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

Ali Shafiei, Mohammad Ali Ahmadi, Maurice Dusseault, Ali Elkamel, Sohrab Zendehboudi, Ioannis Chatzis

Research output: Contribution to journalArticle

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.
LanguageEnglish
Article number292
Number of pages29
JournalEnergies
Volume11
Issue number2
StatePublished - Jan 26 2018

Fingerprint

Petroleum reservoirs
Carbonates
Crude oil
Steam
Recovery
Thermal oil recovery
Oil sands
Regression analysis
Oils
Personnel
Viscosity
Fluids

Keywords

  • heavy oil; fractured carbonate reservoirs; steamflooding; cumulative steam to oil ratio; recovery factor; statistical predictive tools, digitalization, data analytics

Cite this

Shafiei, A., Ahmadi, M. A., Dusseault, M., Elkamel, A., Zendehboudi, S., & Chatzis, I. (2018). Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs. Energies, 11(2), [292].

Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs. / Shafiei, Ali; Ahmadi, Mohammad Ali; Dusseault, Maurice; Elkamel, Ali; Zendehboudi, Sohrab; Chatzis, Ioannis.

In: Energies, Vol. 11, No. 2, 292, 26.01.2018.

Research output: Contribution to journalArticle

Shafiei, A, Ahmadi, MA, Dusseault, M, Elkamel, A, Zendehboudi, S & Chatzis, I 2018, 'Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs' Energies, vol. 11, no. 2, 292.
Shafiei A, Ahmadi MA, Dusseault M, Elkamel A, Zendehboudi S, Chatzis I. Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs. Energies. 2018 Jan 26;11(2). 292.
Shafiei, Ali ; Ahmadi, Mohammad Ali ; Dusseault, Maurice ; Elkamel, Ali ; Zendehboudi, Sohrab ; Chatzis, Ioannis. / Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs. In: Energies. 2018 ; Vol. 11, No. 2.
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