The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
Amir H Mohammadi - One of the best experts on this subject based on the ideXlab platform.
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ga rbf model for prediction of Dew Point Pressure in gas condensate reservoirs
Journal of Molecular Liquids, 2016Co-Authors: Adel Najafimarghmaleki, Amir H Mohammadi, Afshi Tata, Ali Aratiharooni, Mohammadjavad ChoobinehAbstract: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.
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toward a predictive model for estimating Dew Point Pressure in gas condensate systems
Fuel Processing Technology, 2013Co-Authors: Milad Arabloo, Amin Shokrollahi, Farhad Gharagheizi, Amir H MohammadiAbstract:Abstract Dew-Point Pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to present a reliable, computer-based predictive model for prediction of Dew-Point Pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data Points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of the model. The results showed that the developed model significantly outperforms all the existing methods and provide predictions in acceptable agreement with experimental data. In addition, it is shown that the proposed model is capable of simulating the actual physical trend of the Dew-Point Pressure versus temperature for a constant composition fluid on the phase envelope.
Mohammad Ali Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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robust correlation to predict Dew Point Pressure of gas condensate reservoirs
Petroleum, 2017Co-Authors: Mohammad Ali Ahmadi, Adel M ElsharkawyAbstract:Abstract When the bottom-hole flowing Pressure in a gas condensate reservoir drops below the Dew Point Pressure, liquid starts to build up around the well bore resulting in gas productivity decline. For this reason it is important to be able to accurately either measure or estimate the Dew Point Pressure. The condensate formed in the reservoir will not flow until its saturation reaches the critical saturation and in many cases it might not be entirely recovered. It order to maximize gas production and condensate recovery, the reservoir Pressure must be maintained close to the Dew Point Pressure. Several attempts have been made to predict the Dew Point Pressure in case the gas sample becomes unavailable or measured value is unreliable. Unfortunately, most of these attempts have minor success rates and are based on limited data. In this paper we present a robust, cheap, and easy model for predicting the Dew Point Pressure for gas condensate reservoirs. The new model is an intelligent based model called “Gene Expression Programming” that is carried out to generate a precise and accurate correlation to estimate the Dew Point Pressure in condensate gas reservoirs. The new model has been trained and tested using a large data bank collected for the literature. Precision of the suggested correlation has been compared to published correlations. The validity of this model has also been compared to experimental data and other published correlations.
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robust intelligent tool for estimating Dew Point Pressure in retrograded condensate gas reservoirs application of particle swarm optimization
Journal of Petroleum Science and Engineering, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad Ebadi, Arash YazdanpanahAbstract:Abstract Liquid production from gas condensate reservoirs, which is an important economic and technical issue, depends on the thermodynamic conditions underlying the porous media. Accurately estimating the relevant parameters is an incentive for researchers to develop and propose a diversity of correlations; however, certain correlations are not sufficiently precise compared with correlations that are routinely applied to determine the Dew Point Pressure ( P d ). Due to numerous misunderstandings in P d estimations, which are typically observed in upstream industries, great effort was expended herein to produce a high-performance method to monitor the P d . The solution was produced by creating a hybrid of two effective and robust methods, the swarm intelligence and artificial neural network (ANN) models. The proposed model was extended using precise Dew Point Pressure data reported in previous studies; moreover, based on these data, the evolved intelligent approach and conventional schemes were compared. The statistical results show a notable performance by the smart model in determining the Dew Point Pressure of condensate gas reservoirs. Based on the reliable results, which are highly accurate and effective, it can logically be inferred that implementing the proposed approach, PSO-ANN, can aid in better understanding reservoir fluid behavior through reservoir simulation scenarios.
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evolving smart approach for determination Dew Point Pressure through condensate gas reservoirs
Fuel, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad EbadiAbstract:To design gas condensate production planes with low uncertainty along with robust reservoir simulation, precise estimation or monitoring of Dew Point Pressure play a crucial role. To handle successfully the addressed issue of condensate gas reservoirs, massive attentions have been performed previously but unfortunately fail to develop accurate approach for estimation Dew Point Pressure. Dedicated to this fact, in current study enormous attempts have been put forth to proposed revolutionary method for determining Dew Point Pressure in gas condensate reservoirs. To gain this end the new type of support vector machine method which evolved by Suykens and VanDewalle was utilized to generate robust approach to figure Dew Point Pressure in condensate gas reservoir out. Also, lucrative and high precise Dew Point Pressures reported in previous attentions were carried out to test and validate support vector machine approach. To serve better understanding of the proposed support vector machine approach, the conventional feed-forward artificial neural network and couple of genetic algorithm (GA) and fuzzy logic applied to the referred data banks and the gained solutions were contrasted with each other. According to the root mean square error (RMSE), correlation coefficient and average absolute relative deviation, the suggested support vector machine approach has acceptable reliability, integrity and robustness draw an analogy with the artificial neural network model and conventional methods. Thus, the proposed intelligent based way can be considered as an alternative model to monitor the Dew Point Pressure of condensate gas reservoirs when the required real data are not accessible.
Milad Arabloo - One of the best experts on this subject based on the ideXlab platform.
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development of genetic programming gp models for gas condensate compressibility factor determination below Dew Point Pressure
Journal of Petroleum Science and Engineering, 2018Co-Authors: Hamid Reza Saghafi, Milad ArablooAbstract:Abstract Gas compressibility factor plays a vital role in various engineering applications related to natural gas reservoir management, planning, transportation and processing. Compared to dry gases, gas condensates are thermodynamically complex and require thorough attention. The main challenge is phase segregation and compositional change during temperature variations or Pressure depletion. Therefore, this study is focused on proposing novel compositional models based on a Genetic Programming (GP) framework for the accurate calculation of the gas condensate compressibility factor below Dew Point Pressure. The new models are developed based on 1800 gas condensate datasets obtained from open literature. Both qualitative and statistical quantitative assessments were used to compare the precision and accuracy estimation of the new models to existing literature models. Moreover, the proficiency of the proposed models for compressibility factor calculations of gas condensate samples for sweet and sour gas samples was investigated. In addition, a sensitivity analysis based on Spearman and Pearson techniques was performed to carry out the degree of influence of each input parameter on the target value. It is expected that the developed models will pave the way for the accurate calculation of compressibility factors for gas condensates, which can be used by engineers for performance monitoring, optimization and production management in gas condensate systems.
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evolving an accurate model based on machine learning approach for prediction of Dew Point Pressure in gas condensate reservoirs
Chemical Engineering Research & Design, 2014Co-Authors: Seyed Mohammad Javad Majidi, Milad Arabloo, Amin Shokrollahi, Rami Mahdikhanisoleymanloo, Mohse MasihiAbstract:Abstract Over the years, accurate prediction of Dew-Point Pressure of gas condensate has been a vital importance in reservoir evaluation. Although various scientists and researchers have proposed correlations for this purpose since 1942, but most of these models fail to provide the desired accuracy in prediction of Dew-Point Pressure. Therefore, further improvement is still needed. The objective of this study is to present an improved artificial neural network (ANN) method to predict Dew-Point Pressures in gas condensate reservoirs. The model was developed and tested using a total set of 562 experimental data Point from different gas condensate fluids covering a wide range of variables. After a series of optimization processes by monitoring the networks performance, the best network structure was selected. This study also presents a detailed comparison between the results predicted by this ANN model and those of other universal empirical correlations for estimation Dew-Point Pressure. The results showed that the developed model outperforms all the existing methods and provides predictions in acceptable agreement with experimental data. Also it is shown that the improved ANN model is capable of simulating the actual physical trend of the Dew-Point Pressure versus temperature between the cricondenbar and cricondenterm on the phase envelope. Finally, an outlier diagnosis was performed on the whole data set to detect the erroneous measurements from experimental data.
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toward a predictive model for estimating Dew Point Pressure in gas condensate systems
Fuel Processing Technology, 2013Co-Authors: Milad Arabloo, Amin Shokrollahi, Farhad Gharagheizi, Amir H MohammadiAbstract:Abstract Dew-Point Pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to present a reliable, computer-based predictive model for prediction of Dew-Point Pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data Points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of the model. The results showed that the developed model significantly outperforms all the existing methods and provide predictions in acceptable agreement with experimental data. In addition, it is shown that the proposed model is capable of simulating the actual physical trend of the Dew-Point Pressure versus temperature for a constant composition fluid on the phase envelope.
Farhad Gharagheizi - One of the best experts on this subject based on the ideXlab platform.
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toward a predictive model for estimating Dew Point Pressure in gas condensate systems
Fuel Processing Technology, 2013Co-Authors: Milad Arabloo, Amin Shokrollahi, Farhad Gharagheizi, Amir H MohammadiAbstract:Abstract Dew-Point Pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to present a reliable, computer-based predictive model for prediction of Dew-Point Pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data Points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of the model. The results showed that the developed model significantly outperforms all the existing methods and provide predictions in acceptable agreement with experimental data. In addition, it is shown that the proposed model is capable of simulating the actual physical trend of the Dew-Point Pressure versus temperature for a constant composition fluid on the phase envelope.
Amin Shokrollahi - One of the best experts on this subject based on the ideXlab platform.
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evolving an accurate model based on machine learning approach for prediction of Dew Point Pressure in gas condensate reservoirs
Chemical Engineering Research & Design, 2014Co-Authors: Seyed Mohammad Javad Majidi, Milad Arabloo, Amin Shokrollahi, Rami Mahdikhanisoleymanloo, Mohse MasihiAbstract:Abstract Over the years, accurate prediction of Dew-Point Pressure of gas condensate has been a vital importance in reservoir evaluation. Although various scientists and researchers have proposed correlations for this purpose since 1942, but most of these models fail to provide the desired accuracy in prediction of Dew-Point Pressure. Therefore, further improvement is still needed. The objective of this study is to present an improved artificial neural network (ANN) method to predict Dew-Point Pressures in gas condensate reservoirs. The model was developed and tested using a total set of 562 experimental data Point from different gas condensate fluids covering a wide range of variables. After a series of optimization processes by monitoring the networks performance, the best network structure was selected. This study also presents a detailed comparison between the results predicted by this ANN model and those of other universal empirical correlations for estimation Dew-Point Pressure. The results showed that the developed model outperforms all the existing methods and provides predictions in acceptable agreement with experimental data. Also it is shown that the improved ANN model is capable of simulating the actual physical trend of the Dew-Point Pressure versus temperature between the cricondenbar and cricondenterm on the phase envelope. Finally, an outlier diagnosis was performed on the whole data set to detect the erroneous measurements from experimental data.
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toward a predictive model for estimating Dew Point Pressure in gas condensate systems
Fuel Processing Technology, 2013Co-Authors: Milad Arabloo, Amin Shokrollahi, Farhad Gharagheizi, Amir H MohammadiAbstract:Abstract Dew-Point Pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to present a reliable, computer-based predictive model for prediction of Dew-Point Pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data Points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of the model. The results showed that the developed model significantly outperforms all the existing methods and provide predictions in acceptable agreement with experimental data. In addition, it is shown that the proposed model is capable of simulating the actual physical trend of the Dew-Point Pressure versus temperature for a constant composition fluid on the phase envelope.