|تعداد مشاهده مقاله||103,621,074|
|تعداد دریافت فایل اصل مقاله||81,464,311|
Pressure Loss Estimation of Three-Phase Flow in Inclined Annuli for Underbalanced Drilling Condition using Artificial Intelligence
|Journal of Chemical and Petroleum Engineering|
|مقاله 4، دوره 50، شماره 2، اردیبهشت 2017، صفحه 29-35 اصل مقاله (592.32 K)|
|نوع مقاله: Original Paper|
|شناسه دیجیتال (DOI): 10.22059/jchpe.2017.60502|
|Birjand University of Technology, Birjand, Iran.|
|Underbalanced drilling as multiphase flow is done in oil drilling operation in low pressure reservoir or highly depleted mature reservoir. Correct determination of the pressure loss of three phase fluids in drilling annulus is essential in determination of hydraulic horsepower requirements during drilling operations. In this paper the pressure loss of solid-gas-liquid three-phase fluids flow in inclined annulus was estimated using artificial neural network (ANN). Experimental data which are available in the literature were used for design of ANN. Pressure loss as output of ANN, was estimated from five effective parameters as inputs of ANN including gas and liquid superficial velocities, the inclination from horizontal, rate of penetration (ROP), pipe rotation speed (RPM). The correlation coefficient between predicted and experimental value for train and test data is 0.998 and 0.997 respectively.The root mean square error (RMS) and average absolute percent relative error (AAPE) for train data are 0.0082 and 2.77% and for test data, they are 0.0108 and 3.68 % respectively. The reliable results showed the high ability of artificial neural network for estimating pressure loss of three phase flow in annulus.|
|Underbalanced drilling؛ Pressure loss؛ Three-phase flow؛ ANN؛ Annulus|
 Ramalho, J. (2006). “Underbalanced drilling in the reservoir, An integrated technologyapproach[C].” SPE Russian Oil and Gas Technical Conference and Exhibition, Moscow, Russia.
 Zhiming, W., Liqiu, P., Ke, Z. (2007). “Prediction of dynamic wellbore pressure in gasified fluid drilling.” Petroleum Science , Vol. 4, No. 4, pp. 66-73.
 Lockhart, R.W., Martinelli, R.C. (1949). Proposed correlation of data for isothermal two-phase, two-component flow in pipes.” Chemical Engineering Progress, Vol. 45, No. 1, pp. 39–48.
 Duns, H. Jr., Ros, N.C.J. (1963). “Vertical flow of gas and liquid mixtures in wells.” Proceedings of the 6th World Petroleum Congress, Toyko, Japan.
 Beggs, H.D., Brill, J.P. (1973). “A study of two-phase flow in inclined pipes.” Journal of Petroleum Technology , Vol. 25, No. 5, pp. 607-617.
 Oriol, J., Leclerc, J.P., Jallut, C., Tochon, P., Clement, P. (2008). “Characterization of the two-phase flow regimes and liquid dispersion in horizontal and vertical tubes by using colored tracer and non-intrusive optical detector.” Chemical Engineering Science , Vol. 63, pp. 24-34.
 Bonizzi, M., Andreussi, P., Banerjee, S. (2009). “Flow regime independent, high resolution multi-field modeling of near-horizontal gas– liquid flows in pipelines.” International Journal of Multiphase Flow, Vol. 35, pp. 34-46.
 Sadatomi, M., Sato, Y., Saruwatari, S. (1982). “Two-phase flow in vertical noncircular chan nels.”International Journal of Multiphase Flow Vol. 8, pp. 641-655.
 Hasan, A.R., Kabir, C.S. (1992). “Two-phase flow in vertical and inclined annuli.” International Journal of Multiphase Flow , Vol. 18, pp. 279–293.
 Zhou, L. (2004). “Cuttings transport with aerated mud in horizontal annulus under elevated pressure and temperature conditions.” PhD Thesis, The University of Tulsa.
 Osgouei, R.E. (2010). “Determination of cuttings transport properties of gasified drilling fluids.” PhD Thesis, Middle East Technical University, Ankara, Turkey.
 Wei, N., Meng, Y.F., Li, G., Wan, L.P., Xu, Z.Y., Xu, X.F., Zhang, Y. R. (2013). “Cuttings transport models and experimental visualization of underbalanced horizontal drilling.” Mathemati -cal Problems in Engineering , Vol. 2013, 6 pages.
 Yan, T, Wang, K., Sun, X., Luan, S., Shao S. (2014). “State-of-the-art cuttings transport with aerated liquid and foam in complex structure wells.” Renew Sustainable Energy Review, Vol. 37, pp. 560-568.
 Suradi, S.R., Mamat, N.S., Jafar, M.Z., Sulaiman, W.R.W., Ismail, A.R. (2015). “Study of cuttings transport using stable foam based mud in inclined wellbore.” Journal of applied science , Vol. 15, pp. 808-814.
 Sato, Y., Yoshinaga, T., Sadatomi, M. (1991). “Data and empirical correlation for the mean velocity of coarse particles in a vertical three-phase air-water-solid particle flow.” Proceedings of the international conference on multiphase flows, Tsukuba, Japan.
 Gillies, R.G., Mckibben, M. J., Shook, C. (1997). “Pipeline flow of gas, liquid and sand mixture at low velocity.” Journal of Canadian Petroleum Technology , Vol. 36, pp. 36-42.
 King, M.J.J., Fairhurst, C.P., Hill, T.J. (2001). “Solids transport in multiphase flows: applications to high viscosity systems.” Journal of Energy Resource , Vol. 123, pp. 200-204.
 Yang, Z.L., Ladam, Y., Laux, H., Danielson, T.J., Goldszal, A., Martins, A.L. (2007). “Simulation of sand transport in a stratified gas-liquid two-phase pipe flow.” Proceedings of the BHR Multiphase Production Technology Conference Edinburgh, UK.
 Ozbayoglu, M.E., Osgouei, R.E., Ozbayoglu, A.M., Yuksel, E. (2012). “Hole cleaning performance of gasified drilling fluids in horizontal well sections.” SPE Journal , Vol. 17, No. 3, pp. 912-923.
 Xie, J., Yu B., Zhang, X., Shao, Q., Song, X. (2013). “Numerical simulation of gas-liquid-solid three-phase flow in deep wells.” Advances in Mechanical Engineering , pp. 1-10.
 Hagan, M.T., Demuth, H.B., Beale, M.H. (1996). Neural network design . PWS Publishing, Boston, MA.
 Rooki, R., DoulatiArdejani, F., Moradzadeh, A., Kelessidis, V.C., Nourozi, M. (2012). “Predic tion of terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law pseudoplastic fluid using artificial neural network.” International Journal of Min eral Processing , Vol. 110-111, pp. 53-61.
 Rooki, R, DoulatiArdejani, F, Moradzadeh, A. (2014). “Hole cleaning prediction in foam drilling using artificialneural network and multiple linear regression.” Geomaterials, Vol. 4, No. 1, pp. 47-53.
 Rooki, R. (2015). “Estimation of pressure loss of Herschel–Bulkley drilling fluids during horizontal annulus using artificial neural network.” Journal of Dispersion Science and Technology, Vol. 36, pp. 161-169.
 Osman, E.S.A. (2004). “Artificial neural net work models for identifying flow regimes and predicting liquid holdup in horizontal multiphase flow.” SPE Production and Facilities, Vol. 19, pp. 33-40.
 Shippen, M. E., Scott, S. L. (2004). “A neural network model for prediction of liquid holdup in two-phase horizontal flow.” SPE Production and Facility , Vol. 19, No. 2, pp. 67-76.
 Ozbayoglu, E.M., Ozbayogl, M.A. (2009). “Estimating flow patterns and frictional pressure losses of two-phase fluids in horizontal wellbores using artificial neural networks.” Petrolelum Science and Technology , Vol. 27, pp. 135-149.
 Alizadehdakhel, A., Rahimi, M., Sanjar, J., Alsairafi, A.A. (2009). “CFD and artificial neural network modeling of two-phase flow pressure drop.” International Journal of Heat and Mass Transfer , Vol. 36, pp. 850–856.
 Haykin, S. (1999). “Neural networks: A comprehensive foundation.” 2nd ed. Upper Saddle River, NJ, Prentice Hall.
 Demuth, H., Beale M. (2002). “Neural network toolbox for use with MATLAB.” User’s Guide Version 4.
 Hornik, K., Stinchcombe M., White H. (1989). “Multilayer feed forward networks are univer sal approximators.” Neural Network , Vol. 2, pp. 359-66.
 Cybenko, G. (1989). “Approximation by super position of a sigmoidal function.” Mathematics of Control, Signals, and Systems , Vol. 2, pp. 303- 14.
 Fletcher, D., Goss, E., (1993). “Forecasting with neural networks: an application using bankruptcy data.” Information and Management Vol. 24, pp. 159-167.
تعداد مشاهده مقاله: 1,251
تعداد دریافت فایل اصل مقاله: 1,089