi:10.2118/166393-PA
Lim, F. H., Saner, W. B., Stilwell, W. H., & Patton, J. T. (1993). Steamflood Pilot Test in Waterflooded, 2,500-ft Tar Zone Reservoir, Fault Block II Unit, Wilmington Field, California. Society of Petroleum Engineers. doi:10.2118/26615-MS
Robertson, J. A., Blesener, J. A., & Hoo, S. S. (1987). Subzone Redevelopment of the Long Beach Unit, Wilmington Oil Field: A Case Study. Society of Petroleum Engineers. doi:10.2118/15101-PA
Thiele, M. R., & Batycky, R. P. (2006). Using Streamline-Derived Injection Efficiencies for Improved Waterflood Management. Society of Petroleum Engineers. doi:10.2118/84080-PA