Smart Determination of Difference Index for Asphaltene Stability Evaluation

Precipitation and deposition of asphaltene during different stages of petroleum production is recognized as problematic in oil industry because of the increase in production cost and the inhibition of a consistent flow of crude oil in different medium. Numerous correlations have been developed to determine asphaltene stability in crude oil. In this study, a novel ONN method was used to estimate difference index from SARA fraction data for rapid, accurate, and cost-effective determination of asphaltene stability. Neural networks are highly in danger of trapping in local minima. To eliminate this flaw, a hybrid genetic algorithm-pattern search technique was used instead of common back-propagation algorithm for training the employed neural network. A comparison between neural network and optimized neural network indicated superiority of optimized neural network.

[1]  Johan Sjöblom,et al.  Asphaltene Aggregation from Crude Oils and Model Systems Studied by High-Pressure NIR Spectroscopy , 2002 .

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

[3]  Øystein Brandal,et al.  Our current understanding of water-in-crude oil emulsions. - Recent characterization techniques and high pressure performance , 2003 .

[4]  Kenneth R. Hall,et al.  An algebraic method that includes Gibbs minimization for performing phase equilibrium calculations for any number of components or phases , 2003 .

[5]  S. Andersen,et al.  Study of Pressure and Temperature Effects on Asphaltene Stability in Presence of CO2 , 2006 .

[6]  J. Sjöblom,et al.  Near-IR study on the dispersive effects of amphiphiles and naphthenic acids on asphaltenes in model heptane-toluene mixtures , 2002 .

[7]  J. Sjöblom,et al.  Study of asphaltenes adsorption onto different minerals and clays: Part 1. Experimental adsorption with UV depletion detection , 2008 .

[8]  Shahab D. Mohaghegh,et al.  Virtual-Intelligence Applications in Petroleum Engineering: Part 2—Evolutionary Computing , 2000 .

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

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

[11]  Ali Chamkalani,et al.  Correlations between SARA Fractions, Density, and RI to Investigate the Stability of Asphaltene , 2012 .

[12]  Jill S. Buckley,et al.  Development of a General Method for Modeling Asphaltene Stability , 2009, Energy & Fuels.

[13]  Mahmood Amani,et al.  Assessment of asphaltene deposition due to titration technique , 2013 .

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

[15]  Mojtaba Asoodeh,et al.  Core Porosity Estimation through Different Training Approaches for Neural Network: Back-Propagation Learning vs. Genetic Algorithm , 2013 .

[16]  Mojtaba Asoodeh,et al.  Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems , 2011, Rock Mechanics and Rock Engineering.

[17]  S. Basu,et al.  Statistical analysis on parameters that affect wetting for the crude oil/brine/mica system , 2002 .