Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm

Rabiei, Arash; Sayyad, Hossein; Riazi, Masoud; Hashemi, Abdolnabi

doi:10.1016/j.fluid.2014.11.027

Cited by 23 publications

(11 citation statements)

References 53 publications

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“…It is especially suitable for dealing with the complex and nonlinear problems which traditional search methods cannot resolve. Comparing with conventional optimization methods, the most highlighted advantage of GA is being not easy to trap in local optima due to many individuals (population) exploring in their search space [26].…”

Section: Basic Theory Of Gamentioning

confidence: 99%

Damage Identification of Urban Overpass Based on Hybrid Neurogenetic Algorithm Using Static and Dynamic Properties

Liu

Wang

Gong

et al. 2015

Mathematical Problems in Engineering

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Urban overpass is an important component of transportation system. Health condition of overpass is essential to guarantee the safe operation of urban traffic. Therefore, damage identification of urban overpass possesses important practical significance. In this paper, finite element model of left auxiliary bridge of Qianjin Overpass is constructed and vulnerable sections of structure are chosen as objects for damage recognition. Considering the asymmetry of Qianjin bridge, change rate of modal frequency and strain ratio are selected as input parameters for hybrid neurogenetic algorithm, respectively. Identification effects of damage location and severity are investigated and discussed. The results reveal that the proposed method can successfully identify locations and severities with single and multiple damage locations; its interpolation ability is better than extrapolation ability. Comparative analysis with BP neural network is conducted and reveals that the damage identification accuracy of hybrid neurogenetic algorithm is superior to BP. The effectiveness between dynamic and static properties as input variable is also analyzed. It indicates that the identification effect of strain ratios is more satisfactory than frequency ratio.

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Section: Basic Theory Of Gamentioning

confidence: 99%

Damage Identification of Urban Overpass Based on Hybrid Neurogenetic Algorithm Using Static and Dynamic Properties

Liu

Wang

Gong

et al. 2015

Mathematical Problems in Engineering

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“…CCE experiments require several sequential pressure decreases in little stages. CVD tests are preferentially done on volatile oils and light hydrocarbons because there is considerable change in the hydrocarbon composition when pressure decreases during the test [7]. Although both tests generate accurate results, the cost and time required to run the test becomes an important limitation.…”

Section: Introductionmentioning

confidence: 99%

“…Peng-Robinson and Soave-Redlich-Kwong equation of state (EOS) models have been used to predict PVT properties of hydrocarbons. These EOS models have to be tuned with experimental data for heptane-plus fraction before they can be used to characterize hydrocarbon phase behaviour [7,10]. The performance of EOS models is generally satisfactory for simple hydrocarbon fluids.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the accuracy of EOS models is dependent on several factors: binary interaction coefficient (BIC), volume shift parameter (S), critical pressure (Pc), critical temperature (Tc) and acentric factor (ω) for the last carbon group [1,11]. These parameters need to be estimated for each component in the gas condensate mixture and this becomes increasingly complicated when characterizing the plus fractions of condensate mixtures [7,[12][13][14]. Empirical correlations are also used to predict dewpoint pressure and it offers a simple, fast estimate of dewpoint pressure for used in planning purposes.…”

Section: Introductionmentioning

confidence: 99%

“…This limits the accuracy of empirical correlations especially when estimating dewpoint pressure at elevated temperature conditions. Many researchers have explored the potential of applying artificially intelligent tools and other robust techniques such as; artificial neural networks (ANNs) [10, [17][18][19], support vector machine (SVM) [16,20], genetic algorithm (GA) [21,22], multi-gene genetic programming approach Hossein [23] and swarm particle optimization (SPO) [16] to predict dewpoint pressure of gas condensate reservoirs.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

et al. 2020

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Accurate knowledge of the dew point pressure for a gas condensate reservoir is necessary for optimizing mitigation operations during field development plan. This study explores the use of machine learning models in predicting the dew point pressure of gas condensate reservoirs. 535 experimental dew point pressure data-points with maximum temperature and pressure of 304 °F and 10,500 psi were used for this analysis. First, a standard multiple linear regression (MLR) was used as a benchmark for comparing the performance of the machine learning models. Multilayer perceptron Neural Networks (MLP) [optimized for the number of neurons and hidden layers], Support Vector Machine (SVM) [using radial basis function kernel] and Decision Tree [Gradient boost Method (GBM) and XG Boost (XGB)] algorithms were used to predict the dew point pressure. The performance of these algorithms was then compared with results obtained from published machine learning models. The input parameters for the model include; gas composition, specific gravity, the molecular weight of the heavier component and compressibility factor. The performances of these algorithms were analysed using root mean square error (RMSE), absolute average relative deviation percentage (AARD %) and coefficient of determination (R 2).

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Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models

Esmaeili-Jaghdan,

Tatar,

Shokrollahi

et al. 2023

Neural Comput & Applic

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Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm

Cited by 23 publications

References 53 publications

Damage Identification of Urban Overpass Based on Hybrid Neurogenetic Algorithm Using Static and Dynamic Properties

Damage Identification of Urban Overpass Based on Hybrid Neurogenetic Algorithm Using Static and Dynamic Properties

Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models

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