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Analysis of Injection and Production Data for Open and Large Reservoirs

Author

Listed:
  • Danial Kaviani

    (Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada)

  • Peter Valkó

    (Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, TX 77843-3116, USA)

  • Jerry Jensen

    (Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
    Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada)

Abstract

Numerous studies have concluded that connectivity is one of the most important factors controlling the success of improved oil recovery processes. Interwell connectivity evaluation can help identify flow barriers and conduits and provide tools for reservoir management and production optimization. The multiwell productivity index (MPI)-based method provides the connectivity indices between well pairs based on injection/production data. By decoupling the effects of well locations, skin factors, injection rates, and the producers’ bottomhole pressures from the calculated connectivity, the heterogeneity matrix obtained by this method solely represents the heterogeneity and possible anisotropy of the formation. Previously, the MPI method was developed for bounded reservoirs with limited numbers of wells. In this paper, we extend the MPI method to deal with cases of large numbers of wells and open reservoirs. To handle open reservoirs, we applied some modifications to the MPI method by adding a virtual well to the system. In cases with large numbers of wells, we applied a model reduction strategy based on the location of the wells, called windowing. Integration of these approaches with the MPI method can quickly and efficiently model field data to optimize well patterns and flood parameters.

Suggested Citation

  • Danial Kaviani & Peter Valkó & Jerry Jensen, 2011. "Analysis of Injection and Production Data for Open and Large Reservoirs," Energies, MDPI, vol. 4(11), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:11:p:1950-1972:d:14784
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    Citations

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    Cited by:

    1. Rafael Wanderley de Holanda & Eduardo Gildin & Jerry L. Jensen & Larry W. Lake & C. Shah Kabir, 2018. "A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting," Energies, MDPI, vol. 11(12), pages 1-45, December.
    2. Qingshun Gong & Zhanguo Liu & Chao Zhu & Bo Wang & Yijie Jin & Zhenghao Shi & Lin Xie & Jin Wu, 2023. "Heterogeneity of a Sandy Conglomerate Reservoir in Qie12 Block, Qaidam Basin, Northwest China and Its Influence on Remaining Oil Distribution," Energies, MDPI, vol. 16(7), pages 1-19, March.

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