Assessing the Dynamic Viscosity of Na–K–Ca–Cl–H2O Aqueous Solutions at High-Pressure and High-Temperature Conditions

Most industrial areas, especially oilfield operations and geothermal reservoirs, deal with varying viscosities in multicomponent electrolyte solutions. An accurate estimate of this property as a function of pressure, temperature, and varying salt concentrations is highly desirable. Although a number of empirical correlations have already been developed, they are still limited to single electrolyte solutions and can only operate over specified temperature and pressure ranges. In this study, a highly accurate model based on an adaptive network-based fuzzy inference system was developed, mainly devoted to dynamic viscosity prediction in aqueous multicomponent chloride solutions. Crisp input data were transformed into fuzzy sets employing the subtractive clustering algorithm with an effective radius optimized by a hybrid of genetic algorithm and particle swarm optimization technique. Comparing the model with thousands of experimental data concluded in squared correlation coefficient (R2) of 0.9986 and an aver...

[1]  Ali Elkamel,et al.  Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization , 2013 .

[2]  Mahmut Firat,et al.  Estimating discharge coefficient of semi-elliptical side weir using ANFIS , 2012 .

[3]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[4]  Matthis Thorade,et al.  Density and viscosity of brine: An overview from a process engineers perspective , 2010 .

[5]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[7]  J. Fuhrmann,et al.  Deep reaching fluid flow close to convective instability in the NE German basin—results from water chemistry and numerical modelling , 2005 .

[8]  E. Königsberger,et al.  Properties of electrolyte solutions relevant to high concentration chloride leaching. II. Density, viscosity and heat capacity of mixed aqueous solutions of magnesium chloride and nickel chloride measured to 90 °C , 2008 .

[9]  P. Bedrikovetsky,et al.  Modified Particle Detachment Model for Colloidal Transport in Porous Media , 2011 .

[10]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[11]  Mohammad Jamialahmadi,et al.  Thermodynamics, Kinetics, and Hydrodynamics of Mixed Salt Precipitation in Porous Media: Model Development and Parameter Estimation , 2014, Transport in Porous Media.

[12]  Alireza Bahadori,et al.  Thermodynamic investigation of asphaltene precipitation during primary oil production laboratory and smart technique , 2013 .

[13]  H. Ezzat Khalifa,et al.  Tables of the Dynamic and Kinematic Viscosity of Aqueous KCl Solutions in the Temperature Range 25-150 C and the Pressure Range 0.1-35 MPa, , 1981 .

[14]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[15]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[16]  I. Abdulagatov,et al.  Viscosity of aqueous Na2SO4 solutions at temperatures from 298 to 573 K and at pressures up to 40 MPa , 2005 .

[17]  Sandow Mark Yidana,et al.  Groundwater flow modeling and particle tracking for chemical transport in the southern Voltaian aquifers , 2011 .

[18]  B. M. Fabuss,et al.  Viscosities of binary aqueous solutions of sodium chloride, potassium chloride, sodium sulfate, and magnesium sulfate at concentrations and temperatures of interest in desalination processes , 1968 .

[19]  Farhad Gharagheizi,et al.  Assessment test of sulfur content of gases , 2013 .

[20]  Mason B. Tomson,et al.  Solubility of Barite up to 250 °C and 1500 bar in up to 6 m NaCl Solution , 2012 .

[21]  Mohammad Jamialahmadi,et al.  Estimating the kinetic parameters regarding barium sulfate deposition in porous media: a genetic algorithm approach , 2014 .

[22]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[23]  B. M. Fabuss,et al.  Viscosity of liquid water from 25 to 150.degree. measurements in pressurized glass capillary viscometer , 1968 .

[24]  Hans Müller-Steinhagen,et al.  Mechanisms of scale deposition and scale removal in porous media , 2008 .

[25]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[26]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[27]  D. Ophori Flow of groundwater with variable density and viscosity, Atikokan Research Area, Canada , 1998 .

[28]  J. Kestin,et al.  Viscosity of Liquid Water in the Range - 8 C to 150 C, , 1978 .

[29]  J. R. Coe,et al.  Absolute viscosity of water at 20-degrees-C , 1952 .

[30]  Md. Mustafizur Rahman,et al.  Performance predictions of laminar heat transfer and pressure drop in an in-line flat tube bundle using an adaptive neuro-fuzzy inference system (ANFIS) model , 2014 .

[31]  Masoud Nikravesh,et al.  Soft computing and intelligent data analysis in oil exploration , 2003 .

[32]  Hiroyuki Watanabe,et al.  Application of a fuzzy discrimination analysis for diagnosis of valvular heart disease , 1994, IEEE Trans. Fuzzy Syst..

[33]  Hossein Safari,et al.  Rigorous modeling of gypsum solubility in Na–Ca–Mg–Fe–Al–H–Cl–H2O system at elevated temperatures , 2014, Neural Computing and Applications.

[34]  J. R. Coe,et al.  Absolute Viscosity of Water at 20° C , 1952 .