Please cite this article as: Ameli, F., Moghbeli, M.R., Alashkar, A., On the effect of salinity a... more Please cite this article as: Ameli, F., Moghbeli, M.R., Alashkar, A., On the effect of salinity and nano-particles on polymer flooding in a heterogeneous porous media: Experimental and modeling approaches,
Due to unique physical and chemical properties of ionic liquids (ILs), they received lots of atte... more Due to unique physical and chemical properties of ionic liquids (ILs), they received lots of attention in many industrial fields and are widely under research. Ionic liquids, also are emerging as important components for applications in electrochemical devices. To develop their applications and achieving desire properties, they are usually mixed with organic solvents. Applying ionic liquids in many applications needs the accurate and reliable data of electrical conductivity of ILs and their mixtures. To this end, a total of 224 experimental data were collected from literature and divided randomly into two datasets: 179 data was selected as training set and the remained 45 data was used as a testing set. Afterwards, a reliable modeling technique is developed for modeling the electrical conductivity of the ILs ternary mixtures. This approach is called least square support vector machine (LSSVM). The model parameters were optimized using the method of couple simulated annealing (CSA). The input model parameters were, temperature of the system, melting point, molecular weight and mole percent of each component. A comprehensive error investigation was carried out, yielding the well accordance between the predictions of the model and experimental data. The presented model can predict the dependency of electrical conductivity variations with input variables. Moreover, the sensitivity analyses demonstrated that, among the selected input parameters, the average melting point of mixture has the largest effect on the electrical conductivity. Furthermore, suspected data were detected using the Leverage approach, residual, Williams plot and statistical hat matrix. Except seven data points, the all data appear to be reliable.
Asphaltene precipitation causes rigorous problems in petroleum industry such as: relative permeab... more Asphaltene precipitation causes rigorous problems in petroleum industry such as: relative permeability reduction, wettability alteration, blockage of the flow, etc. Therefore, accurate determination of onset pressures of asphaltene precipitation is necessary. These pressures can be obtained by experimental measurements on representative samples of the crude oils; however, laboratory analysis of crude oil samples is costly, time consuming and cumbersome. In this communication, three simple and accurate expressions have been proposed for prediction of lower and upper onset pressures of asphaltene precipitation as well as saturation pressures. To this end, 33 crude oil samples were collected from open literature sources. Afterward, two constrained multivariable search methods, namely generalized reduced gradient (GRG) and successive linear programming (SLP), were employed for modeling and expediting the process of achieving a good feasible solution. Then, comparative studies were conducted between the developed equations and equations of state as well as empirical correlations. The results illustrate that the developed equations are accurate, reliable and superior to all other published models. The results show that the proposed equations can predict lower onset pressure, upper onset pressure and saturation pressure with average absolute percent relative errors of 5.04%, 3.93%, and 3.81%, respectively. Besides, it is found that molecular weight of heptane-plus fraction has the greatest impact on the lower onset pressure, while methane has the most significant effect on both of the saturation and upper onset pressures
Over the last decades, modeling asphaltene precipitation has been extensively conducted by many r... more Over the last decades, modeling asphaltene precipitation has been extensively conducted by many researchers. Among different models proposed for the prediction of asphaltene precipitation, scaling equation models have received much attention. The advantages of these models over the other models include no requirement to asphaltene properties and simple mathematical formulations. In these models, by fitting a limited set of experimental data, it is possible to predict asphaltene precipitation behavior at other conditions. Rassamdana et al. developed the first scaling equations based on an Iranian * ameli@iust.ac.ir. * aut. et al. 128 crude oil sample. Afterward, several scaling equations have been proposed for predicting the amount of asphaltene precipitations at different conditions. In this study, all of the available scaling equations are reviewed. These equations are categorized into two groups as: the models developed for asphaltene precipitation titration data; the models developed for asphaltene precipitation during natural depletion of reservoir. Moreover, experimental measurements of asphaltene precipitation data are associated with many uncertainties; therefore, the quality of asphaltene precipitation data is discussed based on statistical models such as leverage approach. In this method, both quantitative and qualitative analyses are performed to check the reliability of the models and to find the suspected data points. This study provides a new insight into asphaltene precipitation modeling through scaling equations.
In this study, the adsorption mechanism of bovine serum albumin on internal surface of membrane, ... more In this study, the adsorption mechanism of bovine serum albumin on internal surface of membrane, as well as that of intermediate blocking are studied using pore-network model. Accordingly, membrane considered as a network of pore and throats. Determining the flow rate and pressure drop of the system, the equation of convection and diffusion solved simultaneously with a potential term consisting of attachment and detachment of proteins on throats’ surface. To study the effect of coordination number on permeability reduction, population balance equation including aggregation term, coupled with pore network model to account for the effect of complete blocking, determining the average size of aggregates. Results showed that the slope of pressure drop curve increases sharply throughout the primary steps of the process. Attaining to a high velocity in throats, called ‘critical velocity’ causes the partially detachment of deposited proteins. This causes the slope to increase more smoothly. Fitting parameters of the system including release and capture coefficients, were obtained using optimization and introduced as a global term.
Journal of Natural Gas Science and Engineering, 2015
Accurate knowledge of static and dynamic characteristics of the reservoir plays a key role in pre... more Accurate knowledge of static and dynamic characteristics of the reservoir plays a key role in precise evaluation of the field performance. Pressure and temperature variations of the reservoir as well as precipitation of complex components like asphaltene during reservoir production change its characteristics including fluid flow and reservoir permeability. This effect is more significant in gas condensate fractured reservoirs; therefore, application of a dynamic mesh for accurate simulation of the reservoir is inevitable. In this paper, a novel technique has been proposed for generating a dynamic mesh in which static and dynamic parameters of a gas condensate fractured reservoir are combined with the capability of updating. Static parameters including permeability distribution and the location of active wells were combined with fluid patterns in the reservoir. This approach led to generating an element size map, and subsequently was applied for unstructured mesh generation. Afterward, this technique was applied in a multi-phase flow compositional simulator. Moreover, an auto-tune PVT package was developed for properties estimation. The priority of this package was freely choosing the number of regression parameters for properties estimation. To evaluate the performance of the developed model, the results for this model were compared with those obtained by uniform grid model. A grouping technique was also developed in the compositional simulator, to optimize the computation algorithm in terms of the CPU time. It was found that the proposed method provides more accurate results for the recovery of gas reservoir compared with the uniform grid model. Moreover, it is faster and computationally less expensive than the fine model. In addition, the proposed model would provide us with a new and different approach for studying the flow and rock physics and simulating fractured reservoirs, which could be an alternative to dual porosity-dual permeability methods.
Journal of Petroleum Science and Engineering, 2018
One of the most prevalent methods for enhanced oil recovery in heavy oil reservoirs is thermal te... more One of the most prevalent methods for enhanced oil recovery in heavy oil reservoirs is thermal techniques. FastSAGD process is an advance method of enhance oil recovery in which offset wells are drilled for cyclic steam injection and production. This leads to increasing the production efficiency. Optimization of this process was performed using various techniques including genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). Combination of recovery factor and cumulative steam to oil ratio was introduced as the objective function. This study represents a novel supplementary technique implemented in optimization algorithms to increase its speed, significantly. In this technique, effective parameters were defined using sensitivity analysis. Then they were converted to discrete variables. To discretize the selected parameters, three different functions including logarithmic, square, and linear were applied using Minitab 18. Moreover, repetition inhibitory algorithm (RIA) was implemented in optimization algorithms for the first time to prevent recalculation of duplicate states in optimization for the next generation, and to speed-up the optimization process. The results of sensitivity analysis indicated that maximum and minimum effective parameters were attributed to Fast-SAGD production well height and soak time, respectively. Results indicated that among various optimization algorithms, GA worked 6% better in comparison to other optimization techniques and linear discretization function resulted in better optimized point in a shorter time. Results indicated that optimization process using discrete variables and repetition inhibitory algorithm led to the optimized point 6.33 times faster than discrete optimization procedure without RIA. This was 16.46 times faster in comparison to continuous optimization algorithm. Moreover using RIA led to termination of optimization algorithm 9.67 times faster than continuous mode.
Nitrogen is of paramount importance for many processes in chemical and petroleum engineering; inc... more Nitrogen is of paramount importance for many processes in chemical and petroleum engineering; including enhanced oil recovery, gas injection for pressure maintenance, and gas recycling. Precise estimation of interfacial tension (IFT) between N2 and the reservoir hydrocarbons is, therefore indispensable. However, experimental measurement of IFT is expensive and time consuming. Therefore, reliable model for estimating IFT is vital. In this communication, the IFT between N2 and n-alkanes was modeled over a wide range of pressure (0.1–69 MPa) and temperature (295–442 K) based on the principle of corresponding state theory using dimensionless pressure and dimensionless temperature. Three well-known models; namely, Multilayer Perceptron (MLP) Neural Networks (optimized by Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), or Bayesian Regularization (BR)), two Radial Basis Function (RBF) Neural Networks (optimized by Particle Swarm optimization (PSO) technique or Genetic Algorithm (GA)) and one Least Square Support Vector Machine (LSSVM) (optimized by coupled simulated annealing) were used to develop robust and accurate models for predicting IFT based on the proposed dimensionless parameters. Results suggested that the developed MLP-LM was the most accurate model of all with an average absolute relative error of 1.38%. MLP-LM model was compared with three well-known models in the literature; namely Density Gradient Theory (DGT), Linear Gradient Theory (LGT), and Parachor approaches combined with the Volume Translated Predictive Peng Robinson Equation of State (VT-PPR EOS) and the recently developed model by Hemmati-Sarapardeh and Mohagheghian. In addition to the advantage of being normal alkane-independent, results showed that the proposed MLP-LM model is superior to published models. Lastly, the quality of the literature IFT data and the applicability domain of MLP-LM model were evaluated using the Leverage approach.
Particle size distribution (PSD) is an important factor that determines how asphaltene instabilit... more Particle size distribution (PSD) is an important factor that determines how asphaltene instability can damage porous media during natural depletion and enhanced oil recovery processes. In this work, aggregate size distribution under natural depletion and miscible nitrogen injection processes are determined via image analysis techniques and the results are modelled by population balance equation. Unimodal distribution curves in natural depletion show the dominance of particle-particle aggregation mechanism and the clustering is detected only around crude oil bubble point pressure. It is also observed that miscible nitrogen injection considerably increases the number and size of asphaltene flocs and directs the agglomeration process towards cluster-cluster aggregation (i.e. bimodal distribution curves) which can severely damage porous media. Results of population balance modeling characterize dominant aggregation mechanisms providing one optimum collision factor for unimodal curves and two optimum collision factors for bimodal distributions which confirms the alteration of aggregation mechanism due to miscible gas injection.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y, 2018
In this work, four prompt and robust techniques have been used to introduce new
generalized model... more In this work, four prompt and robust techniques have been used to introduce new generalized models for estimation of the physical properties of pure substances, including molecular weight and acentric factor. These methods were developed based on radial basis function (RBF) neural networks, group method of data handling (GMDH), multilayer perceptron (MLP), and least square support vector machine (LSSVM) techniques. Models were introduced based on a set of experimental data including 563 pure compounds that were collected from available literature. Input parameters for estimation of molecular weight were considered as specific gravity and normal boiling point. Critical temperature, critical pressure and normal boiling point were selected as inputs for estimation of the acentric factor. Statistical and graphical error analyses normal boiling point revealed that all of the developed models are accurate. The designed RBF models give the most accurate results with an AAPRE of 5.98% and 1.92% for molecular weight and acentric factor, respectively. The developed GMDH models are in the form of simple correlations, which can be used easily in hand calculation problems without any need to computers. Comparison of the developed models with the available methods showed that all of the developed models are more accurate than the existing methods. Using the relevancy factor, the impact of each input parameter on the output results was determined. Additionally, to find out the applicability region of the developed models, and to demonstrate the reliability of the models, the Leverage method has been used. There are few data out of the applicability domain of the proposed models. All the statistical and graphical resolutions, demonstrate the reliability of the developed models in estimating the molecular weight and acentric factor
Please cite this article as: Ameli, F., Moghbeli, M.R., Alashkar, A., On the effect of salinity a... more Please cite this article as: Ameli, F., Moghbeli, M.R., Alashkar, A., On the effect of salinity and nano-particles on polymer flooding in a heterogeneous porous media: Experimental and modeling approaches,
Due to unique physical and chemical properties of ionic liquids (ILs), they received lots of atte... more Due to unique physical and chemical properties of ionic liquids (ILs), they received lots of attention in many industrial fields and are widely under research. Ionic liquids, also are emerging as important components for applications in electrochemical devices. To develop their applications and achieving desire properties, they are usually mixed with organic solvents. Applying ionic liquids in many applications needs the accurate and reliable data of electrical conductivity of ILs and their mixtures. To this end, a total of 224 experimental data were collected from literature and divided randomly into two datasets: 179 data was selected as training set and the remained 45 data was used as a testing set. Afterwards, a reliable modeling technique is developed for modeling the electrical conductivity of the ILs ternary mixtures. This approach is called least square support vector machine (LSSVM). The model parameters were optimized using the method of couple simulated annealing (CSA). The input model parameters were, temperature of the system, melting point, molecular weight and mole percent of each component. A comprehensive error investigation was carried out, yielding the well accordance between the predictions of the model and experimental data. The presented model can predict the dependency of electrical conductivity variations with input variables. Moreover, the sensitivity analyses demonstrated that, among the selected input parameters, the average melting point of mixture has the largest effect on the electrical conductivity. Furthermore, suspected data were detected using the Leverage approach, residual, Williams plot and statistical hat matrix. Except seven data points, the all data appear to be reliable.
Asphaltene precipitation causes rigorous problems in petroleum industry such as: relative permeab... more Asphaltene precipitation causes rigorous problems in petroleum industry such as: relative permeability reduction, wettability alteration, blockage of the flow, etc. Therefore, accurate determination of onset pressures of asphaltene precipitation is necessary. These pressures can be obtained by experimental measurements on representative samples of the crude oils; however, laboratory analysis of crude oil samples is costly, time consuming and cumbersome. In this communication, three simple and accurate expressions have been proposed for prediction of lower and upper onset pressures of asphaltene precipitation as well as saturation pressures. To this end, 33 crude oil samples were collected from open literature sources. Afterward, two constrained multivariable search methods, namely generalized reduced gradient (GRG) and successive linear programming (SLP), were employed for modeling and expediting the process of achieving a good feasible solution. Then, comparative studies were conducted between the developed equations and equations of state as well as empirical correlations. The results illustrate that the developed equations are accurate, reliable and superior to all other published models. The results show that the proposed equations can predict lower onset pressure, upper onset pressure and saturation pressure with average absolute percent relative errors of 5.04%, 3.93%, and 3.81%, respectively. Besides, it is found that molecular weight of heptane-plus fraction has the greatest impact on the lower onset pressure, while methane has the most significant effect on both of the saturation and upper onset pressures
Over the last decades, modeling asphaltene precipitation has been extensively conducted by many r... more Over the last decades, modeling asphaltene precipitation has been extensively conducted by many researchers. Among different models proposed for the prediction of asphaltene precipitation, scaling equation models have received much attention. The advantages of these models over the other models include no requirement to asphaltene properties and simple mathematical formulations. In these models, by fitting a limited set of experimental data, it is possible to predict asphaltene precipitation behavior at other conditions. Rassamdana et al. developed the first scaling equations based on an Iranian * ameli@iust.ac.ir. * aut. et al. 128 crude oil sample. Afterward, several scaling equations have been proposed for predicting the amount of asphaltene precipitations at different conditions. In this study, all of the available scaling equations are reviewed. These equations are categorized into two groups as: the models developed for asphaltene precipitation titration data; the models developed for asphaltene precipitation during natural depletion of reservoir. Moreover, experimental measurements of asphaltene precipitation data are associated with many uncertainties; therefore, the quality of asphaltene precipitation data is discussed based on statistical models such as leverage approach. In this method, both quantitative and qualitative analyses are performed to check the reliability of the models and to find the suspected data points. This study provides a new insight into asphaltene precipitation modeling through scaling equations.
In this study, the adsorption mechanism of bovine serum albumin on internal surface of membrane, ... more In this study, the adsorption mechanism of bovine serum albumin on internal surface of membrane, as well as that of intermediate blocking are studied using pore-network model. Accordingly, membrane considered as a network of pore and throats. Determining the flow rate and pressure drop of the system, the equation of convection and diffusion solved simultaneously with a potential term consisting of attachment and detachment of proteins on throats’ surface. To study the effect of coordination number on permeability reduction, population balance equation including aggregation term, coupled with pore network model to account for the effect of complete blocking, determining the average size of aggregates. Results showed that the slope of pressure drop curve increases sharply throughout the primary steps of the process. Attaining to a high velocity in throats, called ‘critical velocity’ causes the partially detachment of deposited proteins. This causes the slope to increase more smoothly. Fitting parameters of the system including release and capture coefficients, were obtained using optimization and introduced as a global term.
Journal of Natural Gas Science and Engineering, 2015
Accurate knowledge of static and dynamic characteristics of the reservoir plays a key role in pre... more Accurate knowledge of static and dynamic characteristics of the reservoir plays a key role in precise evaluation of the field performance. Pressure and temperature variations of the reservoir as well as precipitation of complex components like asphaltene during reservoir production change its characteristics including fluid flow and reservoir permeability. This effect is more significant in gas condensate fractured reservoirs; therefore, application of a dynamic mesh for accurate simulation of the reservoir is inevitable. In this paper, a novel technique has been proposed for generating a dynamic mesh in which static and dynamic parameters of a gas condensate fractured reservoir are combined with the capability of updating. Static parameters including permeability distribution and the location of active wells were combined with fluid patterns in the reservoir. This approach led to generating an element size map, and subsequently was applied for unstructured mesh generation. Afterward, this technique was applied in a multi-phase flow compositional simulator. Moreover, an auto-tune PVT package was developed for properties estimation. The priority of this package was freely choosing the number of regression parameters for properties estimation. To evaluate the performance of the developed model, the results for this model were compared with those obtained by uniform grid model. A grouping technique was also developed in the compositional simulator, to optimize the computation algorithm in terms of the CPU time. It was found that the proposed method provides more accurate results for the recovery of gas reservoir compared with the uniform grid model. Moreover, it is faster and computationally less expensive than the fine model. In addition, the proposed model would provide us with a new and different approach for studying the flow and rock physics and simulating fractured reservoirs, which could be an alternative to dual porosity-dual permeability methods.
Journal of Petroleum Science and Engineering, 2018
One of the most prevalent methods for enhanced oil recovery in heavy oil reservoirs is thermal te... more One of the most prevalent methods for enhanced oil recovery in heavy oil reservoirs is thermal techniques. FastSAGD process is an advance method of enhance oil recovery in which offset wells are drilled for cyclic steam injection and production. This leads to increasing the production efficiency. Optimization of this process was performed using various techniques including genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). Combination of recovery factor and cumulative steam to oil ratio was introduced as the objective function. This study represents a novel supplementary technique implemented in optimization algorithms to increase its speed, significantly. In this technique, effective parameters were defined using sensitivity analysis. Then they were converted to discrete variables. To discretize the selected parameters, three different functions including logarithmic, square, and linear were applied using Minitab 18. Moreover, repetition inhibitory algorithm (RIA) was implemented in optimization algorithms for the first time to prevent recalculation of duplicate states in optimization for the next generation, and to speed-up the optimization process. The results of sensitivity analysis indicated that maximum and minimum effective parameters were attributed to Fast-SAGD production well height and soak time, respectively. Results indicated that among various optimization algorithms, GA worked 6% better in comparison to other optimization techniques and linear discretization function resulted in better optimized point in a shorter time. Results indicated that optimization process using discrete variables and repetition inhibitory algorithm led to the optimized point 6.33 times faster than discrete optimization procedure without RIA. This was 16.46 times faster in comparison to continuous optimization algorithm. Moreover using RIA led to termination of optimization algorithm 9.67 times faster than continuous mode.
Nitrogen is of paramount importance for many processes in chemical and petroleum engineering; inc... more Nitrogen is of paramount importance for many processes in chemical and petroleum engineering; including enhanced oil recovery, gas injection for pressure maintenance, and gas recycling. Precise estimation of interfacial tension (IFT) between N2 and the reservoir hydrocarbons is, therefore indispensable. However, experimental measurement of IFT is expensive and time consuming. Therefore, reliable model for estimating IFT is vital. In this communication, the IFT between N2 and n-alkanes was modeled over a wide range of pressure (0.1–69 MPa) and temperature (295–442 K) based on the principle of corresponding state theory using dimensionless pressure and dimensionless temperature. Three well-known models; namely, Multilayer Perceptron (MLP) Neural Networks (optimized by Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), or Bayesian Regularization (BR)), two Radial Basis Function (RBF) Neural Networks (optimized by Particle Swarm optimization (PSO) technique or Genetic Algorithm (GA)) and one Least Square Support Vector Machine (LSSVM) (optimized by coupled simulated annealing) were used to develop robust and accurate models for predicting IFT based on the proposed dimensionless parameters. Results suggested that the developed MLP-LM was the most accurate model of all with an average absolute relative error of 1.38%. MLP-LM model was compared with three well-known models in the literature; namely Density Gradient Theory (DGT), Linear Gradient Theory (LGT), and Parachor approaches combined with the Volume Translated Predictive Peng Robinson Equation of State (VT-PPR EOS) and the recently developed model by Hemmati-Sarapardeh and Mohagheghian. In addition to the advantage of being normal alkane-independent, results showed that the proposed MLP-LM model is superior to published models. Lastly, the quality of the literature IFT data and the applicability domain of MLP-LM model were evaluated using the Leverage approach.
Particle size distribution (PSD) is an important factor that determines how asphaltene instabilit... more Particle size distribution (PSD) is an important factor that determines how asphaltene instability can damage porous media during natural depletion and enhanced oil recovery processes. In this work, aggregate size distribution under natural depletion and miscible nitrogen injection processes are determined via image analysis techniques and the results are modelled by population balance equation. Unimodal distribution curves in natural depletion show the dominance of particle-particle aggregation mechanism and the clustering is detected only around crude oil bubble point pressure. It is also observed that miscible nitrogen injection considerably increases the number and size of asphaltene flocs and directs the agglomeration process towards cluster-cluster aggregation (i.e. bimodal distribution curves) which can severely damage porous media. Results of population balance modeling characterize dominant aggregation mechanisms providing one optimum collision factor for unimodal curves and two optimum collision factors for bimodal distributions which confirms the alteration of aggregation mechanism due to miscible gas injection.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y, 2018
In this work, four prompt and robust techniques have been used to introduce new
generalized model... more In this work, four prompt and robust techniques have been used to introduce new generalized models for estimation of the physical properties of pure substances, including molecular weight and acentric factor. These methods were developed based on radial basis function (RBF) neural networks, group method of data handling (GMDH), multilayer perceptron (MLP), and least square support vector machine (LSSVM) techniques. Models were introduced based on a set of experimental data including 563 pure compounds that were collected from available literature. Input parameters for estimation of molecular weight were considered as specific gravity and normal boiling point. Critical temperature, critical pressure and normal boiling point were selected as inputs for estimation of the acentric factor. Statistical and graphical error analyses normal boiling point revealed that all of the developed models are accurate. The designed RBF models give the most accurate results with an AAPRE of 5.98% and 1.92% for molecular weight and acentric factor, respectively. The developed GMDH models are in the form of simple correlations, which can be used easily in hand calculation problems without any need to computers. Comparison of the developed models with the available methods showed that all of the developed models are more accurate than the existing methods. Using the relevancy factor, the impact of each input parameter on the output results was determined. Additionally, to find out the applicability region of the developed models, and to demonstrate the reliability of the models, the Leverage method has been used. There are few data out of the applicability domain of the proposed models. All the statistical and graphical resolutions, demonstrate the reliability of the developed models in estimating the molecular weight and acentric factor
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reduction, wettability alteration, blockage of the flow, etc. Therefore, accurate determination of onset
pressures of asphaltene precipitation is necessary. These pressures can be obtained by experimental
measurements on representative samples of the crude oils; however, laboratory analysis of crude oil
samples is costly, time consuming and cumbersome. In this communication, three simple and accurate
expressions have been proposed for prediction of lower and upper onset pressures of asphaltene precipitation as well as saturation pressures. To this end, 33 crude oil samples were collected from open
literature sources. Afterward, two constrained multivariable search methods, namely generalized
reduced gradient (GRG) and successive linear programming (SLP), were employed for modeling and
expediting the process of achieving a good feasible solution. Then, comparative studies were conducted
between the developed equations and equations of state as well as empirical correlations. The results
illustrate that the developed equations are accurate, reliable and superior to all other published models.
The results show that the proposed equations can predict lower onset pressure, upper onset pressure and
saturation pressure with average absolute percent relative errors of 5.04%, 3.93%, and 3.81%, respectively.
Besides, it is found that molecular weight of heptane-plus fraction has the greatest impact on the lower
onset pressure, while methane has the most significant effect on both of the saturation and upper onset
pressures
intermediate blocking are studied using pore-network model. Accordingly, membrane considered as a network of pore and
throats. Determining the flow rate and pressure drop of the system, the equation of convection and diffusion solved
simultaneously with a potential term consisting of attachment and detachment of proteins on throats’ surface. To study the effect
of coordination number on permeability reduction, population balance equation including aggregation term, coupled with pore
network model to account for the effect of complete blocking, determining the average size of aggregates. Results showed that
the slope of pressure drop curve increases sharply throughout the primary steps of the process. Attaining to a high velocity in
throats, called ‘critical velocity’ causes the partially detachment of deposited proteins. This causes the slope to increase more
smoothly. Fitting parameters of the system including release and capture coefficients, were obtained using optimization and
introduced as a global term.
evaluation of the field performance. Pressure and temperature variations of the reservoir as well as
precipitation of complex components like asphaltene during reservoir production change its characteristics including fluid flow and reservoir permeability. This effect is more significant in gas condensate
fractured reservoirs; therefore, application of a dynamic mesh for accurate simulation of the reservoir is
inevitable. In this paper, a novel technique has been proposed for generating a dynamic mesh in which
static and dynamic parameters of a gas condensate fractured reservoir are combined with the capability
of updating. Static parameters including permeability distribution and the location of active wells were
combined with fluid patterns in the reservoir. This approach led to generating an element size map, and
subsequently was applied for unstructured mesh generation. Afterward, this technique was applied in a
multi-phase flow compositional simulator. Moreover, an auto-tune PVT package was developed for
properties estimation. The priority of this package was freely choosing the number of regression parameters for properties estimation. To evaluate the performance of the developed model, the results for
this model were compared with those obtained by uniform grid model. A grouping technique was also
developed in the compositional simulator, to optimize the computation algorithm in terms of the CPU
time. It was found that the proposed method provides more accurate results for the recovery of gas
reservoir compared with the uniform grid model. Moreover, it is faster and computationally less
expensive than the fine model. In addition, the proposed model would provide us with a new and
different approach for studying the flow and rock physics and simulating fractured reservoirs, which
could be an alternative to dual porosity-dual permeability methods.
injection and production. This leads to increasing the production efficiency. Optimization of this process was
performed using various techniques including genetic algorithm (GA), imperialist competitive algorithm (ICA),
and particle swarm optimization (PSO). Combination of recovery factor and cumulative steam to oil ratio was
introduced as the objective function. This study represents a novel supplementary technique implemented in
optimization algorithms to increase its speed, significantly. In this technique, effective parameters were defined
using sensitivity analysis. Then they were converted to discrete variables. To discretize the selected parameters,
three different functions including logarithmic, square, and linear were applied using Minitab 18. Moreover,
repetition inhibitory algorithm (RIA) was implemented in optimization algorithms for the first time to prevent
recalculation of duplicate states in optimization for the next generation, and to speed-up the optimization
process. The results of sensitivity analysis indicated that maximum and minimum effective parameters were
attributed to Fast-SAGD production well height and soak time, respectively. Results indicated that among various optimization algorithms, GA worked 6% better in comparison to other optimization techniques and linear
discretization function resulted in better optimized point in a shorter time. Results indicated that optimization
process using discrete variables and repetition inhibitory algorithm led to the optimized point 6.33 times faster
than discrete optimization procedure without RIA. This was 16.46 times faster in comparison to continuous
optimization algorithm. Moreover using RIA led to termination of optimization algorithm 9.67 times faster than
continuous mode.
enhanced oil recovery, gas injection for pressure maintenance, and gas recycling. Precise estimation of interfacial tension (IFT) between N2 and the reservoir hydrocarbons is, therefore indispensable. However, experimental measurement of IFT is expensive and time consuming. Therefore, reliable model for estimating IFT is
vital. In this communication, the IFT between N2 and n-alkanes was modeled over a wide range of pressure
(0.1–69 MPa) and temperature (295–442 K) based on the principle of corresponding state theory using dimensionless pressure and dimensionless temperature. Three well-known models; namely, Multilayer Perceptron
(MLP) Neural Networks (optimized by Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), or Bayesian
Regularization (BR)), two Radial Basis Function (RBF) Neural Networks (optimized by Particle Swarm optimization
(PSO) technique or Genetic Algorithm (GA)) and one Least Square Support Vector Machine (LSSVM) (optimized by
coupled simulated annealing) were used to develop robust and accurate models for predicting IFT based on the
proposed dimensionless parameters. Results suggested that the developed MLP-LM was the most accurate model
of all with an average absolute relative error of 1.38%. MLP-LM model was compared with three well-known
models in the literature; namely Density Gradient Theory (DGT), Linear Gradient Theory (LGT), and Parachor
approaches combined with the Volume Translated Predictive Peng Robinson Equation of State (VT-PPR EOS)
and the recently developed model by Hemmati-Sarapardeh and Mohagheghian. In addition to the advantage of
being normal alkane-independent, results showed that the proposed MLP-LM model is superior to published
models. Lastly, the quality of the literature IFT data and the applicability domain of MLP-LM model were
evaluated using the Leverage approach.
techniques and the results are modelled by population balance equation. Unimodal distribution curves in natural
depletion show the dominance of particle-particle aggregation mechanism and the clustering is detected only
around crude oil bubble point pressure. It is also observed that miscible nitrogen injection considerably increases
the number and size of asphaltene flocs and directs the agglomeration process towards cluster-cluster aggregation (i.e. bimodal distribution curves) which can severely damage porous media. Results of population balance
modeling characterize dominant aggregation mechanisms providing one optimum collision factor for unimodal
curves and two optimum collision factors for bimodal distributions which confirms the alteration of aggregation
mechanism due to miscible gas injection.
generalized models for estimation of the physical properties of pure substances, including
molecular weight and acentric factor. These methods were developed based on radial basis
function (RBF) neural networks, group method of data handling (GMDH), multilayer perceptron (MLP), and least square support vector machine (LSSVM) techniques. Models were
introduced based on a set of experimental data including 563 pure compounds that were
collected from available literature. Input parameters for estimation of molecular weight
were considered as specific gravity and normal boiling point. Critical temperature, critical
pressure and normal boiling point were selected as inputs for estimation of the acentric
factor. Statistical and graphical error analyses normal boiling point revealed that all of the
developed models are accurate. The designed RBF models give the most accurate results
with an AAPRE of 5.98% and 1.92% for molecular weight and acentric factor, respectively.
The developed GMDH models are in the form of simple correlations, which can be used
easily in hand calculation problems without any need to computers. Comparison of the
developed models with the available methods showed that all of the developed models are
more accurate than the existing methods. Using the relevancy factor, the impact of each
input parameter on the output results was determined. Additionally, to find out the
applicability region of the developed models, and to demonstrate the reliability of the
models, the Leverage method has been used. There are few data out of the applicability
domain of the proposed models. All the statistical and graphical resolutions, demonstrate the reliability of the developed models in estimating the molecular weight and acentric
factor
reduction, wettability alteration, blockage of the flow, etc. Therefore, accurate determination of onset
pressures of asphaltene precipitation is necessary. These pressures can be obtained by experimental
measurements on representative samples of the crude oils; however, laboratory analysis of crude oil
samples is costly, time consuming and cumbersome. In this communication, three simple and accurate
expressions have been proposed for prediction of lower and upper onset pressures of asphaltene precipitation as well as saturation pressures. To this end, 33 crude oil samples were collected from open
literature sources. Afterward, two constrained multivariable search methods, namely generalized
reduced gradient (GRG) and successive linear programming (SLP), were employed for modeling and
expediting the process of achieving a good feasible solution. Then, comparative studies were conducted
between the developed equations and equations of state as well as empirical correlations. The results
illustrate that the developed equations are accurate, reliable and superior to all other published models.
The results show that the proposed equations can predict lower onset pressure, upper onset pressure and
saturation pressure with average absolute percent relative errors of 5.04%, 3.93%, and 3.81%, respectively.
Besides, it is found that molecular weight of heptane-plus fraction has the greatest impact on the lower
onset pressure, while methane has the most significant effect on both of the saturation and upper onset
pressures
intermediate blocking are studied using pore-network model. Accordingly, membrane considered as a network of pore and
throats. Determining the flow rate and pressure drop of the system, the equation of convection and diffusion solved
simultaneously with a potential term consisting of attachment and detachment of proteins on throats’ surface. To study the effect
of coordination number on permeability reduction, population balance equation including aggregation term, coupled with pore
network model to account for the effect of complete blocking, determining the average size of aggregates. Results showed that
the slope of pressure drop curve increases sharply throughout the primary steps of the process. Attaining to a high velocity in
throats, called ‘critical velocity’ causes the partially detachment of deposited proteins. This causes the slope to increase more
smoothly. Fitting parameters of the system including release and capture coefficients, were obtained using optimization and
introduced as a global term.
evaluation of the field performance. Pressure and temperature variations of the reservoir as well as
precipitation of complex components like asphaltene during reservoir production change its characteristics including fluid flow and reservoir permeability. This effect is more significant in gas condensate
fractured reservoirs; therefore, application of a dynamic mesh for accurate simulation of the reservoir is
inevitable. In this paper, a novel technique has been proposed for generating a dynamic mesh in which
static and dynamic parameters of a gas condensate fractured reservoir are combined with the capability
of updating. Static parameters including permeability distribution and the location of active wells were
combined with fluid patterns in the reservoir. This approach led to generating an element size map, and
subsequently was applied for unstructured mesh generation. Afterward, this technique was applied in a
multi-phase flow compositional simulator. Moreover, an auto-tune PVT package was developed for
properties estimation. The priority of this package was freely choosing the number of regression parameters for properties estimation. To evaluate the performance of the developed model, the results for
this model were compared with those obtained by uniform grid model. A grouping technique was also
developed in the compositional simulator, to optimize the computation algorithm in terms of the CPU
time. It was found that the proposed method provides more accurate results for the recovery of gas
reservoir compared with the uniform grid model. Moreover, it is faster and computationally less
expensive than the fine model. In addition, the proposed model would provide us with a new and
different approach for studying the flow and rock physics and simulating fractured reservoirs, which
could be an alternative to dual porosity-dual permeability methods.
injection and production. This leads to increasing the production efficiency. Optimization of this process was
performed using various techniques including genetic algorithm (GA), imperialist competitive algorithm (ICA),
and particle swarm optimization (PSO). Combination of recovery factor and cumulative steam to oil ratio was
introduced as the objective function. This study represents a novel supplementary technique implemented in
optimization algorithms to increase its speed, significantly. In this technique, effective parameters were defined
using sensitivity analysis. Then they were converted to discrete variables. To discretize the selected parameters,
three different functions including logarithmic, square, and linear were applied using Minitab 18. Moreover,
repetition inhibitory algorithm (RIA) was implemented in optimization algorithms for the first time to prevent
recalculation of duplicate states in optimization for the next generation, and to speed-up the optimization
process. The results of sensitivity analysis indicated that maximum and minimum effective parameters were
attributed to Fast-SAGD production well height and soak time, respectively. Results indicated that among various optimization algorithms, GA worked 6% better in comparison to other optimization techniques and linear
discretization function resulted in better optimized point in a shorter time. Results indicated that optimization
process using discrete variables and repetition inhibitory algorithm led to the optimized point 6.33 times faster
than discrete optimization procedure without RIA. This was 16.46 times faster in comparison to continuous
optimization algorithm. Moreover using RIA led to termination of optimization algorithm 9.67 times faster than
continuous mode.
enhanced oil recovery, gas injection for pressure maintenance, and gas recycling. Precise estimation of interfacial tension (IFT) between N2 and the reservoir hydrocarbons is, therefore indispensable. However, experimental measurement of IFT is expensive and time consuming. Therefore, reliable model for estimating IFT is
vital. In this communication, the IFT between N2 and n-alkanes was modeled over a wide range of pressure
(0.1–69 MPa) and temperature (295–442 K) based on the principle of corresponding state theory using dimensionless pressure and dimensionless temperature. Three well-known models; namely, Multilayer Perceptron
(MLP) Neural Networks (optimized by Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), or Bayesian
Regularization (BR)), two Radial Basis Function (RBF) Neural Networks (optimized by Particle Swarm optimization
(PSO) technique or Genetic Algorithm (GA)) and one Least Square Support Vector Machine (LSSVM) (optimized by
coupled simulated annealing) were used to develop robust and accurate models for predicting IFT based on the
proposed dimensionless parameters. Results suggested that the developed MLP-LM was the most accurate model
of all with an average absolute relative error of 1.38%. MLP-LM model was compared with three well-known
models in the literature; namely Density Gradient Theory (DGT), Linear Gradient Theory (LGT), and Parachor
approaches combined with the Volume Translated Predictive Peng Robinson Equation of State (VT-PPR EOS)
and the recently developed model by Hemmati-Sarapardeh and Mohagheghian. In addition to the advantage of
being normal alkane-independent, results showed that the proposed MLP-LM model is superior to published
models. Lastly, the quality of the literature IFT data and the applicability domain of MLP-LM model were
evaluated using the Leverage approach.
techniques and the results are modelled by population balance equation. Unimodal distribution curves in natural
depletion show the dominance of particle-particle aggregation mechanism and the clustering is detected only
around crude oil bubble point pressure. It is also observed that miscible nitrogen injection considerably increases
the number and size of asphaltene flocs and directs the agglomeration process towards cluster-cluster aggregation (i.e. bimodal distribution curves) which can severely damage porous media. Results of population balance
modeling characterize dominant aggregation mechanisms providing one optimum collision factor for unimodal
curves and two optimum collision factors for bimodal distributions which confirms the alteration of aggregation
mechanism due to miscible gas injection.
generalized models for estimation of the physical properties of pure substances, including
molecular weight and acentric factor. These methods were developed based on radial basis
function (RBF) neural networks, group method of data handling (GMDH), multilayer perceptron (MLP), and least square support vector machine (LSSVM) techniques. Models were
introduced based on a set of experimental data including 563 pure compounds that were
collected from available literature. Input parameters for estimation of molecular weight
were considered as specific gravity and normal boiling point. Critical temperature, critical
pressure and normal boiling point were selected as inputs for estimation of the acentric
factor. Statistical and graphical error analyses normal boiling point revealed that all of the
developed models are accurate. The designed RBF models give the most accurate results
with an AAPRE of 5.98% and 1.92% for molecular weight and acentric factor, respectively.
The developed GMDH models are in the form of simple correlations, which can be used
easily in hand calculation problems without any need to computers. Comparison of the
developed models with the available methods showed that all of the developed models are
more accurate than the existing methods. Using the relevancy factor, the impact of each
input parameter on the output results was determined. Additionally, to find out the
applicability region of the developed models, and to demonstrate the reliability of the
models, the Leverage method has been used. There are few data out of the applicability
domain of the proposed models. All the statistical and graphical resolutions, demonstrate the reliability of the developed models in estimating the molecular weight and acentric
factor