A Prediction Method for Development Indexes of Waterflooding Reservoirs Based on Modified Capacitance–Resistance Models

Libing Fu (), Lun Zhao, Song Chen, Anzhu Xu, Jun Ni and Xuanran Li
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Libing Fu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Lun Zhao: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Song Chen: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Anzhu Xu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Jun Ni: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Xuanran Li: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China

Energies, 2022, vol. 15, issue 18, 1-16

Abstract: Capacitance–resistance models (CRMs) are semi-analytical methods to estimate the production rate of either an individual producer or a group of producers based on historical observed production and injection rates using material balance and signal correlations between injectors and producers. Waterflood performance methods are applied to evaluate the waterflooding performance effect and to forecast the development index on the basis of Buckley–Leverett displacement theory and oil–water permeability curve. In this case study, we propose an approach that combines a capacitance–resistance model (CRM) modified by increasing the influence radius on the constraints and a waterflood performance equation between oil cut and oil accumulative production to improve liquid and oil production prediction ability. By applying the method, we can understand the waterflood performance, inter-well connectivities between injectors and producer, and production rate fluctuation better, in order to re-just the water injection and optimize the producers’ working parameters to maximize gain from the reservoir. The new approach provides an effective way to estimate the conductivities between wells and production rates of a single well or well groups in CRMs. The application results in Kalamkas oilfield show that the estimated data can be in good agreement with the actual observation data with small fitting errors, indicating a good development index forecasting capability.

Keywords: capacitance–resistance model; influencing radius; regression fitting; waterflooding performance equation; development index prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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