GA-RBF model for prediction of dew point pressure in gas condensate reservoirs

Abstract This study presents the application of an intelligent algorithm in estimation of dew point pressure (DPP) of gas condensate systems. Radial Basis Function (RBF) network in conjunction with Genetic Algorithm (GA) as an optimization algorithm was utilized for this aim. The performance of the proposed GA-RBF model was investigated by statistical and graphical analysis of results. The analysis show that the implemented model is precise and robust in prediction of experimental DPP data. Furthermore, the GA-RBF model was compared with a literature intelligent approach (GEP model) as well as three well-known correlations. The comparison reveals that the GA-RBF model is superior to other model and correlations and successfully improves the predictions.

[1]  Srinivas Bette,et al.  Production Performance of a Retrograde Gas Reservoir: A Case Study of the Arun Field , 1994 .

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

[3]  Ali Danesh,et al.  Phase behavior modeling of gas-condensate fluids using an equation of state , 1991 .

[4]  Ali Barati-Harooni,et al.  A new model for prediction of binary mixture of ionic liquids + water density using artificial neural network , 2016 .

[5]  Amir H. Mohammadi,et al.  Experimental and modeling studies on the effects of temperature, pressure and brine salinity on interfacial tension in live oil-brine systems , 2016 .

[6]  Adel M. Elsharkawy,et al.  Predicting the dew point pressure for gas condensate reservoirs: empirical models and equations of state , 2002 .

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

[9]  Harvey T. Kennedy,et al.  A Correlation of Dewpoint Pressure With Fluid Composition and Temperature , 1967 .

[10]  Dariush Mowla,et al.  Application of expert systems for accurate determination of dew-point pressure of gas condensate reservoirs , 2014 .

[11]  Aboozar Khajeh,et al.  Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network , 2010, Expert Syst. Appl..

[12]  S. H. Huang,et al.  Artificial neural networks in manufacturing: concepts, applications, and perspectives , 1994 .

[13]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[14]  A. Mohammadi,et al.  Comparison of two soft computing approaches for predicting CO2 solubility in aqueous solution of piperazine , 2016 .

[15]  K. Eilerts,et al.  Specific volumes and phase-boundary properties of separator-gas and liquid-hydrocarbon mixtures , 1942 .

[16]  Richard A. Startzman,et al.  Improved neural-network model predicts dewpoint pressure of retrograde gases , 2003 .

[17]  Amir H. Mohammadi,et al.  Accurate prediction of solubility of hydrogen in heavy oil fractions , 2016 .

[18]  R. Santos,et al.  Comparison Between Multilayer Feedforward Neural Networks and Radial Basis Function Network to Detect and Locate Leaks in a Pipeline Transporting Gas , 2013 .

[19]  Bogdan M. Wilamowski,et al.  Implementation of RBF type networks by MLP networks , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[20]  Ilsis Marruffo,et al.  Statistical Forecast Models To Determine Retrograde Dew Pressure and C7+ Percentage of Gas Condensates on Basis of Production Test Data of Eastern Venezuelan Reservoirs , 2001 .

[21]  Curtis H. Whitson,et al.  Modeling Gas-Condensate Well Deliverability , 1996 .

[22]  T. Poggio,et al.  Networks and the best approximation property , 1990, Biological Cybernetics.

[23]  A. G. Spillette,et al.  Gas Condensate Reservoir Behaviour: Productivity and Recovery Reduction Due to Condensation , 1995 .

[24]  B. H. Sage,et al.  Volumetric and Phase Behavior of Oil and Gas from Paloma Field , 1945 .

[25]  Judith E. Dayhoff,et al.  Neural Network Architectures: An Introduction , 1989 .

[26]  B. H. Sage,et al.  Volumetric and Viscosity Studies of Oil and Gas from a San Joaquin Valley Field , 1949 .

[27]  Arne Crogh.  Improved correlations for retrograde gases , 1996 .

[28]  M. A. Al-Marhoun,et al.  A New Correlation for Gas-condensate Dewpoint Pressure Prediction , 2001 .

[29]  Amin Shokrollahi,et al.  Geo-mechanical modeling and selection of suitable layer for hydraulic fracturing operation in an oil reservoir (south west of Iran) , 2015 .

[30]  M. R. Carlson,et al.  Obtaining PVT Data For Very Sour Retrograde Condensate Gas and Volatile Oil Reservoirs: A Multi-disciplinary Approach , 1996 .

[31]  Amir H. Mohammadi,et al.  Rapid method for the estimation of dew point pressures in gas condensate reservoirs , 2016 .

[32]  B. H. Sage,et al.  Volumetric Behavior of Oil and Gas From a Louisiana Field I , 1950 .

[33]  E. I. Organick,et al.  Prediction of Saturation Pressures for Condensate-Gas and Volatile-Oil Mixtures , 1952 .

[34]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[35]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[36]  Amin Shokrollahi,et al.  Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs , 2014 .

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