Applying Fuzzy c-means approach as a novel method for prediction of interfacial tension between carbon dioxide and hydrocarbons

The worldwide demand for energy increases and also the price of crude oil increases so these reasons have been caused the searchers have motivated to investigate enhanced of oil recovery (EOR) processes. The carbon dioxide injection is recognized as one of the favorable approaches of EOR because of high displacement efficiency, environmentally aspects and lower cost. The Interfacial tension between crude oil and carbon dioxide is known as one of the critical factors which affect the performance of injection. The main objective of the present investigation is development of Fuzzy c-means (FCM) approach as novel method to estimate interfacial tension between carbon dioxide and hydrocarbons as function of pressure, temperature, liquid and gas densities and molecular weight of alkane. The performance of predicting model was evaluated statistically and graphically and the results confirmed the ability of the model to predict interfacial tension between carbon dioxide and hydrocarbons.     

Applying Grid partitioning based Fuzzy inference system method to estimate interfacial tension of brine and hydrocarbon

The oil recovery reservoirs and oil trapping in the reservoirs are extensively function of interfacial tension between brine and hydrocarbon, so estimation of interfacial tension becomes one of the interesting topics in petroleum industry. In this study, Grid partitioning based Fuzzy inference system method is utilized to forecast interfacial tension of hydrocarbon and brine based on various effective parameters such as ionic strength of brine, carbon number of hydrocarbon, pressure, and temperature. The estimated values of interfacial tension were compared with real interfacial tension of brine and hydrocarbon using graphical and statistical analyses. The determined coefficients of determination (R²) for training and testing phases were 0.9916 and 0.9447, respectively. The comparing analyses express that the Grid partitioning based Fuzzy inference system method has great ability in prediction of interfacial tension, and it can be used as an applicable tool in petroleum industry.     

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.     

Utilization of Fuzzy C-means algorithm as a novel predictive tool for estimation of interfacial tension of hydrocarbon and brine

The interfacial tension that exists between brine and hydrocarbon is known as one of major properties in petroleum industries because it extremely affects oil trapping in reservoirs and consequently oil recovery. Due to aforementioned reasons the importance of investigation of this parameter has been highlighted. In the present study, Fuzzy C-means (FCM) algorithm was developed to predict interfacial tension between hydrocarbon and brine as function of different parameters such as pressure, temperature, carbon number of hydrocarbon and ionic strength of brine. The obtained results of predicting algorithm expressed its low relative error and deviation from the experimental data which gathered from the literature. Also the coefficients of determination (R²) for training and testing data were calculated 0.9508 and 0.9309 respectively. This predictive tool is simple and user friend to utilize and can be helpful for petroleum engineers to estimate interfacial tension between hydrocarbons and brine.     

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.     

The ANFIS-PSO strategy as a novel method to predict interfacial tension of hydrocarbons and brine

The interfacial tension of hydrocarbons and brine is known as one of the important parameters which are measured in petroleum and petrochemical industries for example the interfacial tension has straight effect on trapping of oil in a reservoir. In the present work the Adaptive neuro-fuzzy inference system (ANFIS) algorithm was used as a novel approach for estimation of interfacial tension between hydrocarbons and brine as function of pressure, temperature, carbon number of hydrocarbon and ionic strength of brine then the particle swarm optimization (PSO) was used to optimize the predicting model parameters.in order to better evaluation of performance of predicting algorithm the coefficient of determination (R²), average absolute relative deviation (AARD) and root mean squared error (RMSE) were estimated for different steps. The outcomes of this investigation expressed that proposed model has high potential for prediction of interfacial tension between hydrocarbons and brine.     

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.     

Prediction of the interfacial tension between hydrocarbons and carbon dioxide

In the recent years due to increasing demand for energy and declination of reservoir production, an impressive notice on enhancement of oil recovery has been found. The gas injection especially carbon dioxide injection due to low cost and friendly environmentally of this approach the special attention to CO₂ injection increased. The miscibility is known as key factor which effects on enhancement of recovery. The miscibility is controlled by interfacial tension of hydrocarbons and carbon dioxide so the importance of investigation of the interfacial tension becomes highlighted.in this investigation by using radial basis function (RBF) artificial neural network (ANN) as a novel approach the interfacial tension of hydrocarbons and carbon dioxide in terms of pressure, temperature, liquid and gas densities and molecular weight of alkane. The graphical and statistical results illustrated the fact that RBF-ANN algorithm is applicable for estimation of interfacial tension between hydrocarbons and carbon dioxide with great accuracy.     

Application of Fuzzy C-means algorithm as a novel approach to predict solubility of hydrocarbons in carbon dioxide

In the recent years, declination of oil reservoir causes the importance of researches on enhancement of oil recovery processes become more important. One of wide applicable approaches in enhancement of oil recovery is carbon dioxide injection which becomes interested because of relative low cost, good displacement and environmentally aspects. The injection of carbon dioxide to oil reservoir causes the lighter hydrocarbons of crude oil are extracted by CO₂. This phenomena can be affected by various factors such the solubility of hydrocarbons in carbon dioxide so in the present investigation Fuzzy c-means (FCM) as a novel approach for estimation of solubility of alkanes in carbon dioxide in terms of temperature, pressure and carbon number of alkane were utilized. The predicting algorithm FCM has reliable ability to estimate solubility based on graphical and statistical results. The coefficient of determination (R²) for training and testing data are calculated as 0.9856 and 0.9529 respectively.     

Applying Grid partitioning based Fuzzy inference system as novel algorithm to predict Asphaltene precipitation

Asphaltene precipitation is one of challenging problems in petroleum and chemical engineering so the importance of investigation of Asphaltene precipitation is clear. The asphaltene deposition effects on wellbore plugging, wettability alteration and facility damages. In order to solve these problems, a novel investigation based on Grid partitioning based Fuzzy inference system algorithm to predict precipitated asphaltene in terms of dilution ratio, temperature and carbon number of precipitant was developed. The predicting algorithm performance was evaluated statistically and graphically. The coefficients of determination (R²) for training and testing phases 0.9973 and 0.9900 respectively which confirm the great accuracy and high potential of predicting algorithm for estimation of precipitated asphaltene so this algorithm can be used as high accurate and simple software for prediction of asphaltene behavior in crude oil.     

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.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

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.     

Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery

Development of reliable and accurate models to estimate carbon dioxide–brine interfacial tension (IFT) is necessary, since its experimental measurement is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure. In the current study, feed forward artificial neural network is used for estimation of CO₂–brine IFT based on data from published literature which consists of a number of carbon dioxide–brine interfacial tension data covering broad ranges of temperature, total salinity, mole fractions of impure components and pressure. Trial-and-error method is utilized to optimize the artificial neural network topology in order to enhance its capability of generalization. The results showed that there is good agreement between experimental values and modeling results. Comparison of the empirical correlations with the proposed model suggests that the current model can predict the CO₂–brine IFT more accurately and robustly.     

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.     

南海北部边缘盆地CO2成因及充注驱油的石油地质意义

在获取大量地质、地球化学资料的基础上,对南海北部边缘盆地CO₂的成因及充注驱油特征进行了分析,结果表明:南海北部边缘盆地CO₂可划分为壳源型岩石化学成因、壳源型有机成因、壳幔混合型及火山幔源型成因4种成因类型;其中,莺歌海盆地壳源型及壳幔混合型CO₂运聚成藏主要受控于泥底辟热流体晚期分层分块多期局部上侵活动与上新统—中新统海相含钙砂泥岩的物理化学综合作用;琼东南盆地东部及珠江口盆地火山幔源型CO₂成藏主要受控于幔源型火山活动及沟通深部气源的基底深大断裂的展布;CO₂运聚成藏中,其充注驱油过程主要受运聚输导条件及气源供给等诸多地质关键因素的制约和控制.由于CO₂充注驱替往往导致油气藏中油气再分配或重新组合,并引起原来的油气产出及产状特征发生变化,故容易形成新的油藏或气藏.因此,可以将CO₂充注驱油特征作为判识油气成藏动态过程的示踪标志,用于预测油气运聚状态,追踪油气分布特征.     

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.     

Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations

Development of robust predictive models to estimate the transport properties of gases (namely viscosity and thermal conductivity) is of immense help in many engineering applications. This study highlights the application of the artificial neural network (ANN) and least squares support vector machine (LSSVM) modeling approaches to estimate the viscosity and thermal conductivity of CO₂. To propose the machine learning methods, a total of 800 data gathered from the literature covering a wide temperature range of 200–1000 K and a wide pressure range of 0.1–100 MPa were used. Particle swarm optimization (PSO) and genetic algorithm (GA) as population-based stochastic search algorithms were applied for training of ANNs and to achieve the optimum LSSVM model variables. For the purpose of predicting viscosity, the PSO-ANN and GA-LSSVM methods yielded the mean absolute error (MAE) and coefficient of determination (R²) values of 1.736 and 0.995 as well as 0.51930 and 0.99934, respectively for the whole data set, while for the purpose of predicting thermal conductivity, the PSO-ANN and GA-LSSVM models yielded the MAE and R² values of 1.43044 and 0.99704 as well as 0.72140 and 0.99857, respectively for the whole data set. Both methods provide properly capable method for predicting the thermal conductivity and viscosity of CO₂.     

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.     

A rapid method for interfacial tension calculation between rock plug and crude oil based on contact angles,application for EOR

Interfacial tension and contact angle are two specific important parameters to take decisions for enhanced oil recovery, for instance, proper chemicals to use for surface tension reduction, expected wettability of solids, interaction between crude oil and rock. For this purpose, the article presents a method for easy calculation of the solid-liquid interfacial tension based on contact angle measurements applying Neumann's correlation and Young's equation. The main idea stands on the calculation of the rock parameters, like wettability, with known substances and extend these results to crude oils. It was possible, based on the results obtained, to establish a relationship between solid-liquid interfacial tension and contact angle for the crude oil – rock system, which can definitively be used for the calculation of interfacial tension of any other fluid spread out on the same kind of rock. A linear regression was obtained with an accuracy as good as R² = 0.9989. Viscosity as a function of contact angle could also be obtained for the studied crude oils in the same kind of rock.