Predicting Minimum Miscible Pressure in Injection of Gas to Oil Reservoirs by Data-Based Intelligent Methods

Document Type : Research Article

Authors

Department of Petroleum, Faculty of Chemical Engineering, Oil and Gas, Iran University of Science and Technology, Tehran, Iran

Abstract

CO2 injection processes are among the effective methods for enhanced oil recovery. A key parameter in the design of CO2 injection project is the minimum miscibility pressure (MMP). From an experimental point of view, slim tube displacements test routinely determines the MMP. Because such experiments are very expensive and time-consuming, searching for fast and robust methods for determination of MMP is usually requested. The Neural Network (NN) and Support Vector Machine Regression (SVM) were used to designs networks for estimation MMP. The Networks Trained by trusted data including independent variables. The validity of these new models were successfully approved by comparing the models results to the pure and impure experimental slim-tube CO2-oil MMP and the calculated results for the common pure and impure CO2-oil MMP correlations. The average accuracy of the predicted values for the neural network in terms of coefficient of determination (R2) and mean square error (MSE) are 0.9863 and 0.0018. These values for the SVM regression are 0.9870 and 0.0017, respectively. In addition, the new models could be used for predicting the impure CO2-oil MMP at higher fractions of non-CO2 components (Up to 100% for methane and 50% for hydrogen sulfide).

Keywords

Main Subjects

References

[1] Czarnota R., Janiga D., Stopa J., Wojnarowski P., Determination of Minimum Miscibility Pressure for CO2 and Oil System Using Acoustically Monitored Separator, Journal of CO2 Utilization, 17: 32-36 (2017).
[2] Czarnota R., Janiga D., Stopa J., Wojnarowski P., Kosowski P., Minimum Miscibility Pressure Measurement for CO2 and Oil Using Rapid Pressure Increase Method, Journal of CO2 utilization, 21: 156-161 (2017).
[3] Liu Y., Jiang L., Tang L., Song Y., Zhao J., Zhang Y.,  Minimum miscibility pressure estimation for a CO2/n-decane system in porous media by X-ray CT. Experiments in Fluids, 56(7): 154 (2015).
[4] Liu Y., Jiang L., Tang L., Song Y., Zhao J., Zhang Y., Estimation of minimum miscibility pressure (MMP) of CO2 and liquid   n-alkane systems using an improved MRI technique. Magnetic resonance imaging, 34(2): 97-104 (2016).
[5] Ahmad W., Vakili-Nezhaad G., Al-Bemani A., Al-Wahaibi Y.,  Experimental determination of minimum miscibility pressure. Procedia engineering, 148: 1191-1198 (2016).
[6] Wang Y., Peck D., Analytical calculation of minimum miscibility pressure: comprehensive testing and its application in a quantitative analysis of the effect of numerical dispersion for different miscibility development mechanisms. in SPE/DOE Improved Oil Recovery Symposium. Society of Petroleum Engineers (2000).
[7] Tian Y., Ju B., Yang Y., Wang H., Dong Y., Liu N.,  Estimation of minimum miscibility pressure during CO2 flooding in hydrocarbon reservoirs using an optimized neural network. Energy Exploration & Exploitation, 38(6): 2485-2506 (2020).
[8] Dindoruk B., Johns R., Orr F., Measurement and modeling of minimum miscibility pressure: A state-of-the-art review. SPE Reservoir Evaluation & Engineering, 24(2): 367-389 (2021).
[9] Jia J., Li J., Liang Y., Peng B., Molecular Dynamics Study on Performance of Olefin Sulfonate at the Decane–Water Interface: Effect of Molecular Architecture. Fuel, 308: 122013 (2022).
[10] Zhu S., Yu H., Yan G., Li J., Cao A., Sun C., Miscibility Process of Hydrocarbon Mixture Gas and Crude Oil: Insights from Molecular Dynamics. Industrial & Engineering Chemistry Research, 60(37): 13710-13718 (2021).
[11] Nezhad  A.B., Mousavi S.M., Aghahoseini S., Development of an artificial neural network model to predict CO2 minimum miscibility pressure. Nafta, 62(3-4): 105-108 (2011).
[12] Zhang H., Hou D., Li K., An improved CO2-crude oil minimum miscibility pressure correlation. Journal of Chemistry, (2015).
[13] Chen G., Wang X., Liang Z., Gao R., Sema T., Luo P., Zeng F., Tontiwachwuthikul P., Simulation of CO2-oil minimum miscibility pressure (MMP) for CO2 enhanced oil recovery (EOR) using neural networks. Energy Procedia, 37: 6877-6884 (2013).
[14] Zendehboudi S., Ahmadi M., Shafiei A., Babadagli T., A developed smart technique to predict minimum miscible pressure—EOR implications. The Canadian Journal of Chemical Engineering, 91(7): 1325-1337 (2013).
[15] Zendehboudi S., Ahmadi M., Mohammadzadeh O., Bahadori A., Chatzis I., Thermodynamic investigation of asphaltene precipitation during primary oil production: laboratory and smart technique. Industrial & Engineering Chemistry Research, 52(17): 6009-6013 (2013).
[16] Roosta A., Setoodeh P., Jahanmiri A., Artificial neural network modeling of surface tension for pure organic compounds. Industrial & Engineering Chemistry Research, 51(1): 561-566 (2011).
[17] Kumar K.V., Neural network prediction of interfacial tension at crystal/solution interface. Industrial & Engineering Chemistry Research, 48(8): 4160-4164  (2009).
[18] Ghiasi M.M., Bahadori A., Zendehboudi S., Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network. Journal of Natural Gas Science and Engineering, 17: 26-32 (2014).
[19] Kamari A., Mohammadi A., Bahadori A., Zendehboudi S., A reliable model for estimating the wax deposition rate during crude oil production and processing. Petroleum Science and Technology, 32(23): 2837-2844 (2014).
[20] Zendehboudi S., Elkamel A., Chatzis I., Ahmadi M., Bahadori A.,  Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling. AIChE Journal, 60(5): 1905-1919 (2014).
[21] Kamari A.,  Bahadori A., Mohammadi A., Zendehboudi S.,  New tools predict monoethylene glycol injection rate for natural gas hydrate inhibition. Journal of Loss Prevention in the Process Industries, 33: 222-231 (2015).
[22] Alston  R., Kokolis G., James C., CO2 minimum miscibility pressure: a correlation for impure CO2 streams and live oil systems. Society of Petroleum Engineers Journal, 25(02): 268-274 (1985).
[23] Maklavani A., Vatani A., Moradi B., Tangsirifard J., New minimum miscibility pressure (MMP) correlation for hydrocarbon miscible injections. Brazilian Journal of Petroleum and Gas, 4(10) (2010).
[24] Emera M., Lu J., Genetic Algorithm (GA)-Based Correlations Offer More Reliable Prediction of Minimum Miscibility Pressures (MMP) Between the Reservoir Oil and CO2 or Flue Gas. in Canadian International Petroleum Conference. Petroleum Society of Canada (2007).
[25] Shokir E.M.E.M., CO2–oil minimum miscibility pressure model for impure and pure CO2 streams. Journal of Petroleum Science and Engineering, 58(1-2): 173-185 (2007).
[26] Yellig W., Metcalfe R., Determination and Prediction of CO2 Minimum Miscibility Pressures (includes associated paper 8876). Journal of Petroleum Technology, 32(01): 160-168 (1980).
[27] Emera M.K., Sarma H.K., Use of genetic algorithm to estimate CO2–oil minimum miscibility pressure—a key parameter in design of CO2 miscible flood. Journal of petroleum science and engineering, 46(1-2): 37-52 (2005).
[28] Jaubert J.N., Avaullee L., Souvay J.F., A crude oil data bank containing more than 5000 PVT and gas injection data. Journal of Petroleum Science and Engineering, 34(1-4): 65-107 (2002).
[29] Glaso O., Generalized minimum miscibility pressure correlation (includes associated papers 15845 and 16287). Society of Petroleum Engineers Journal, 25(06): 927-934 (1985).
[30] Chen G., Fu K., Liang Z., Sema T., Li C., Tontiwachwuthikul P., Idem R., The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel, 126: 202-212  (2014).
[31] Dehghani S.M., Sefti M., Ameri A., Kaveh N., Minimum miscibility pressure prediction based on a hybrid neural genetic algorithm. chemical engineering research and design, 86(2): 173-185 (2008).
[32] Fathinasab M., and Ayatollahi S., On the determination of CO2–crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods. Fuel, 173: 180-188 (2016).
[33] Kaydani H., Najafzadeh M., Hajizadeh A., A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming. Journal of Natural Gas Science and Engineering, 21: 625-630 (2014).
[34] Lee J.,  Effectiveness of carbon dioxide displacement under miscible and immiscible conditions. Report RR-40, Petroleum Recovery Inst., Calgary, (1979).
Nashrieh Shimi va Mohandesi Shimi Iran
Volume 42, Issue 3 - Serial Number 109
September 2024
Pages 359-370

Files

Share

How to cite

Statistics