New model for standpipe pressure prediction while drilling using Group Method of Data Handling

Caenn, 1996, Drilling fluids: state of the art, J. Petrol. Sci. Eng., 14, 221, 10.1016/0920-4105(95)00051-8 Skalle, 2011 Whittaker, 1985 Maglione, 1996, A computer program to predict stand pipe pressure while drilling using the drilling well as viscometer Mitsuishi, 1974, Non-Newtonian fluid flow in an eccentric annulus, J. Chem. Eng. Jpn., 6, 402, 10.1252/jcej.6.402 Uner, 1988, An approximate solution for non-Newtonian flow in eccentric annuli, Ind. Eng. Chem. Res., 27, 698, 10.1021/ie00076a028 Langlinais, 1983, Frictional pressure losses for the flow of drilling mud and mud/gas mixtures McCann, 1995, Effects of high-speed pipe rotation on pressures in narrow annuli, SPE Drill. Complet., 10, 96, 10.2118/26343-PA Haciislamoglu, 1994, Practical pressure loss predictions in realistic annular geometries Maglione, 1996, Field rheological parameters improve stand pipe pressure prediction while drilling Ahmed, 2005 Kelessidis, 2006, Optimal determination of rheological parameters for Herschel–Bulkley drilling fluids and impact on pressure drop, velocity profiles and penetration rates during drilling, J. Petrol. Sci. Eng., 53, 203, 10.1016/j.petrol.2006.06.004 Founargiotakis, 2008, Laminar, transitional and turbulent flow of Herschel–Bulkley fluids in concentric annulus, Can. J. Chem. Eng., 86, 676, 10.1002/cjce.20074 Sorgun, 2011, Predicting frictional pressure loss during horizontal drilling for non-Newtonian fluids, Energy Sources, Part A Recover, Util. Environ. Eff., 33, 631 Wold, 2015 Sterri, 2000, Frictional pressure losses during drilling: drill string rotation effects on axial flow of shear thinning fluids in an eccentric annulus, Oil Gas Eur. Mag., 26, 30 Ogugbue, 2009, Friction pressure correlations for oilfield polymeric solutions in eccentric annulus, Int. Conf. Offshore Mech. Arctic Eng., 583 Anifowoshe, 2012, The effect of equivalent diameter definitions on frictional pressure loss estimation in an annulus with pipe rotation Saasen, 2014, Annular frictional pressure losses during drilling—predicting the effect of drillstring rotation, J. Energy Resour. Technol., 136, 10.1115/1.4026205 Rooki, 2016, Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel–Bulkley drilling fluids in oil drilling, Measurement, 85, 184, 10.1016/j.measurement.2016.02.037 Rooki, 2015, Estimation of pressure loss of Herschel–Bulkley drilling fluids during horizontal annulus using artificial neural network, J. Dispersion Sci. Technol., 36, 161, 10.1080/01932691.2014.904793 Chowdhury, 2009 Menad, 2019, Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: application to thermal enhanced oil recovery processes, Fuel, 242, 649, 10.1016/j.fuel.2019.01.047 Hemmati-Sarapardeh, 2018, On the evaluation of the viscosity of nanofluid systems: modeling and data assessment, Renew. Sustain. Energy Rev., 81, 313, 10.1016/j.rser.2017.07.049 Ahmadi, 2014, Evolving smart approach for determination dew point pressure through condensate gas reservoirs, Fuel, 117, 1074, 10.1016/j.fuel.2013.10.010 Elkatatny, 2018, New approach to optimize the rate of penetration using artificial neural network, Arabian J. Sci. Eng., 43, 6297, 10.1007/s13369-017-3022-0 Elkatatny, 2019, Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network, Arab. J. Geosci., 12, 19, 10.1007/s12517-018-4185-z Zhao, 2019, A new methodology for optimization and prediction of rate of penetration during drilling operations, Eng. Comput., 1 Al-AbdulJabbar, 2018, Predicting rate of penetration using artificial intelligence techniques Youcefi, 2020, Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm, Earth Sci. Informatics., 13, 1351, 10.1007/s12145-020-00524-y Elkatatny, 2017, Real-time prediction of rheological parameters of KCl water-based drilling fluid using artificial neural networks, Arabian J. Sci. Eng., 42, 1655, 10.1007/s13369-016-2409-7 Elkatatny, 2018, A new approach to determine the rheology parameters for water-based drilling fluid using artificial neural network, Soc. Pet. Eng. - SPE Kingdom Saudi Arab. Annu. Tech. Symp. Exhib. Bispo, 2017, Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids, J. Petrol. Sci. Eng., 150, 69, 10.1016/j.petrol.2016.11.030 Ahmadi, 2018, An accurate model to predict drilling fluid density at wellbore conditions, Egypt, J. Petrol., 27, 1 Ahmadi, 2016, Toward reliable model for prediction drilling fluid density at wellbore conditions: a LSSVM model, Neurocomputing, 211, 143, 10.1016/j.neucom.2016.01.106 Khosravanian, 2016, Weight on drill bit prediction models: sugeno-type and Mamdani-type fuzzy inference systems compared, J. Nat. Gas Sci. Eng., 36, 280, 10.1016/j.jngse.2016.10.046 Toreifi, 2014, New method for prediction and solving the problem of drilling fluid loss using modular neural network and particle swarm optimization algorithm, J. Pet. Explor. Prod. Technol., 10.1007/s13202-014-0102-5 Kumar, 2020, Machine learning methods for herschel–bulkley fluids in annulus: pressure drop predictions and algorithm performance evaluation, Appl. Sci., 10, 2588, 10.3390/app10072588 Ivakhnenko, 1968, The group method of data of handling; a rival of the method of stochastic approximation, Sov. Autom. Control, 13, 43 Farlow, 1984 Ghazanfari, 2017, Evaluation of GMDH and MLP networks for prediction of compressive strength and workability of concrete, Bull. La Société R. Des Sci. Liège., 86, 855, 10.25518/0037-9565.7032 Menad, 2019, Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming, Eng. Appl. Comput. Fluid Mech., 13, 724 Gascón-Moreno, 2013, An evolutionary-based hyper-heuristic approach for optimal construction of group method of data handling networks, Inf. Sci., 247, 94, 10.1016/j.ins.2013.06.017 Savins, 1954, A direct-indicating viscometer for drilling fluids Soares, 2019, Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models, J. Petrol. Sci. Eng., 172, 934, 10.1016/j.petrol.2018.08.083 Braga, 2019 Schwertman, 2004, The effect of cuttings on annular pressure loss, Comput. Stat. Data Anal., 47, 165, 10.1016/j.csda.2003.10.012 Bakar, 2017, Identification of non-equilibrium growth for bitcoin exchange rate: mathematical derivation method in islamic financial engineering, Int. J. Sci. Res. Manag., 5, 7772 Amar, 2020, Application of gene expression programming for predicting density of binary and ternary mixtures of ionic liquids and molecular solvents, J. Taiwan Inst. Chem. Eng. Nait Amar, 2020, Prediction of lattice constant of A2XY6 cubic crystals using gene expression programming, J. Phys. Chem. B, 124, 6037, 10.1021/acs.jpcb.0c04259 Amar, 2020, Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods, Int. J. Hydrogen Energy, 45, 33274, 10.1016/j.ijhydene.2020.09.145 Amar, 2020, Prediction of CO2 diffusivity in brine using white-box machine learning, J. Petrol. Sci. Eng., 190, 107037, 10.1016/j.petrol.2020.107037 Arakkal, 2008, Early detection of drillstring washouts based on downhole turbine RPM monitoring prevents twist-offs in challenging drilling environment in India