State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

Abstract

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive models/correlations available in the literature. It has been observed that the proposed model correctly captures the physical trend of changing the natural gas viscosity as a function of the temperature and pressure. Finally, sensitivity analysis was performed to assess the effect of the gas viscosity uncertainty on the cumulative gas production for a synthetic natural gas reservoir, using a numerical reservoir simulation. Results revealed that applications of LSSVM modeling can lead to a more accurate and reliable estimation of the gas viscosity over a wide range of reservoir conditions.

Application of foam floods for enhancing heavy oil recovery through stability analysis and core flood experiments

“…Leverage methodology determines the outliers based on standardized residuals values (R) and the Hat matrix (H) that consists of experimental data and the predicted values obtained from a model. The Hat matrix and the corresponding indices are calculated from the following equation: [68,70,71,78,79] H…”

“…After that, the Williams plot is plotted to identify the outliers. [67,68,70,71,78,79] As a matter of fact, the Williams plot shows the relation between Hat indices and standardized residuals. Usually the cut-off value of 3 considered for leverage value and also the warning Leverage ðH Ã Þ is equal to 3(p þ 1)/n.…”

“…It has been studied extensively for both classification and regression analysis. [65,79,81,82] The details of LS-SVM theory can be found in in previous researches, [65,79,80] so we will not describe its theory and main idea here.…”

“…To this end, the mean squared error between the outputs of developed model and corresponding experimental values was chosen as the objective function. [65,79] It should be mentioned that the coupled simulated annealing optimization algorithm was used to optimize the mentioned parameters. [77] Assessment of obtained experimental data…”

A novel modeling approach to optimize oxygen–steam ratios in coal gasification process

“…These correlation coefficients 291 have values ranging from À1 to +1. A value of +1 and À1 imply that 292 the variables have a perfect increasing and decreasing linear 293 relationship, respectively [35,45]. …”