Toward the Development of a Universal Choke Correlation – Global Optimization and Rigorous Computational Techniques
Abstract
Wellhead choke is an integral part of production systems. Wellhead chokes act as regulators to protect surface facilities from high rate slugs, formation damage, and water coning. These chokes also control the decline rate of reservoir pressure.
Accordingly, a diverse data bank of critical flow across wellhead chokes has been compiled to develop a universal choke performance model. Three models developed to predict liquid flow rate in two-phase flow through chokes. The first two models are measured by computational intelligence paradigms such as the Artificial Neural Network and the Least Square Support Vector Machine. The third model was developed using a global optimization simplex algorithm. The three models outperformed all existing models, and the results clearly highlight the accuracy and superiority of the intelligence models over the existing empirical correlations.
References
Achong, I.B., 1961. Revised bean and performance formula for Lake Maracaibo Wells. Houston.
Al-Attar, H.H., Abdul-Majeed, G.H., 1988. Revised Bean Performance Equation for East Baghdad Oil Wells. SPE Prod. Eng. 3, 127–131. doi:10.2118/13742-PA
Al-Khalifa, M.A., Al-Marhoun, M.A., 2013. Application of Neural Network for Two-Phase Flow through Chokes, in: SPE Saudi Arabia Section Technical Symposium and Exhibition. Society of Petroleum Engineers. doi:10.2118/169597-MS
AlAjmi, M.D., Alarifi, S.A., Mahsoon, A.H., 2015. Improving Multiphase Choke Performance Prediction and Well Production Test Validation Using Artificial Intelligence: A New Milestone, in: SPE Digital Energy Conference and Exhibition. Society of Petroleum Engineers. doi:10.2118/173394-MS
Alrumah, M.K., Bizanti, M.S., 2007. New Multiphase Choke Correlations for Kuwait Fields, in:
SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers. doi:10.2118/105103-MS
Ashford, F.E., Pierce, P.E., 1975. Determining Multiphase Pressure Drops and Flow Capacities in Down-Hole Safety Valves. J. Pet. Technol. 27, 1145–1152. doi:10.2118/5161-PA
Baxendell, P.B., 1958. Producing Wells on Casing Flow-An Analysis of Flowing Pressure Gradients.
Boser, B.E., Laboratories, T.B., Guyon, I.M., Laboratories, T.B., Vapnik, V.N., 1992. A Training Algorithm for Optimal Margin Classifiers, in: Fifth Annual Workshop on Computational Learning Theory. Pittsburgh, pp. 144–152.
Gholgheysari Gorjaei, R., Songolzadeh, R., Torkaman, M., Safari, M., Zargar, G., 2015. A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes. J. Nat. Gas Sci. Eng. 24, 228–237. doi:10.1016/j.jngse.2015.03.013
Gilbert, W., 1954. Flowing and gas-lift well performance. API Drill. Prod. Pract. doi:10.2118
Mesallati Biznati, M., and Mansouri, A., 2000. Multiphase-Flow Choke Correlations for Offshore Bouri Oil Field, in: Proc., International Gas Union 21st World Gas Conference. Nice.
Nelder, J.A., Mead, R., 1965. A Simplex Method for Function Minimization. Comput. J. 7, 308–313. doi:10.1093/comjnl/7.4.308
Owolabi, O., Dune, K., Ajienka, J., 1991. Producing the Multiphase Flow Performance Through Wellhead Chokes for the Niger Delta Oil Wells, in: International Conference of the SPE Nigeria.
Pilehvari, A.A., 1981. Experimental study of critical two-phase flow through wellhead chokes. University of Tulsa.
Poettman, F.H and Beck, R.L., 1963. ew Charts Developed to Predict Gas-Liquid Flow Through Chokes. World Oil.
Ros, N.C.J., 1960. An analysis of critical simultaneous gas/liquid flow through a restriction and its application to flowmetering. Appl. Sci. Res. 9, 374–388. doi:10.1007/BF00382215
Sadeq, D., 2012. Predication of Oil Flow Rate through Choke at Critical Flow for Iraqi Oil Wells. J. Pet. Res. Stud. 2012, 53–79.
Tangren, R.F., Dodge, C.H. and Seifert, H.S., 1949. Compressibilibty Effects in Two Phase Flow. J. Appl. Phys.
Wright, M.H., 2012. Nelder, Mead, and the Other Simplex Method. Doc. Math. I, 271–276.
