Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs

Ali Shafiei, Mohammad Ali Ahmadi, Maurice B. Dusseault, Ali Elkamel, Sohrab Zendehboudi and Ioannis Chatzis
Additional contact information
Ali Shafiei: Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana 010000, Kazakhstan
Mohammad Ali Ahmadi: Faculty of Engineering & Applied Science, Memorial University, St. John’s, NL A1B3X7, Canada
Maurice B. Dusseault: Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON N2L 3G2, Canada
Ali Elkamel: Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G2, Canada
Sohrab Zendehboudi: Faculty of Engineering & Applied Science, Memorial University, St. John’s, NL A1B3X7, Canada
Ioannis Chatzis: Department of Petroleum Engineering, College of Engineering and Petroleum, Kuwait University, Kuwait City 10002, Kuwait

Energies, 2018, vol. 11, issue 2, 1-29

Abstract: Thermal oil recovery techniques, including steam processes, account for more than 80% of the current global heavy oil, extra heavy oil, and bitumen production. Evaluation of Naturally Fractured Carbonate Reservoirs (NFCRs) for thermal heavy oil recovery using field pilot tests and exhaustive numerical and analytical modeling is expensive, complex, and personnel-intensive. Robust statistical models have not yet been proposed to predict cumulative steam to oil ratio (CSOR) and recovery factor (RF) during steamflooding in NFCRs as strong process performance indicators. In this paper, new statistical based techniques were developed using multivariable regression analysis for quick estimation of CSOR and RF in NFCRs subjected to steamflooding. The proposed data based models include vital parameters such as in situ fluid and reservoir properties. The data used are taken from experimental studies and rare field trials of vertical well steamflooding pilots in heavy oil NFCRs reported in the literature. The models show an average error of <6% for the worst cases and contain fewer empirical constants compared with existing correlations developed originally for oil sands. The interactions between the parameters were considered indicating that the initial oil saturation and oil viscosity are the most important predictive factors. The proposed models were successfully predicted CSOR and RF for two heavy oil NFCRs. Results of this study can be used for feasibility assessment of steamflooding in NFCRs

Keywords: heavy oil; fractured carbonate reservoirs; steamflooding; cumulative steam to oil ratio; recovery factor; statistical predictive tools, digitalization, data analytics (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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