Application of evolutionary Gaussian processes regression by particle swarm optimization for prediction of dew point pressure in gas condensate reservoirs

2012 ◽  
Vol 24 (3-4) ◽  
pp. 705-713 ◽  
Author(s):  
Habib Rostami ◽  
Abbas Khaksar Manshad
Keyword(s):  
Particle Swarm Optimization ◽  
Gaussian Processes ◽  
Particle Swarm ◽  
Gas Condensate ◽  
Dew Point ◽  
Swarm Optimization ◽  
Gas Condensate Reservoirs ◽  
Point Pressure ◽  
Dew Point Pressure

Application of Artificial Neural Network–Particle Swarm Optimization Algorithm for Prediction of Gas Condensate Dew Point Pressure and Comparison With Gaussian Processes Regression–Particle Swarm Optimization Algorithm

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Abbas Khaksar Manshad ◽  
Habib Rostami ◽  
Seyed Moein Hosseini ◽  
Hojjat Rezaei
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Equations Of State ◽  
Particle Swarm ◽  
Gas Condensate ◽  
Dew Point ◽  
Data Sets ◽  
Swarm Optimization ◽  
Unseen Data ◽  
Point Pressure

For gas condensate reservoirs, as the reservoir pressure drops below the dew point pressure (DPP), a large amount of valuable condensate drops out and remains in the reservoir. Thus, prediction of accurate values for DPP is important and leads to successful development of gas condensate reservoirs. There are some experimental methods such as constant composition expansion (CCE) and constant volume depletion (CVD) for DPP measurement but difficulties in experimental measurement especially for lean retrograde gas condensate causes to develop of different empirical correlations and equations of state for DPP calculation. Equations of state and empirical correlations are developed for special and limited data sets and for unseen data sets they are not generalizable. To mitigate this problem, in this paper we developed new artificial neural network optimized by particle swarm optimization (ANN-PSO) for DPP prediction. Reservoir fluid composition, temperature and characteristics of the C7+ considered as input parameters to neural network and DPP as target parameter. Comparing results of the developed model in this research with Gaussian processes regression by particle swarm optimization (GPR-PSO), previous models and correlations shows that the predictive model is accurate and is generalizable to new unseen data sets.

Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs

2014 ◽  
Vol 92 (5) ◽  
pp. 891-902 ◽  
Author(s):  
Seyed Mohammad Javad Majidi ◽  
Amin Shokrollahi ◽  
Milad Arabloo ◽  
Ramin Mahdikhani-Soleymanloo ◽  
Mohsen Masihi
Keyword(s):  
Machine Learning ◽  
Gas Condensate ◽  
Dew Point ◽  
Learning Approach ◽  
Accurate Model ◽  
Gas Condensate Reservoirs ◽  
Model Based ◽  
Machine Learning Approach ◽  
Point Pressure ◽  
Dew Point Pressure

scholarly journals Robust correlation to predict dew point pressure of gas condensate reservoirs

Petroleum ◽  
2017 ◽  
Vol 3 (3) ◽  
pp. 340-347 ◽  
Author(s):  
Mohammad Ali Ahmadi ◽  
Adel Elsharkawy
Keyword(s):  
Gas Condensate ◽  
Dew Point ◽  
Gas Condensate Reservoirs ◽  
Point Pressure ◽  
Dew Point Pressure

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

2016 ◽  
Vol 223 ◽  
pp. 979-986 ◽  
Author(s):  
Adel Najafi-Marghmaleki ◽  
Afshin Tatar ◽  
Ali Barati-Harooni ◽  
Mohammad-Javad Choobineh ◽  
Amir H. Mohammadi
Keyword(s):  
Gas Condensate ◽  
Dew Point ◽  
Gas Condensate Reservoirs ◽  
Point Pressure ◽  
Dew Point Pressure
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