A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

Precise estimation of temperature variations throughout gas production systems can enhance designing the production amenities. Routine methods for determining the temperature profiles in gas production systems are based on the gas composition and flash calculations. However, if the gas compositions are not available, the gas production system can be modelled by employing a black-oil approach, which is also a method for calculating the oil/gas resources and for modelling the gas reservoir operation. Accordingly, for black-oil models and when the natural gas compositions are not accessible, applying robust predictive tools in this research is of high interest in natural production systems. The current study places emphasis on applying the predictive model based on the least- squares support vector machine (LSSVM) to estimate precisely the proper temperature drop associated with a given pressure drop throughout the natural gas production systems based on the black-oil approach to acquire an accurate result for the temperature drop of natural gas streams. Genetic algorithm was used to optimise hyper-parameters (γ and σ2) which are embedded in the LSSVM model. Using this method is simple and it accurately determines the temperature drop through the natural gas stream with minimum uncertainty.

[1]  K. H. Coats,et al.  Simulation of gas condensate reservoir performance , 1985 .

[2]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[3]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[4]  Colin R. Goodall,et al.  13 Computation using the QR decomposition , 1993, Computational Statistics.

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[7]  Ahmed H. El-Banbi,et al.  Investigation of Well Productivity in Gas-Condensate Reservoirs , 2000 .

[8]  Ahmed H. El-Banbi,et al.  Producing Rich-Gas-Condensate Reservoirs—Case History and Comparison Between Compositional and Modified Black-Oil Approaches , 2000 .

[9]  Curtis H. Whitson,et al.  Guidelines for Choosing Compositional and Black-Oil Models for Volatile Oil and Gas- Condensate Reservoirs , 2000 .

[10]  Johan A. K. Suykens,et al.  Automatic relevance determination for Least Squares Support Vector Machines classifiers , 2001, ESANN.

[11]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with , 2003 .

[12]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

[15]  Ruisheng Zhang,et al.  Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines , 2005, J. Comput. Aided Mol. Des..

[16]  Zhide Hu,et al.  Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. , 2005, The journal of physical chemistry. B.

[17]  B. Izgec,et al.  Performance Analysis of Compositional and Modified Black-Oil Models for a Rich Gas Condensate Reservoir , 2005 .

[18]  Ahmed H. El-Banbi,et al.  New Modified Black-Oil Correlations for Gas Condensate and Volatile Oil Fluids , 2006 .

[19]  Chi-Man Vong,et al.  Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference , 2006, Eng. Appl. Artif. Intell..

[20]  J. Valderrama,et al.  Critical Properties, Normal Boiling Temperatures, and Acentric Factors of Fifty Ionic Liquids , 2007 .

[21]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[22]  Guangyi Cao,et al.  Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines , 2008 .

[23]  A. Niazi,et al.  Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines. , 2008, Journal of hazardous materials.

[24]  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..

[25]  Alireza Bahadori,et al.  A Simple Method for Accurate Prediction of Temperature Drops in Natural Gas Production Systems for Black-Oil Models , 2009 .

[26]  Prediction of temperature drop accompanying a given pressure drop for natural gas wellstreams , 2010 .

[27]  M. Ahmadi.  Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm , 2011 .

[28]  M. Reihanian,et al.  Application of neural network and genetic algorithm to powder metallurgy of pure iron , 2011 .

[29]  K. Movagharnejad,et al.  A comparative study between LS-SVM method and semi empirical equations for modeling the solubility of different solutes in supercritical carbon dioxide , 2011 .

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

[31]  M. Ahmadi.  Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

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

[33]  Sohrab Zendehboudi,et al.  Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization , 2012 .

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

[35]  Amir H. Mohammadi,et al.  Experimental Study and Modeling of Ultrafiltration of Refinery Effluents Using a Hybrid Intelligent Approach , 2013 .

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

[37]  Amin Shokrollahi,et al.  Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir , 2013, Appl. Soft Comput..

[38]  Mohammad Ali Ahmadi,et al.  Corrigendum to “Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion” [J. Pet. Sci. Eng. 98–99 (2012) 40–49] , 2013 .

[39]  Alireza Bahadori,et al.  A developed smart technique to predict minimum miscible pressure—eor implications , 2013 .

[40]  Mohammad Ali Ahmadi,et al.  Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs , 2014 .

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

[42]  R. Kharrat,et al.  Gas Analysis by In Situ Combustion in Heavy-Oil Recovery Process: Experimental and Modeling Studies , 2014 .

[43]  Mohammad Ali Ahmadi,et al.  Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach , 2014 .

[44]  Mohammad Ebadi,et al.  Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence , 2014 .

[45]  Mohammad Ali Ahmadi,et al.  Evolving smart approach for determination dew point pressure through condensate gas reservoirs , 2014 .

[46]  M. Ahmadi,et al.  Phase Equilibrium Modeling of Clathrate Hydrates of Carbon Dioxide + 1,4-Dioxine Using Intelligent Approaches , 2015 .

[47]  Alireza Baghban,et al.  Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique , 2015 .

[48]  A. Bahadori,et al.  A LSSVM approach for determining well placement and conning phenomena in horizontal wells , 2015 .

[49]  Mohammad Ali Ahmadi,et al.  Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process , 2015 .

[50]  Mohammad Ali Ahmadi,et al.  Connectionist approach estimates gas–oil relative permeability in petroleum reservoirs: Application to reservoir simulation , 2015 .

[51]  Behzad Pouladi,et al.  Connectionist technique estimates H2S solubility in ionic liquids through a low parameter approach , 2015 .

[52]  A. Bahadori,et al.  A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature , 2015 .

[53]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .