A developed smart technique to predict minimum miscible pressure-eor implications

Sohrab Zendehboudi, Mohammad Ali Ahmadi, Alireza Bahadori, Ali Shafiei, Tayfun Babadagli

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)

Abstract

Miscible gas injection (MGI) processes such as miscible CO2 flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas-oil minimum miscibility pressure (MMP), which is normally determined through expensive and time-consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas-oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas-oil MMP on the basis of a feed-forward artificial neural network (FF-ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO-ANN has the greatest impact on MMP value. The developed PSO-ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas-oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil-gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO-ANN model failed in providing a good estimate of the oil-gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran.

Original languageEnglish
Pages (from-to)1325-1337
Number of pages13
JournalCanadian Journal of Chemical Engineering
Volume91
Issue number7
DOIs
Publication statusPublished - Jul 1 2013
Externally publishedYes

Keywords

  • EOR
  • Experimental study
  • Minimum miscible pressure
  • Miscible gas flooding
  • Optimised neural network
  • Smart technique

ASJC Scopus subject areas

  • Chemical Engineering(all)

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