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A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

Author

Listed:
  • Rafael Wanderley de Holanda

    (Petroleum Engineering Department, Texas A&M University, College Station, TX 77843-3116, USA
    Current address: Petrobras America Inc., 10350 Richmond Ave, Houston, TX 77042, USA.)

  • Eduardo Gildin

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

  • Jerry L. Jensen

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

  • Larry W. Lake

    (Department of Petroleum and Geosystems Engineering, University of Texas, Austin, TX 78712-1585, USA)

  • C. Shah Kabir

    (Department of Petroleum Engineering, University of Houston, Houston, TX 77204-0945, USA)

Abstract

Capacitance resistance models (CRMs) comprise a family of material balance reservoir models that have been applied to primary, secondary and tertiary recovery processes. CRMs predict well flow rates based solely on previously observed production and injection rates, and producers’ bottomhole pressures (BHPs); i.e., a geological model and rock/fluid properties are not required. CRMs can accelerate the learning curve of the geological analysis by providing interwell connectivity maps to corroborate features such as sealing faults and channels, as well as diagnostic plots to determine sweep efficiency and reservoir compartmentalization. Additionally, it is possible to compute oil and water rates by coupling a fractional flow model to CRMs which enables, for example, optimization of injected fluids allocation in mature fields. This literature review covers the spectrum of the CRM theory and conventional reservoir field applications, critically discussing their advantages and limitations, and recommending potential improvements. This review is timely because over the last decade there has been a significant increase in the number of publications in this subject; however, a paper dedicated to summarize them has not yet been presented.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3368-:d:187068
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Daigang Wang & Yong Li & Jing Zhang & Chenji Wei & Yuwei Jiao & Qi Wang, 2019. "Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs," Energies, MDPI, vol. 12(5), pages 1-15, March.
    2. Peyman Bahrami & Farzan Sahari Moghaddam & Lesley A. James, 2022. "A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering," Energies, MDPI, vol. 15(14), pages 1-32, July.
    3. Libing Fu & Lun Zhao & Song Chen & Anzhu Xu & Jun Ni & Xuanran Li, 2022. "A Prediction Method for Development Indexes of Waterflooding Reservoirs Based on Modified Capacitance–Resistance Models," Energies, MDPI, vol. 15(18), pages 1-16, September.

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