Implementing PSO-LSSVM model to approximate vaporization enthalpy of pure hydrocarbons and petroleum fractions

The purpose of the present research was to estimate the heat of vaporization for petroleum fractions and pure hydrocarbons by the least-square support vector machine (LSSVM) as a function of the specific gravity, molecular weight, and boiling point temperature. Moreover, a particle swarm optimization technique was applied to determine optimal dependent parameters of LSSVM. In addition, results obtained from the proposed LSSVM model have been compared to some developed correlations by scholars. According to statistical observations, the LSSVM model has acceptable predictions by the value of mean relative deviation and R-squared (R²) equal to 0.51% and 0.9998, respectively. The present predictive manner can be used in petroleum engineering for an accurate approximation of heat of vaporization.     

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.     

An Efficient Approach for the Determination of Oil Production Rate During the Water-flooding Recovery Method

In this work, a mathematical methodology namely, least square support vector machine (LSSVM) is implemented to predict the variation of oil production rate as a function of oil water viscosity ratio and water injection rate for water-flooding. Furthermore, the coupled simulated annealing (CSA) optimization technique is coupled with LSSVM to find the optimal architecture and parameters of the LSSVM. The obtained results demonstrate that the CSA-LSSVM estimations are in a satisfactory agreement with literature-reported data and the previously published correlation. Consequently, the R² and average absolute relative deviation of CSA-LSSVM model in testing phase are reported 0.979 and 8.15, respectively.     

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.     

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.     

Utilization of LSSVM algorithm for estimating synthetic natural gas density

In the gas engineering the accurate calculation for pipeline and gas reservoirs requires great accuracy in estimation of gas properties. The gas density is one of major properties which are dependent to pressure, temperature and composition of gas. In this work, the Least squares support vector machine (LSSVM) algorithm was utilized as novel predictive tool to predict natural gas density as function of temperature, pressure and molecular weight of gas. A total number of 1240 experimental densities were gathered from the literature for training and validation of LSSVM algorithm. The statistical indexes, Root mean square error (RMSE), coefficient of determination (R²) and average absolute relative deviation (AARD) were determined for total dataset as 0.033466, 1 and 0.0025686 respectively. The graphical comparisons and calculated indexes showed that LSSVM can be considered as a powerful and accurate tool for prediction of gas density.     

Phase equilibrium modelling of natural gas hydrate formation conditions using LSSVM approach

The formation of gas hydrates in industries and chemical plants, especially in natural gas production and transmission, is an important factor that can lead to operational and economic risks. Hence, if the hydrate conditions are well addressed, it is possible overcome hydrate-related problems. To that end, evolving an accurate and simple-to-apply approach for estimating gas hydrate formation is vitally important. In this contribution, the least square support vector machine (LSSVM) approach has been developed based on Katz chart data points to estimate natural gas hydrate formation temperature as function of the pressure and gas gravity. In addition, a genetic algorithm has been employed to optimize hyper parameters of the LSSVM. Moreover, the present model has been compared with five popular correlations and was concluded that the LSSVM approach has fewer deviations than these correlations so to estimate hydrate formation temperature. According to statistical analyses, the obtained values of MSE and R² were 0.278634 and 0.9973, respectively. This predictive tool is simple to apply and has great potential for estimating natural gas hydrate formation temperature and can be of immense value for engineers who deal with the natural gas utilities.     

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.     

Determination of oil pollutants in aqueous samples with the use of steam carrier gas chromatography

A laboratory chromatograph for analysis of trace amounts of petroleum products in water using steam as a carrier gas was proposed. The original design of a trap for sample preparation based on solid-phase microextraction was described. Measurements were made on a model solution of benzene, toluene, ethylbenzene, and o-xylene in water over the concentration range of 3 to 100 μg/dm³. The solubility of AI-92 (RON 92) gasoline and diesel fuel in water was measured. It was shown that the solubility of diesel fuel at room temperature in water did not exceed 12.9 ± 0.7 mg/dm³. The solubility of decane in water was measured, which turned out to be 0.18 ± 0.01 mg/dm³. Original Russian Text ? A.V. Chuikin, S.V. Grigors’ev, A.A. Velikov, 2006, published in Neftekhimiya, 2006, Vol. 46, No. 1, pp. 65–69.     

Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.     

Data-driven modeling of heavy oil viscosity in the reservoir from geophysical well logs

Viscosity is the most crucial fluid property on recovery and productivity of hydrocarbon reservoirs, more particularly heavy oil reservoirs. In heavy and extra heavy oil reservoirs e.g. bitumen and tar sands more energy is required to be injected into the system in order to decrease the viscosity to make the flow easier. Therefore, attempt to develop a reliable and rapid method for accurate estimation of heavy oil viscosity is inevitable. In this study, a predictive model for estimating of heavy oil viscosity is proposed, utilizing geophysical well logs data including gamma ray, neutron porosity, density porosity, resistivity logs, spontaneous potential as well as P-wave velocity and S-wave velocity and their ratio (Vp/Vs). To this end, a supervised machine learning algorithm, namely least square support vector machine (LSSVM), has been employed for modeling, and a dataset was provided from well logs data in a Canadian heavy oil reservoir, the Athabasca North area. The results indicate that the predicted viscosity values are in agreement with the actual data with correlation coefficient (R²) of 0.84. Furthermore, the outlier detection analysis conducted shows that only one data point is out of the applicability of domain of the develop model.     

A New Approach for Accurate Prediction of the Phase Behavior of the Complex Mixtures of Acid Gases,Hydrocarbons, Water,and Methanol in the Petroleum Industry

In this work a new approach for accurate prediction of the vapor-liquid equilibrium of the complex mixtures of water, methanol, acid gases (H₂S, CO₂), and hydrocarbons considering the hydrogen bonding association, hydrolytic dissociation, and acid gas solvation effects is presented. The overall average absolute deviation between the predicted and measured compositions regarding 330 sour gas mixtures is about 3.22%. The proposed CPA/electrolyte model is quite reliable over wide ranges of temperatures and sour gas concentrations and can be employed for accurate design of sour natural treatment and flow assurance systems in oil and gas industries.     

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.     

轮南油田注水系统结垢趋势预测

本文针对轮南油田油水系统的特点,对碳酸盐垢、硫酸盐垢的结垢趋势分别进行了理论预测,同时对室内仿真模拟实验结垢样品进行了X射线衍射分析。理论预测结果表明,轮南油田注水系统中可能会形成碳酸盐垢但不会形成硫酸盐垢,理论预测和室内仿真实验结果完全相符。本文为轮南油田注水系统选择合适的阻垢剂提供了依据。     

CPA方程对CO2-水体系相态研究

CO₂作为酸性气体之一,其热力学性质对石油、天然气开发至关重要。水通常在地层中与烃类共生,由于地层盐水的存在使得与烃类混合的气体量减少,并且这种效应将随着压力和水相量的增加而增加（随盐度的降低而减小）。因此,弄清CO₂-水体系的热力学性质将对理解这些过程具有重要的指导意义。通过运用SRK-CPA状态方程结合CR-1混合规则对CO₂-水体系的相平衡特征进行计算,研究CO₂在水中的溶解度和水在CO₂气相中的溶解度,并对308 K,373 K和473 K等3种温度下,CO₂-水体系不同缔合模型相互作用的模拟结果与实验数据进行分析,结果表明：在CO₂的临界温度和临界压力附近,由于发生了由气-液到液-液的相态转变,CO₂和水的溶解度在此温度和压力点将发生显著的变化;当CO₂作为非缔合物与缔合模型为4 C的水发生溶剂化交叉缔合时,运用CPA方程计算的溶解度结果与实验数据拟合较好。CPA方程在工程应用中能够满足含CO₂和水体系的热力学性质预测需求。     

Rigorous modeling of solubility of normal alkanes in supercritical CO2

In this contribution, ANFIS approaches are developed for the prediction of normal alkane solubility in supercritical carbon dioxide. Regarding the economic and environmental benefits of carbon dioxide injection, it introduced as a well-known procedure of EOR. With this in mind that solubility of normal alkanes followed by CO₂ injection affected by various operational condition, in this article functionality of solubility of normal alkanes in supercritical carbon dioxide from operational condition was investigated using Adaptive Neuro Fuzzy Interface System (ANFIS). Results demonstrate that the model is precise. The model shows an overall R² and AARD% estimations of 0.9921 and 0.89%, respectively.     

Evolving new group contribution-LSSVM model to estimate standard molar chemical exergy of pure organic substances

Chemical exergy values of pure organic compounds are required in order to perform an exergy analysis to achieve the optimum conditions. Development of reliable predictive tools for standard molar chemical exergy estimation, is of great importance. A least squares support vector machine (LSSVM) based group contribution (GC) method is proposed for standard molar chemical exergy prediction of pure organic compounds. The proposed model is trained and evaluated based on a comprehensive data base comprising standard molar chemical exergy for 133 organic compounds. 47 chemical substructures are employed in the process of model development. The proposed model is evaluated using different graphical and statistical error analysis. Determination coefficient (R²) and average absolute relative deviation (AARD%) values of 1.00 and 0.56% indicate the applicability potential and reliability of the predictions from the proposed model.     

Estimation of Hydrate Inhibitor Loss in Hydrocarbon Liquid Phase

Abstract

The amount of hydrate inhibitor to be injected in the gas processing and transmission system to avoid hydrate formation not only must be sufficient to prevent freezing of the inhibitor in the water phase but also must be sufficient to provide for the equilibrium vapor phase content of the inhibitor and the loss of the inhibitor in any liquid hydrocarbon. In this article, a new numerical algorithm is developed for estimation of loss of methanol in paraffinic hydrocarbons at various temperatures and methanol concentrations in the water phase The predicted values showed good agreement with the reported data. The solubility of methanol in paraffin hydrocarbons is calculated for temperatures in the range of 240° to 320°K and methanol concentrations up to 70% in the water phase, where the average absolute deviation is around 4%.     

Compositional modeling and simulation of dimethyl ether (DME)- enhanced waterflood to investigate oil mobility improvement

Dimethyl ether (DME) is a widely used industrial compound, and Shell developed a chemical EOR technique called DME-enhanced waterflood (DEW). DME is applied as a miscible solvent for EOR application to enhance the performance of conventional waterflood. When DME is injected into the reservoir and contacts the oil, the first-contact miscibility process occurs, which leads to oil swelling and viscosity reduction. The reduction in oil density and viscosity improves oil mobility and reduces residual oil saturation, enhancing oil production. A numerical study based on compositional simulation has been developed to describe the phase behavior in the DEW model. An accurate compositional model is imperative because DME has a unique advantage of solubility in both oil and water. For DEW, oil recovery increased by 34% and 12% compared to conventional waterflood and CO₂ flood, respectively. Compositional modeling and simulation of the DEW process indicated the unique solubility effect of DME on EOR performance.     

Modeling of the Properties of Gasoline and Petroleum Fractions Using a Robust Scheme

Gasoline is one of the most recognized products of the petroleum industry due to its use as a liquid fuel worldwide. As a result, it is of great importance to accurately determine the properties of gasoline, so as to evaluate its quality. In this article, an effective mathematical and predictive strategy, namely least squares support vector machines (LSSVM) is applied to predict some gasoline properties, viz. specific gravity (SG), motor octane number (MON), research octane number (RON), and Reid vapor pressure (RVP). A comprehensive error analysis is also undertaken to compare the values predicted from the model with actual data which enables one to evaluate the performance of the model developed in this study. The results indicate that the model developed has reasonable accuracy and prediction capability. The correlation indices, R², are 0.990, 0.933, 0.955, and 0.920 for SG, MON, RON, and RVP, respectively.