Rigorous prognostication of natural gas viscosity: Smart modeling and comparative study

[1]  A. Mohammadi,et al.  Use of Artificial Neural Networks for Estimating Water Content of Natural Gases , 2007 .

[2]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[3]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[4]  Hari S. Viswanathan,et al.  An Integrated Framework for Optimizing CO2 Sequestration and Enhanced Oil Recovery , 2014 .

[5]  Mohammad Amin Anbaz,et al.  Accurate estimation of CO2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN) algorithm , 2017 .

[6]  A. Elkamel,et al.  Asphaltene precipitation and deposition in oil reservoirs –technical aspects, experimental and hybrid neural network predictive tools , 2014 .

[7]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[8]  B. Dabir,et al.  Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system , 2017 .

[9]  Gioia Falcone,et al.  Inaccurate Gas Viscosity at HP/HT Conditions and its Effect on Unconventional Gas Reserves Estimation , 2009 .

[10]  Zhao Yue,et al.  Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost , 2011, 2011 International Conference on Business Management and Electronic Information.

[11]  Johan A. K. Suykens,et al.  Coupled Simulated Annealing , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Tarek Ahmed,et al.  Reservoir Engineering Handbook , 2002 .

[13]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[14]  A. Mohammadi,et al.  Modeling of CO2 solubility in crude oil during carbon dioxide enhanced oil recovery using gene expression programming , 2017 .

[15]  Milad Arabloo,et al.  Laccase immobilized manganese ferrite nanoparticle: synthesis and LSSVM intelligent modeling of decolorization. , 2014, Water research.

[16]  Alireza Rostami,et al.  Fast Estimation of Supercritical CO2 Thermal Conductivity by a Supervised Learning Machine - Implications for EOR , 2017 .

[17]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[18]  Johan A. K. Suykens,et al.  Intelligence and Cooperative Search by Coupled Local Minimizers , 2002, Int. J. Bifurc. Chaos.

[19]  Alireza Rostami,et al.  Toward gene expression programming for accurate prognostication of the critical oil flow rate through the choke: correlation development , 2017 .

[20]  Amir H. Mohammadi,et al.  On the prediction of Watson characterization factor of hydrocarbons , 2017 .

[21]  Viscosity model based on equations of state for hydrocarbon liquids and gases , 1997 .

[22]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[23]  O. Kisi,et al.  Comparison of three back-propagation training algorithms for two case studies , 2005 .

[24]  L. I. Stiel,et al.  The viscosity of nonpolar gas mixtures at moderate and high pressures , 1965 .

[25]  Amir H. Mohammadi,et al.  On the prediction of interfacial tension (IFT) for water-hydrocarbon gas system , 2016 .

[26]  A. Bahadori,et al.  Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network , 2014 .

[27]  Hari S. Viswanathan,et al.  Uncertainty analysis of carbon sequestration in an active CO2-EOR field , 2016 .

[28]  Mohammad Ranjbar,et al.  Artificial neural network for permeability damage prediction due to sulfate scaling , 2011 .

[29]  Ali Elkamel,et al.  Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling , 2014 .

[30]  François Maréchal,et al.  Energy integration on multi‐periods for vehicle thermal powertrains , 2017 .

[31]  Alireza Baghban,et al.  Application of a supervised learning machine for accurate prognostication of higher heating values of solid wastes , 2018 .

[32]  Zhou Feng Advances in Chemical Viscosity-Reducing Methods and Techniques for Viscous Crude Oils , 2001 .

[33]  Davut Hanbay,et al.  Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs , 2009, Expert Syst. Appl..

[34]  Dominique Richon,et al.  An enhanced method to calibrate vibrating tube densimeters , 2001 .

[35]  Alireza Bahadori,et al.  Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure , 2013 .

[36]  Ali Chamkalani,et al.  Integration of LSSVM technique with PSO to determine asphaltene deposition , 2014 .

[37]  Mark White,et al.  CO2 Accounting and Risk Analysis for CO2 Sequestration at Enhanced Oil Recovery Sites. , 2016, Environmental science & technology.

[38]  Robert Balch,et al.  Optimum design of CO 2 storage and oil recovery under geological uncertainty , 2017 .

[39]  Ali Eslamimanesh,et al.  Corresponding States Method for Determination of the Viscosity of Gases at Atmospheric Pressure , 2012 .

[40]  Amin Shokrollahi,et al.  State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity , 2014 .

[41]  Amir Varamesh,et al.  On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment , 2018 .

[42]  A. Shokrollahi,et al.  Accurate prediction of water dewpoint temperature in natural gas dehydrators using gene expression programming approach , 2017 .

[43]  F. Esmaeilzadeh,et al.  Natural gas viscosity estimation through corresponding states based models , 2013 .

[44]  R. P. Sutton Fundamental PVT Calculations for Associated and Gas/Condensate Natural-Gas Systems , 2007 .

[45]  Jingtao Yao,et al.  An Enhanced Support Vector Machine Model for Intrusion Detection , 2006, RSKT.

[46]  Farhad Gharagheizi,et al.  Toward a predictive model for estimating dew point pressure in gas condensate systems , 2013 .

[47]  Martin T. Hagan,et al.  Neural network design , 1995 .

[48]  Tung-Shou Chen,et al.  A Novel Knowledge Protection Technique Base on Support Vector Machine Model for Anti-classification , 2011 .

[49]  E. Shokir,et al.  Genetic Programming (GP)-Based Model for the Viscosity of Pure and Hydrocarbon Gas Mixtures , 2009 .

[50]  A. Hezave,et al.  Artificial neural network as an applicable tool to predict the binary heat capacity of mixtures containing ionic liquids , 2012 .

[51]  Elif Derya Übeyli Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals , 2010, Expert Syst. Appl..

[52]  Brian Lee,et al.  A comparison of neural network backpropagation algorithms for electricity load forecasting , 2013, 2013 IEEE International Workshop on Inteligent Energy Systems (IWIES).

[53]  Amir H. Mohammadi,et al.  A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids , 2015 .

[54]  D. B. Burrows,et al.  Viscosity of Hydrocarbon Gases Under Pressure , 1954 .

[55]  Arash Azamifard,et al.  Toward a predictive model for predicting viscosity of natural and hydrocarbon gases , 2014 .

[56]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[57]  Alireza Bahadori,et al.  Estimation of the silica solubility in the superheated steam using LSSVM modeling approach , 2016 .

[58]  Xingru Wu,et al.  Scale formation in porous media and its impact on reservoir performance during water flooding , 2017 .

[59]  Hao Yu,et al.  Advantages of Radial Basis Function Networks for Dynamic System Design , 2011, IEEE Transactions on Industrial Electronics.

[60]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[61]  Hui Wang,et al.  Using Radial Basis Function Networks for Function Approximation and Classification , 2012 .

[62]  Mario H. Gonzalez,et al.  The Viscosity of Natural Gases , 1966 .

[63]  A. Mohammadi,et al.  On modeling of bitumen/n‐tetradecane mixture viscosity: Application in solvent‐assisted recovery method , 2018 .

[64]  Milad Arabloo,et al.  Robust Modeling Approach for Estimation of Compressibility Factor in Retrograde Gas Condensate Systems , 2014 .

[65]  Alireza Rostami,et al.  Genetic Programming (GP) Approach for Prediction of Supercritical CO2 Thermal Conductivity , 2017 .

[66]  Robert Will,et al.  Co‐optimization of CO2‐EOR and storage processes in mature oil reservoirs , 2017 .

[67]  Amir H. Mohammadi,et al.  Characterizing the CO2-brine interfacial tension (IFT) using robust modeling approaches: A comparative study , 2017 .

[68]  Nian Shong Chok PEARSON'S VERSUS SPEARMAN'S AND KENDALL'S CORRELATION COEFFICIENTS FOR CONTINUOUS DATA , 2010 .

[69]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[70]  Klaus Lucas,et al.  Die Druckabhängigkeit der Viskosität von Flüssigkeiten – eine einfache Abschätzung , 1981 .

[71]  Ali Danesh,et al.  Comparative study of cubic equations of state for predicting phase behaviour and volumetric properties of injection gas-reservoir oil systems , 1991 .

[72]  Amir H. Mohammadi,et al.  Application of ANFIS soft computing technique in modeling the CO2 capture with MEA, DEA, and TEA aqueous solutions , 2016 .

[73]  Mohammad Sharifi,et al.  Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO2-crude oil minimum miscibility pressure , 2017 .

[74]  E. Davani Experimental investigation on high-pressure, high-temperature viscosity of gas mixtures , 2011 .

[75]  Rosalind Archer,et al.  Correlations for Hydrocarbon Gas Viscosity and Gas Density - Validation and Correlation of Behavior Using a Large-Scale Database , 2005 .

[76]  Mohammad Soleimani Lashkenari,et al.  Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations , 2013, Petroleum Science.

[77]  Marc J. Assael,et al.  Viscosity of Natural-Gas Mixtures: Measurements and Prediction , 2001 .

[78]  Adel M. Elsharkawy Modeling the Properties of Crude Oil and Gas Systems Using RBF Network , 1998 .

[79]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[80]  Abdolhossein Hemmati-Sarapardeh,et al.  Modeling interfacial tension and minimum miscibility pressure in paraffin-nitrogen systems: Application to gas injection processes , 2017 .

[81]  Haifeng Wang,et al.  Comparison of SVM and LS-SVM for Regression , 2005, 2005 International Conference on Neural Networks and Brain.

[82]  Alireza Bahadori,et al.  Toward genetic programming (GP) approach for estimation of hydrocarbon/water interfacial tension , 2017 .

[83]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[84]  Farhad Gharagheizi,et al.  Diagnosis of asphaltene stability in crude oil through “two parameters” SVM model , 2012 .

[85]  Marc J. Assael,et al.  Dynamic Viscosity Measurements of Three Natural Gas Mixtures—Comparison against Prediction Models , 2007 .

[86]  Masoud Rahimi,et al.  New correlations to predict natural gas viscosity and compressibility factor , 2010 .

[87]  Ali Selamat,et al.  Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems , 2011, Expert Syst. Appl..

[88]  Alireza Bahadori,et al.  Applying SVM framework for modeling of CO2 solubility in oil during CO2 flooding , 2018 .

[89]  Abdolhossein Hemmati-Sarapardeh,et al.  Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids , 2014 .

[90]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[91]  Mahmood Amani,et al.  Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis , 2016 .

[92]  Efficient methods for calculations of compressibility, density and viscosity of natural gases , 2004 .

[93]  M. El‐Halwagi,et al.  Viscosity Measurements and Data Correlation for Two Synthetic Natural Gas Mixtures , 2010 .

[94]  Amir H. Mohammadi,et al.  On the evaluation of asphaltene precipitation titration data: Modeling and data assessment , 2016 .

[95]  Alireza Rostami,et al.  Investigation of a Novel Technique for Decline Curve Analysis in Comparison with the Conventional Models , 2014 .

[96]  E. Vogel,et al.  Viscosity Measurements and Predictions for Natural Gas , 2004 .

[97]  Ehsan Sanjari,et al.  An accurate empirical correlation for predicting natural gas viscosity , 2011 .

[98]  S. Ayatollahi,et al.  Accurate determination of the CO2‐crude oil minimum miscibility pressure of pure and impure CO2 streams: A robust modelling approach , 2016 .

[99]  Z. A. Chen,et al.  On Viscosity Correlations Of Natural Gas , 1993 .

[100]  Elif Derya íbeyli Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals , 2010 .

[101]  George Thodos,et al.  The viscosity of pure substances in the dense gaseous and liquid phases , 1962 .

[102]  Ehsan Heidaryan,et al.  A new and reliable model for predicting methane viscosity at high pressures and high temperatures , 2010 .

[103]  H. Viswanathan,et al.  Uncertainty quantification for CO2 sequestration and enhanced oil recovery , 2014, 1411.4900.

[104]  Amir H. Mohammadi,et al.  Determination of minimum miscibility pressure in N2–crude oil system: A robust compositional model , 2016 .

[105]  Jason E. Heath,et al.  Evaluation of CO2 Storage Mechanisms in CO2 Enhanced Oil Recovery Sites: Application to Morrow Sandstone Reservoir , 2016 .

[106]  Gioia Falcone,et al.  Rolling Ball Viscometer Calibration with Gas Over Whole Interest Range of Pressure and Temperature Improves Accuracy of Gas Viscosity Measurement , 2012 .

[107]  A. Mohammadi,et al.  A Reliable Model for Estimating the Wax Deposition Rate During Crude Oil Production and Processing , 2014 .