Application of grid partitioning based fuzzy inference system as a novel predictor to estimate dynamic viscosity of n-alkane

Abstract

The oil recovery and rate of production are highly dependent on viscosity of reservoir fluid so this term becomes one of the attractive parameters in petroleum engineering. The viscosity of fluid is highly function of composition, temperature, and pressure so in this article, Grid partitioning based Fuzzy inference system approach is utilized as novel predictor to estimate dynamic viscosity of different normal alkanes in the wide range of operational conditions. In order to comparison of model output with actual data, an experimental dataset related to dynamic viscosity of n-alkanes is gathered. The graphical and statistical comparisons between model outputs and experimental data show the high quality performance of predicting algorithm. The coefficients of determination (R²) of training and testing phases are 0.9985 and 0.9980, respectively. The mentioned statistical indexes represent the great accuracy of model in prediction of dynamic viscosity.     

ANFIS-GA modeling of dynamic viscosity of N-Alkane in different operational conditions

Viscosity is known as one of major properties of fluids which have straight effects on different parts of chemical and petroleum industries. Due to this importance, in the present work, adaptive neuro-fuzzy interference system (ANFIS) was coupled with genetic algorithm (GA) to predict dynamic viscosity of normal alkane in terms of molecular weight of n-alkane, temperature and pressure. In order to prepare and validate the predicting model 228 experimental data points were extracted from the literature. The outputs of this predictive tool were compared with the experimental data and comparisons showed that predicted dynamic viscosities have good agreement with experimental data. According to the statistical and graphical analyses this simple tool can be used as a rigorous and accurate method for prediction of dynamic viscosity of n-alkane, especially at reservoir conditions.     

Applying MLP-ANN as a novel and accurate method to estimate gas density

In the gas industries, to increase the degree of accuracy of calculation and estimation in different processes, the importance of accurate prediction of gas properties is highlighted. The gas density, as one of the key properties in gas engineering, has a major effect in calculations. So, in the present paper, multi-layer perceptron artificial neural network (MLP-ANN) was used to predict the gas density based on molecular weight, critical pressure and critical temperature of gas, pressure, and temperature. To this end, a total number of 1240 reliable data of gas density were gathered from literature for the training and testing phases. The MLP-ANN outputs were compared with the actual data in different manners, such as statistical and graphical analyses. The coefficient of determination (R²), average absolute relative deviation (AARD), and root mean squared error (RMSE) for overall process were calculated as 1, 0.0088444, and 0.0259, respectively. The determined parameters and graphical analysis showed that the MLP-ANN has great potential and high degree of accuracy in gas density estimation.     

Application of MLP-ANN as novel tool for estimation of effect of inhibitors on asphaltene precipitation reduction

Sedimentation of heavy fractions of oil such as asphaltene is the main concern in different parts of petroleum industries like production and transportation. Due to this fact, the inhibition of asphaltene precipitation becomes one of the great interests in the petroleum industry. In the present investigation, multi-layer perceptron artificial neural network (MLP-ANN) was developed to estimate asphaltene precipitation reduction as a function of concentration and kind of inhibitors and oil properties. To this end, a total number of 75 data points were extracted from reliable source for training and validation of predicting algorithm. The outputs of MLP-ANN were compared with experimental data graphically and statistically, the determined coefficients of determination (R²) for training and testing are 0.996522 and 0.995239 respectively. These comparisons expressed the high quality of this algorithm in the prediction of asphaltene precipitation reduction. so the MLP-ANN can be used as a powerful machine for estimation of different processes in petroleum industries.     

PSO-ANFIS modeling of viscosity for mixtures of Athabasca bitumen and a high-boiling n-alkane

The bitumen and heavy oil reservoirs are more in number than light crude oil reservoirs in the world. To increase the empty space between molecules and decrease viscosity, the bitumen was diluted with a liquid solvent such as tetradecane. Due to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of mentioned mixture is important. The purpose of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using the adaptive neuro-fuzzy inference system method. For this model, the value of MRE and R² was obtained as 0.34% and 1.00, respectively; so this model can be applied as an accurate approximation for any mixture of heavy oil with a liquid solvent.     

Development of novel method for prediction of gas density in operational conditions

The performance of gas industries is extensively function of gas properties such as gas density. Due to this importance in the present work, a novel grid partitioning based fuzzy inference system method applied to predict gas density base on pressure, temperature and molecular weight of gas. To this end, the required experimental data are collected from reliable sources. Different comparison scenarios are used to evaluate the ability of model. The coefficients of determination (R²) for training and testing phases are calculated as 0.9985 and 0.9980 respectively. The determined indexes and graphical evaluations show that predicting model can estimate gas density in high degree of accuracy. According to the obtained results, the predicting model can be used as a simple and powerful software in gas industries to predict different processes.     

On prediction of asphaltene precipitation in different operational conditions utilization of LSSVM algorithm

Asphaltene which is known as one of the fractions of oil, can cause the important problems during production of crude oil in reservoir, tubing and surface facilities so these problems can influence the production cost and time. In order to predicting and solving asphaltene problems, a powerful Least squares support vector machine (LSSVM) algorithm were developed for asphaltene precipitation estimation as function of dilution ratio, temperature, precipitant carbon number, asphaltene content and API of oil. A total number of 428 measured data were utilized to train and test of LSSVM algorithm. The average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) were determined as 7.7569, 0.98552 and 0.26312 respectively. Based on these statistical parameters and graphical analysis it can be concluded that the predicting algorithm has enough reliability and accuracy in prediction of asphaltene precipitation.     

Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

Prediction of dynamic viscosity of n-alkanes at high pressures using a rigorous approach

An adaptive neuro-fuzzy interference system has been developed for estimating the dynamic viscosity of n-alkanes in a wide range of operating conditions. In this study, for the first time, a simple predictive model is proposed for viscosity prediction of n-pentane, n-octane, n-nonane, n-decane and dodecane at various pressures and temperatures, especially at high pressures, without needing to measurement or estimation of density. This tool predicts the dynamic viscosity of the n-alkanes as function of pressure, temperature and n-alkanes' molecular weight. The obtained results of the model were in an excellent agreement with experimental data with an acceptable coefficient of determination of 0.999 for both training and testing datasets. Moreover, the validity of the proposed model for viscosity trends prediction at various conditions was demonstrated and it showed a very good match with actual data. This model is simple to use and can be of massive evaluation for better understanding the behavior of fluids under reservoir conditions.     

A novel ANFIS model to prediction of the density of n-alkane in different operational condition

Abstract

The density has an important role in the oil and gas industries calculation. In this study, an adaptive neuro-fuzzy interference system (ANFIS) model was employed to predict the density of n-alkane. The result obtained by the ANFIS model analyzed with the statistical parameters (i.e., MSE, RMSE, and R²) and graphical method. According to the result obtained the ANFIS has the highest accuracy with R² = 0.999, MSE = 0.1438, and RMSE = 0.3792.     

Application of Fuzzy C-means algorithm as a novel approach to forecast gas density in different conditions

One of the dominant parameters in accurate calculation and forecasting processes gas industries is accurate estimation of gas properties. The gas density is known as an effective parameter in gas processes calculations which affected by pressure and temperature. In the present paper, the Fuzzy c-means (FCM) algorithm is utilized as a novel predictive tool to estimate gas density as function of molecular weight, critical pressure and critical temperature of gas, pressure and temperature. In the purpose of training and testing of proposed FCM algorithm, a total number of 1240 measured data were gathered from reliable sources. The outputs of model and experimental data comparisons showed the great agreement between them such that the coefficients of determination for training and testing datasets were determined as 0.9982 and 0.9903 respectively. According to the obtained results from the graphical and statistical comparisons it can be concluded that the FCM algorithm has great ability and enough accuracy in prediction of gas density.     

Applying ANFIS-PSO algorithm as a novel accurate approach for prediction of gas density

The accurate estimations of processes in gas engineering need a high degree of accuracy in calculations of gas properties. One of these properties is gas density which is straightly affected by pressure and temperature. In the present work, the Adaptive neuro fuzzy inference system (ANFIS) algorithm joined with Particle Swarm Optimization (PSO) to estimate gas density in terms of pressure, temperature, molecular weight, critical pressure and critical temperature of gas. In order to training and testing of ANFIS-PSO algorithm a total number of 1240 experimental data were extracted from the literature. The statistical parameters, Root mean square error (RMSE), coefficient of determination (R²) and average absolute relative deviation (AARD) were determined for overall process as 0.14, 1 and 0.039 respectively. The determined statistical parameters and graphical comparisons expressed that predicting mode is a robust and accurate model for prediction of gas density. Also the predicting model was compared with three correlations and obtained results showed the better performance of the proposed model respect to the others.     

Utilization of Grid partitioning based Fuzzy inference system approach as a novel method to estimate solubility of hydrocarbons in carbon dioxide

Recently due to increasing demand for energy and declination of oil reservoir the researchers have been encouraged to investigate the enhancement of oil recovery (EOR) approaches. One of popular and wide applicable processes in EOR is carbon dioxide injection which is attractive for researchers and industries due to environmentally aspects, good efficiency in displacement and low cost. The carbon dioxide injection causes the hydrocarbons extracted from crude oil so the solubility of hydrocarbon in carbon dioxide which is one of the critical parameters affects this phenomenon becomes interesting topic for researchers. In the present work Grid partitioning based Fuzzy inference system approach as a new method for prediction of solubility of hydrocarbons in carbon dioxide as function of temperature, pressure and carbon number of alkane was applied. To show the accuracy of the model the coefficients of determination were determined as 0.9902 and 0.9584 for training and testing phases respectively.     

A new dynamic model for accurate prediction of asphaltene precipitation process in petroleum reservoirs

A novel dynamic model for accurate evaluation of the effects of different process variables on the asphaltene precipitation process in petroleum reservoirs is presented. Different deposition mechanisms such as surface deposition, pore throat plugging, deposits entrainment, and asphaltene adsorption are properly incorporated in the proposed multiphase model. The variations of porosity, permeability, and pressure profiles are in good agreement with the available experimental data. The presented approach can have potential application for evaluation of the asphaltene precipitation process in petroleum reservoirs at different operating conditions.     

Application of Grid partitioning based Fuzzy inference system and ANFIS as novel approach for modeling of Athabasca bitumen and tetradecane mixture viscosity

The heavy oil and bitumen reservoirs have effective role on supplying energy due to their availability in the world. The bitumen has extremely high viscosity so this type of reservoirs has numerous problems in production and trans- portation.one of the common approach for reduction of viscosity is injection of solvents such as tetradecane. In the present study the Grid partitioning based Fuzzy inference system was coupled with ANFIS to propose a novel algorithm for prediction of bitumen and tetradecane mixture viscosity in terms of pressure, temperature and weight fraction of the tetradecane. In the present study, the coefficients of determination for training and testing phases are determined as 0.9819 and 0.9525 respectively and the models are visualized and compared with experimental data in literature. According to the results the predicting method has acceptable accuracy for prediction of bitumen and tetradecane mixture viscosity.     

Applying LSSVM algorithm as a novel and accurate method for estimation of interfacial tension of brine and hydrocarbons

One of the critical parameters in petroleum and chemical engineering is the interfacial tension between brine and hydrocarbon which has major effects on trapping and residual oil in reservoir pore throat so it becomes one of the interesting topics in enhancement of oil recovery in this work Least squares support vector machine (LSSVM) algorithm was applied as a novel predicting machine for prediction of interfacial tension of brine and hydrocarbons in terms of hydrocarbon carbon number, temperature, pressure and ionic strength of brine. A total number of 175 interfacial tensions were collected from literature in the purpose of training and testing of the model. The root mean squared error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R²) were calculated overall datasets as 0.23964, 0.27444 and 0.98509 respectively. The results of study showed that predicting LSSVM machine can be applicable for estimation of interfacial tension and EOR processes.     

Application of fuzzy c-means algorithm as a novel method to predict density of mixtures of Athabasca bitumen and heavy n-alkane

The significant number of oil reservoir are bitumen and heavy oil. One of the approaches to enhance oil recovery of these types of reservoir is dilution of reservoir oil by injection of a solvent such as tetradecane into the reservoirs to modify viscosity and density of reservoir fluids. In this investigation, an effective and robust estimating algorithm based on fuzzy c-means (FCM) algorithm was developed to predict density of mixtures of Athabasca bitumen and heavy n-alkane as function of temperature, pressure and weight percent of the solvent. The model outputs were compared to experimental data from literature in different conditions. The coefficients of determination for training and testing datasets are 0.9989 and 0.9988. The comparisons showed that the proposed model can be an applicable tool for predicting density of mixtures of bitumen and heavy n-alkane.     

Applying of LSSVM approach as a novel tool for accurate prediction of asphaltene inhibition efficiency

Asphaltene precipitation is one of critical problems for petroleum industries. There are different methods for inhibition of asphaltene precipitation. One of the common and effective methods for inhibition of asphaltene precipitation is utilizing asphaltene inhibitors. In this work, Least squares support vector machine (LSSVM) algorithm was coupled with simplex optimizer to create a novel and accurate tool for estimation of effect of inhibitors on asphaltene precipitation as function of concentration and structure of inhibitors and crude oil properties. To this end a total number of 75 measured data was extracted from the literature for training and testing of predicting model. The average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) of total data for prediction algorithm were determined as 1.1479, 0.99406 and 0.61039. According to these parameters and graphical comparisons the LSSVM algorithm has potential to predict asphaltene precipitation in high degree of accuracy.     

Application of ANFIS-GA as a novel and accurate tool for estimation of interfacial tension of carbon dioxide and hydrocarbon

In the recent years, the enhancement oil recovery processes become the one of the interesting topics in petroleum engineering because of declination of oil reservoirs. One of the most popular processes is the carbon dioxide injection that has special importance because of its environmentally friendly and high efficiency of displacement. The interfacial tension (IFT) between carbon dioxide and hydrocarbon is known as a key parameter in this process so in the present investigation the Adaptive neuro-fuzzy inference system (ANFIS) was coupled with Genetic Algorithm (GA) to create a novel tool for prediction IFT between carbon dioxide and hydrocarbon in terms of temperature, pressure, molecular weight of alkane, gas and liquid densities. The outputs of predicting model were compared with experimental IFT statistically and graphically. The comparisons showed that predicting model has acceptable accuracy in prediction of IFT of hydrocarbon and carbon dioxide.     

氮气辅助降黏技术在吉7井区的研究与应用

针对昌吉油田吉7井区油藏南部部分油井投产后出现供液能力差、产量递减快、不能持续性生产的现状,开展了氮气辅助化学剂降黏技术的研究与应用。结合吉7井区油藏属性,对降黏剂的适应性进行了评价。实验结果表明,在吉7井区储层温度、矿化度条件下,该降黏剂对稠油具有较好的降黏效果(质量分数1.5%,降黏率75%~85%)。现场应用结果表明:该工艺的应用能够解决油井供液困难、不能连续生产的问题;共实施8井次,累计增油1 011.2t;与未注氮气的油井相比,使用氮气辅助工艺能够起到较为明显的助排作用和控制油井含水上升的作用。