The Use of Capacitance-Resistive Model for Estimation of Fracture Distribution in the Hydrocarbon Reservoir

M. Delshad, A. Bastami, P. Pourafshary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Capacitance- resistive model is a new method to quantify communication between vertical wells in a reservoir. This method is based on the contribution of each injector on each producer. Capacitance- resistive method uses production and injection rate data to develop and calibrate a model to predict the future performance of the producer wells. The proposed procedure uses a nonlinear signal-processing model to provide information about the connectivity between wells and presence of flow barriers and fractures. In this paper, we present a new method to estimate the fracture distribution in the reservoir using Capacitance-resistive model. We focused on the analysis of the relation between each injector/producer pair. We analyzed the connectivity weight factor of each injector and the neighboring producers to quantify the fracture distribution at each section of the reservoir. Hence, the developed model provides the fast estimation of the fracture distribution using only the flow rate data. The developed model has been tested for different synthetic fractured reservoir case studies. The results are in good agreement in comparison with the fine grid numerical simulation results. It showed that our new approach has the capability to compute the fracture distribution much faster and cheaper than the numerical simulations. By the new developed model, it is possible to estimate the fracture distribution easily and accurately in reservoirs, which has an important role in the field development, drilling new wells and future injection schedule.
Original languageEnglish
Title of host publicationSPE
DOIs
Publication statusPublished - Apr 4 2009

Keywords

  • enhanced recovery
  • Reservoir Characterization
  • Upstream Oil & Gas
  • Artificial Intelligence
  • synthetic field
  • machine learning
  • complex reservoir
  • injector

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