Prediction of frictional pressure loss for multiphase flow in inclined annuli during Underbalanced Drilling operations

Natural Gas Industry B - Tập 3 - Trang 275-282 - 2016
Ali Barati-Harooni1, Adel Najafi-Marghmaleki1, Afshin Tatar2, Milad Arabloo2, Le Thi Kim Phung3, Moonyong Lee4, Alireza Bahadori5
1Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
2Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran
3Department of Chemical Process and Equipment, Faculty of Chemical Engineering, Hochiminh City University of Technology, Hochiminh City, Viet Nam
4School of Chemical Engineering, Yeungnam University, Gyeungsan, Republic of Korea
5Southern Cross University, School of Environment, Science and Engineering, PO Box 157, Lismore, NSW, Australia

Tài liệu tham khảo

Shayegi, 2007, Comparison of reservoir knowledge, drilling benefits and economic advantages of UB, MP, Manag Press Drill, 60

Guo, 2002, An innovation in designing underbalanced drilling flow rates: a gas-liquid rate window (GLRW) approach

Rooki, 2014, Hole cleaning prediction in foam drilling using artificial neural network and multiple linear regression, Geomaterials, 4, 47, 10.4236/gm.2014.41005

Arabloo, 2014, Experimental studies on stability and viscoplastic modeling of colloidal gas aphron (CGA) based drilling fluids, J Pet Sci Eng, 113, 8, 10.1016/j.petrol.2013.12.002

Arabloo, 2012, Characterization of colloidal gas aphron-fluids produced from a new plant based surfactant, J Disper Sci Technol, 34, 669, 10.1080/01932691.2012.683989

Nareh'ei, 2012, Rheological and filtration loss characteristics of colloidal gas aphron based drilling fluids, J Jpn Pet Inst, 55, 182, 10.1627/jpi.55.182

Totten P, King BL, Griffith JE. Method of performing well drilling operations with a foamable drilling fluid. US5851960 A; 1998.

Perez-Tellez, 2003

Vieira, 2002, Minimum air and water flow rates required for effective cuttings transport in high angle and horizontal wells

Caetano, 1992, Upward vertical two-phase flow through an annulus—Part I: single phase friction factor, Taylor bubble rise velocity, and flow pattern prediction, J Energy Resour Technol, 114, 1, 10.1115/1.2905917

Sadatomi, 1982, Two-phase flow in vertical noncircular channels, Int J Multiph Flow, 8, 641, 10.1016/0301-9322(82)90068-4

Beggs, 1973, vol. 25, 607

Ozbayoglu, 2009, Estimating flow patterns and frictional pressure losses of two-phase fluids in horizontal wellbores using artificial neural networks, Pet Sci Technol, 27, 135, 10.1080/10916460701700203

Taitel, 1976, A model for predicting flow regime transitions in horizontal and near horizontal gas-liquid flow, AIChE J, 22, 47, 10.1002/aic.690220105

Hao, 2011, Advantages of radial basis function networks for dynamic system design, IEEE Trans Ind Electron, 58, 5438, 10.1109/TIE.2011.2164773

Dayhoff, 1990

Huang, 1994, Artificial neural networks in manufacturing: concepts, applications, and perspectives, IEEE Trans Compon Packag Manuf Technol A, 17, 212, 10.1109/95.296402

Lowe, 1988, Multivariable functional interpolation and adaptive networks, Complex Syst, 2, 321

Zurada, 1992

Du, 2006, 251

Shan, 2002, Fast principal component extraction by a weighted information criterion, IEEE Trans Signal Process, 50, 1994, 10.1109/TSP.2002.800395

Park, 1991, Universal approximation using radial-basis-function networks, Neural Comput, 3, 246, 10.1162/neco.1991.3.2.246

Devijver, 1982

Tatar, 2016, Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models, Int J Greenh Gas Control, 47, 122, 10.1016/j.ijggc.2016.01.048

Tatar, 2016, Comparison of two soft computing approaches for predicting CO2 solubility in aqueous solution of piperazine, Int J Greenh Gas Control, 53, 85, 10.1016/j.ijggc.2016.07.037

Tatar, 2016, An accurate model for predictions of vaporization enthalpies of hydrocarbons and petroleum fractions, J Mol Liq, 220, 192, 10.1016/j.molliq.2016.04.069

Tatar, 2016, Implementing radial basis function neural networks for prediction of saturation pressure of crude oils, Pet Sci Technol, 34, 454, 10.1080/10916466.2016.1141217

Najafi-Marghmaleki, 2016, A new model for prediction of binary mixture of ionic liquids + water density using artificial neural network, J Mol Liq, 220, 232, 10.1016/j.molliq.2016.04.085

Nasery, 2016, Accurate prediction of solubility of hydrogen in heavy oil fractions, J Mol Liq, 222, 933, 10.1016/j.molliq.2016.07.083

Darwin, 1859, 247

Hemmati-Sarapardeh, 2016, A soft computing approach for the determination of crude oil viscosity: light and intermediate crude oil systems, J Taiwan Inst Chem Eng, 59, 1, 10.1016/j.jtice.2015.07.017