Accurate prediction of solubility of hydrogen in heavy oil fractions

Abstract The use of heavy oil is increasing due to the exhaustion of the light crude oil wells. Meanwhile, refineries try to intensify their capacity and process the heaviest fractions of petroleum to turn them into a more valuable form. To design and operate hydro-processes, it is of great importance to acquire information about the hydrogen solubility in heavy oils. However, there are some difficulties in the hydrogen solubility measurements and modeling. Consequently, prediction of hydrogen solubility in heavy oils in an accurate and easy way is essential. In this study, reliable model namely Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for prediction of hydrogen solubility in heavy oil fractions based on experimental data gathered from literature. The accuracy of the proposed model was checked using various statistical parameters and graphical representations. The model exhibit overall R2 and AARD% values of 0.99242 and 3.4 which indicate accuracy and robustness of developed model. Result show that the developed model presents accurate results by reproducing the experimental values with an acceptable error.

[1]  A. Tatar,et al.  Application of Radial Basis Function Neural Network for Prediction of Titration-Based Asphaltene Precipitation , 2015 .

[2]  V. Alopaeus,et al.  Hydrogen solubility in heavy oil systems: Experiments and modeling , 2014 .

[3]  A. Mohammadi,et al.  Evaluating the Unloading Gradient Pressure in Continuous Gas-lift Systems During Petroleum Production Operations , 2014 .

[4]  Alireza Bahadori,et al.  Assessing the Dynamic Viscosity of Na–K–Ca–Cl–H2O Aqueous Solutions at High-Pressure and High-Temperature Conditions , 2014 .

[5]  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.

[6]  J. Shaw A correlation for hydrogen solubility in alicyclic and aromatic solvents , 1987 .

[7]  Sadegh Karimpouli,et al.  A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN) , 2010 .

[8]  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 .

[9]  Abdolhossein Hemmati-Sarapardeh,et al.  Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature , 2015, Korean Journal of Chemical Engineering.

[10]  Alireza Bahadori,et al.  Application of soft computing approaches for modeling saturation pressure of reservoir oils , 2014 .

[11]  Abdolhossein Hemmati-Sarapardeh,et al.  A rigorous approach for determining interfacial tension and minimum miscibility pressure in paraffin-CO2 systems: Application to gas injection processes , 2016 .

[12]  J. Vera,et al.  Method to Calculate the Solubilities of Light Gases in Petroleum and Coal Liquid Fractions on the Basis of Their P/N/A Composition , 2005 .

[13]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[14]  Hossein Safari,et al.  On accurate determination of PVT properties in crude oil systems: Committee machine intelligent system modeling approach , 2015 .

[15]  Ian M. Head,et al.  Biological activity in the deep subsurface and the origin of heavy oil , 2003, Nature.

[16]  Clare McCabe,et al.  Predicting the phase equilibria of petroleum fluids with the SAFT‐VR approach , 2007 .

[17]  Kaiyun Fu,et al.  The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process , 2014 .

[18]  J. D. Seader,et al.  A general correlation of vapor‐liquid equilibria in hydrocarbon mixtures , 1961 .

[19]  D. Peng,et al.  A New Two-Constant Equation of State , 1976 .

[20]  J. Ancheyta,et al.  Vapor–liquid equilibrium of hydrogen–hydrocarbon systems and its effects on hydroprocessing reactors , 2014 .

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

[22]  Gabriele Sadowski,et al.  Perturbed-Chain SAFT: An Equation of State Based on a Perturbation Theory for Chain Molecules , 2001 .

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

[24]  J. D. Hemptinne,et al.  Improving the Modeling of Hydrogen Solubility in Heavy Oil Cuts Using an Augmented Grayson Streed (AGS) Approach , 2013 .

[25]  J. Shaw,et al.  Hydrogen solubility measurements in heavy oil and bitumen cuts , 2001 .

[26]  S. E. Wood,et al.  The Derivation of Equations for Regular Solutions , 1933 .

[27]  V. Alopaeus,et al.  A modified continuous flow apparatus for gas solubility measurements at high pressure and temperature with camera system , 2014 .

[28]  Babak Aminshahidy,et al.  A soft computing approach for the determination of crude oil viscosity: Light and intermediate crude oil systems , 2016 .

[29]  K. Gasem,et al.  Solubilities of Hydrogen in Heavy Normal Paraffins at Temperatures from 323.2 to 423.2 K and Pressures to 17.4 MPa , 1995 .

[30]  Afshin Tatar,et al.  Fracture density determination using a novel hybrid computational scheme: a case study on an Iranian Marun oil field reservoir , 2015 .

[31]  A. E. Mather,et al.  Solubility of hydrogen in Athabasca bitumen , 1999 .

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

[33]  Alireza Bahadori,et al.  Prediction of reservoir brine properties using radial basis function (RBF) neural network , 2015 .

[34]  Alireza Bahadori,et al.  Novel methods predict equilibrium vapor methanol content during gas hydrate inhibition , 2013 .

[35]  Amin Shokrollahi,et al.  Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs , 2014 .

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

[37]  Kwang Hyung Lee,et al.  First Course on Fuzzy Theory and Applications , 2005, Advances in Soft Computing.

[38]  M. Riazi,et al.  A method to predict solubility of hydrogen in hydrocarbons and their mixtures , 2007 .

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

[40]  Alireza Bahadori,et al.  Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models , 2016 .

[41]  Zongxian Wang,et al.  Determination of Hydrogen Solubility in Heavy Fractions of Crude Oils by a Modified Direct Method , 2013 .

[42]  G. Scatchard,et al.  Equilibria in Non-electrolyte Solutions in Relation to the Vapor Pressures and Densities of the Components. , 1931 .

[43]  Ali Naseri,et al.  Reservoir oil viscosity determination using a rigorous approach , 2014 .

[44]  K. Chao,et al.  Correlation of solubility of hydrogen in hydrocarbon solvents , 1981 .

[45]  Babak Rezaee,et al.  Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers , 2009, Expert Syst. Appl..

[46]  Mohammad Ali Ahmadi,et al.  Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion , 2012 .

[47]  A. Bahadori,et al.  Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks , 2016 .

[48]  Y. Wang,et al.  A Model of Solubility of Hydrogen in Hydrocarbons and Coal Liquid , 2010 .

[49]  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.

[50]  Walter G Chapman,et al.  Gas solubility in hydrocarbons—a SAFT-based approach , 2003 .

[51]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

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

[53]  Josep C. Pàmies,et al.  Solubility of hydrogen in heavy n‐alkanes: Experiments and saft modeling , 2003 .

[54]  G. G. Streed Vapor-liquid equilibria for high temperatur, high pressure hydrogen e hydrocarbon systems , 1963 .

[55]  Hossein Safari,et al.  A hybrid intelligent computational scheme for determination of refractive index of crude oil using SARA fraction analysis , 2015 .

[56]  F. Gharagheizi,et al.  Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding , 2015 .