Phase behavior modelling of asphaltene precipitation utilizing MLP-ANN approach

Since the sedimentation of heavy hydrocarbons such as asphaltenes, is the highlighted concern in production and operational, many studies were focused on this challenge in the petroleum industry. Therefore, the petroleum engineers should access to the asphaltene precipitation as an essential factor in order to conquer its problems. In this study, an empirical model for prediction asphaltene precipitation by multi-layer perceptron artificial neural network (MLP-ANN) is offered that takes the effect of the temperature, dilution ratio, and molecular weight for different n-alkanes. The output of this model showed 0.9999 for correlation coefficient (R²) and 0.000495 for mean squared error (MSE). This value illustrates the high quality of this model in compare of other available models. So far, MLP-ANN can offer significant accuracy in predicting asphaltene precipitation of asphaltene and other heavy oil.     

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.     

A novel MLP-ANN approach to predict solution gas-oil ratio

Abstract

The better understanding and estimation of reservoir fluids properties have straight effects on accuracy of different processes such as simulation, well testing, and material balance calculations, so importance of accurate estimation of PVT properties such as solution gas-oil ratio is obvious. To this end, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel predictive tool to estimate solution gas-oil ratio in terms of temperature, bubble point pressure, oil American Petroleum Institute gravity API, and gas specific gravity. Therefore, a total number of 1,137 experimental solution gas-oil ratios has been gathered from reliable databank for evaluation of model outputs. The different graphical and statistical analyses such as determination of average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) were used to evaluate the performance of MLP-ANN algorithm. The comparisons show that MLP-ANN algorithm has great accuracy in prediction of solution gas-oil ratio, so it can be used as a simple tool to predict phase behavior of reservoir fluids.     

Neural computing approach for predicting vaporization enthalpy of pure hydrocarbons and petroleum fractions

The current collaboration was aimed to approximate the heat of vaporization for petroleum fractions and pure hydrocarbons through using the multi-layer perceptron artificial neural network (MLP-ANN) based on the specific gravity, molecular weight, and boiling point temperature. Furthermore, Levenberg Marquardt algorithm was utilized to train the ANN structure and optimize its tuning parameters. Another comparison was carried out between the outcomes of suggested MLP-ANN model and six well-known correlations. Better results were observed for predicting heat of vaporization by the MLP-ANN model with the obtained value of mean relative error (MRE) and R-squared (R²) equal to 1.31% and 0.9962%, respectively. This computational approach can be applied in the petroleum engineering for a precise determination of heat of vaporization.     

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.     

Proposing a novel and accurate model for forecasting dynamic viscosity of n-alkane in operational conditions

Abstract

The viscosity of fluid is known as resistivity of fluid to flow and straightly affected by temperature and pressure. As it is obvious, the viscosity of reservoir fluid is known as one of the critical parameters which extensively effect on production. Therefore, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel and accurate model to predict dynamic viscosity of normal alkanes in the operational conditions. To this end, 228 dynamic viscosity points as function of carbon number of n-alkane, temperature, and pressure were collected from a reliable paper. The comparison between MLP-ANN outputs and experimental dynamic viscosities is performed in graphical and statistical manners. The calculated coefficients of determination 0.99739 and 0.99051 for training and testing phases express the great ability of MLP-ANN algorithm in prediction of dynamic viscosity of n-alkane. According to the analysis, MLP-ANN has enough accuracy and potential to be used as software for which applicable in petroleum industry.     

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.     

Prediction of catalytic hydro conversion of normal heptane over catalysts using multi-layer perceptron artificial neural network (ANN-MLP)

Abstract

Catalytic reforming in the presence of metal-acid bifunctional catalysts is a widely used reaction in refinery industry to improve some properties of products like temperature performance of diesel and octane number of gasoline. So the ability of the prediction of Iso-C₇ selectivity during n-heptane hyroconversion is a key issue. In this study, a data set which was collected from previous publications are put in an artificial neural network-multi layer perceptron (MLP-ANN) model. Properties used as input parameters are: temperature, pressure, WHSV (weight hourly space velocity), catalysts acidity and pore volume of the catalysts, and Iso-C₇ selectivity used as the output parameter. Based on results, the MLP-ANN has great ability to estimate n-heptane hydroconversion. Root mean squared error (RMSE) and R-squared (R²) error were calculated for training, test and total set of data. For training set, test set and total set RMSE are 97915, 5.1607 and 3.9441, respectively and corresponding R² are 0.97915, 0.9334 and 0.9746 respectively.     

基于FOA-SVM模型的油田采出液管道结垢趋势预测

针对油田采出液管道的钙镁无机盐结垢趋势问题,建立了FOA-SVM模型,使用新疆油田实验和测算得到了136组管道结垢趋势及影响因素数据,利用其中的116组数据对模型进行训练,对剩余的20组数据进行预测,并将预测结果与BP神经网络模型、CV-SVM模型以及LS-SVM模型进行对比,以此验证FOA-SVM模型在该领域应用的先进性。研究表明:影响采出液管道结垢趋势的相关因素较多,这是制约结垢趋势预测准确度的主要原因;应用FOA-SVM模型对结垢趋势进行预测,预测结果的误差小于其他预测模型,模型训练时间为2.68 s,仅略高于BP神经网络模型,证明FOA-SVM模型应用于管道结垢量预测具有很强的先进性。     

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.     

基于小波变换和LSSVM-DE的天然气日负荷组合预测模型

为了提高天然气短期负荷的预测精度,提出了基于小波变换和LSSVM-DE(Least Squares Support Vector MachineDifferential Evolution)的天然气日负荷组合预测模型,首先,采用Mallat快速算法对天然气日负荷实际采集数据样本时间序列进行小波分解;其次,对分解出来的高频分量和低频分量分别建立LSSVM预测模型,各分量的模型参数分别采用DE进行优化,以期得到更准确的预测结果;最后,分别对各分量的预测结果进行小波重构。以某市实际采集的样本数据为例,并将重构结果与单独应用LSSVM预测模型及ANN(Artificial Neural Networks)预测模型进行对比分析。结果表明:小波变换和LSSVM-DE预测模型的预测精度分别比单独应用LS-SVM和ANN预测模型高出1.662%、1.14%、3.96%、2.99%、15.53%和1.942%、1.01%、3.07%、1.86%、12.26%。该结论预示着将小波变换和LSSVM-DE理论相结合对天然气日负荷时间序列进行预测是一种行之有效的方法。     

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.     

Evaluating the Unloading Gradient Pressure in Continuous Gas-lift Systems During Petroleum Production Operations

Evaluating the performance, applicability, and field testing of various artificial lift methods, in particular continued gas-lift, can be time consuming and costly. To overcome these drawbacks, it is needed to propose a reliable model to estimate gas-lift applicability in advance of the installation under specific well operational conditions such as tubing size and design oil rate. In this study, the robust least square modification of support vector machine (LSSVM) methodology is implemented to propose a computer program, by which the unloading pressure gradient region can be determined in various design oil production rates and also tubing sizes. The developed LSSVM model results indicate 1.084% average absolute relative deviation from the corresponding unloading pressure gradient literature values, and squared correlation coefficient of 0.9994.     

Radial basis function (RBF) network for modeling gasoline properties

One of the important products of a crude oil refinery is gasoline which is greatly used as a liquid fuel. Hence, it is necessary to accurately specify its quality by measuring different properties of gasoline. In this study, radial basis function neural networks were utilized for estimation of different characteristics of gasoline including specific gravity (SG), Reid vapor pressure (RVP), research octane number (RON) and motor octane number (MON). The genetic algorithm was used as an optimization algorithm to optimize the maximum neuron number and spread of model. Results reveal that the developed GA-RBF model is effective and precise for estimating experimental data. Furthermore, comparison between the GA-RBF model and a previously reported LSSVM model in literature shows the superiority of GA-RBF model.     

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.     

Forecasting Natural Gas Production Using Various Regression Models

Natural gas is an important energy sources governing the world economy. Therefore, accurate forecasting models for its production rate are needed to provide better planning. In the present study, various modeling approaches are used to model global natural gas production (NGP). The regression models developed are validated using some statistical approaches. The developed models are then compared using a test data set which is not utilized during construction of models. Mean absolute percentage error is used for comparing the developed modes. The results reveal that proposed models are capable of giving adequate prediction for the NGP with an acceptable accuracy level. Additionally, the compared results show that the S regression model is more reliable than the other regression models.     

Prediction of Oil Production Rate Using Vapor-extraction Technique in Heavy Oil Recovery Operations

Heavy oil and bitumen are major parts of the petroleum reserves in north of America. Owning to this fact and produce this type of oils various methods could be considered. Vapor extraction (VAPEX) method is one of the promising methods that have been executed successfully through North America, specifically in Canada, and is a solvent-based approach. The authors present the implication of the new type of network approach with low parameters called least square support vector machine (LSSVM) in prediction of the oil production rate via VAPEX method. To evaluate and examine the accuracy and effectiveness of both developed models in estimation oil production rate via VAPEX method, extensive experimental VAPEX data were faced to the two addressed models. Moreover, statistical analysis of the output results of the LSSVM was conducted. Based on the determined statistical parameters, the outcomes of the LSSVM model has lower deviation from relevant actual value. Knowledge about oil production via enhanced oil recovery (EOR) methods could help to select and design more proper EOR approach for production purposes. Outcomes of this research communication could improve precision of the commercial reservoir simulators for heavy oil recovery specifically in thermal techniques.     

基于GWO-LSSVM算法的海底管道腐蚀预测模型研究

目的 针对海底管道腐蚀影响因素存在信息叠加与相互耦合、作用机理复杂、腐蚀速率预测难度大的问题,提出一种灰狼优化(GWO)算法优化最小二乘支持向量机(LSSVM)的腐蚀速率预测新模型.方法 该模型利用灰狼优化算法对最小二乘支持向量机的核参数与惩罚因子进行迭代寻优,减少参数选择的盲目性,提升预测精度,应用该模型对海水挂片腐...     

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.     

一种预测聚合物驱开发动态的模型

在分析聚合物驱与水驱驱替机理差异的基础上,对水驱预测的经验公式进行改进,建立了适用于聚合物驱的预测模型。预测模型可考虑聚合物溶液黏度、注入PV数及最大残余阻力系数等参数对聚合物驱效果的影响。预测模型预测大庆喇南和喇北东块的聚合物驱含水率曲线与实际含水动态吻合,预测提高采收率误差小于8%,结果可靠。预测模型所需参数简单、计算简便,可用于聚合物驱动态预测、效果评价及潜力分析。