Asphaltene precipitation modeling through ACE reaping of scaling equations

Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning. Herein we propose a reliable model for quantitative estimation of asphaltene precipitation. Scaling equation is the most powerful and popular model for accurate prediction of asphaltene precipitated out of solution in crudes without regard to complex nature of asphaltene. We employed a new mathematical-based approach known as alternating conditional expectation (ACE) technique for combining results of different scaling models in order to increase the accuracy of final estimation. Outputs of three well-known scaling equations, including Rassamdana (RE), Hu (HU), and Ashoori (AS), are input to ACE and the final output is produced through a nonlinear combination of scaling equations. The proposed methodology is capable of significantly increasing the precision of final estimation via a divide-and-conquer principle in which ACE functions as the combiner. Results indicate the superiority of the proposed method compared with other individual scaling equation models.

[1]  Meshal Algharaib,et al.  Accurate Estimation of the World Crude Oil PVT Properties Using Graphical Alternating Conditional Expectation , 2006 .

[2]  Riyaz Kharrat,et al.  An improvement of thermodynamic micellization model for prediction of asphaltene precipitation durin , 2011 .

[3]  Amin Gholami,et al.  Renovating Scaling Equation Through Hybrid Genetic Algorithm-Pattern Search Tool for Asphaltene Precipitation Modeling , 2014 .

[4]  Amin Gholami,et al.  Smart Determination of Difference Index for Asphaltene Stability Evaluation , 2014 .

[5]  Ali Abedini,et al.  Comparison of scaling equation with neural network model for prediction of asphaltene precipitation , 2010 .

[6]  F. Vargas,et al.  Revisiting the PC-SAFT characterization procedure for an improved asphaltene precipitation prediction , 2013 .

[7]  Hariprasad J. Subramani,et al.  Revisiting Asphaltene Deposition Tool (ADEPT): Field Application , 2012 .

[8]  G. Ali Mansoori,et al.  Modeling of asphaltene and other heavy organic depositions , 1997 .

[9]  Riyaz Kharrat,et al.  Monitoring of asphaltene precipitation: Experimental and modeling study , 2011 .

[10]  Modelling of an environmental parameter by use of the alternating conditional expectation method , 1999 .

[11]  E. Boek,et al.  Experimental Investigation of Asphaltene Deposition in Capillary Flow , 2012 .

[12]  Karim Salahshoor,et al.  Asphaltene deposition prediction using adaptive neuro-fuzzy models based on laboratory measurements , 2013 .

[13]  B. Shirani,et al.  Prediction of asphaltene phase behavior in live oil with CPA equation of state , 2012 .

[14]  Davood Rashtchian,et al.  Effect of Miscible Nitrogen Injection on Instability, Particle Size Distribution, and Fractal Structure of Asphaltene Aggregates , 2012 .

[15]  Muhammad Sahimi,et al.  Asphalt Flocculation and Deposition. V. Phase Behavior in Miscible and Immiscible Injections , 1999 .

[16]  Shahin Kord,et al.  Asphaltene Deposition in Carbonate Rocks: Experimental Investigation and Numerical Simulation , 2012 .

[17]  Amin Gholami,et al.  Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network , 2014 .

[18]  T. Guo,et al.  A study on the application of scaling equation for asphaltene precipitation , 2000 .

[19]  Taraneh Jafari Behbahani,et al.  Investigation on Asphaltene Deposition Mechanisms during CO2 Flooding Processes in Porous Media: A Novel Experimental Study and a Modified Model Based on Multilayer Theory for Asphaltene Adsorption , 2012 .

[20]  D. Rashtchian,et al.  Investigation of asphaltene precipitation in miscible gas injection processes: experimental study and modeling , 2012 .

[21]  Xiaolei Tang,et al.  Nonlinear relationship between the real exchange rate and economic fundamentals: Evidence from China and Korea , 2013 .

[22]  T. Guo,et al.  Effect of temperature and molecular weight of n-alkane precipitants on asphaltene precipitation , 2001 .

[23]  J. Friedman,et al.  Estimating Optimal Transformations for Multiple Regression and Correlation. , 1985 .

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

[25]  Abbas Firoozabadi,et al.  Molecular-thermodynamic framework for asphaltene-oil equilibria , 1998 .

[26]  M. Ahmadi,et al.  New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept , 2012 .

[27]  Bahram Hemmateenejad,et al.  Multivariate Curve Resolution Alternating Least-Squares As a Tool for Analyzing Crude Oil Extracted Asphaltene Samples , 2012 .

[28]  Amin Gholami,et al.  Fuzzy Assessment of Asphaltene Stability in Crude Oils , 2014 .

[29]  M. Sahimi,et al.  Asphalt flocculation and deposition: I. The onset of precipitation , 1996 .

[30]  Belal J. Abu Tarboush,et al.  Adsorption of asphaltenes from heavy oil onto in situ prepared NiO nanoparticles. , 2012, Journal of colloid and interface science.

[31]  Graciela M. Escandar,et al.  Multivariate Curve Resolution–Alternating Least-Squares , 2014 .

[32]  Anjushri S. Kurup,et al.  PC-SAFT characterization of crude oils and modeling of asphaltene phase behavior , 2012 .

[33]  Ali Naseri,et al.  Modeling of asphaltene precipitation utilizing Association Equation of State , 2012 .

[34]  A. Datta-Gupta,et al.  Optimal transformations for multiple regression : Application to permeability estimation from well logs , 1997 .

[35]  E. M. El-M. Shokir,et al.  CO2–oil minimum miscibility pressure model for impure and pure CO2 streams , 2007 .

[36]  Amin Gholami,et al.  Prediction of Crude Oil Asphaltene Precipitation Using Support Vector Regression , 2014 .