Sharif Digital Repository

In this investigation, application of the least square support vector machine (LSSVM) for modeling of the electrical resistivity of the magnetic Zn-Mn-S nanocrystalline semiconductor films has been described. The model has been trained based on the experimental data obtained from a published work by Sreekantha Reddy et al. The model inputs are temperature and variations in the concentrations of Zn, Mn. The results indicate that LSSVM is able to be used for accurate prediction of the electrical resistivity of the Zn-Mn-S nanocrystalline semiconductor films  

Accurate knowledge of pure hydrocarbon and natural gas viscosity is essential for reliable reservoir characterization and simulation as well as economic design of natural gas processing and transport units. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. When there is no available experimental data for the required composition, pressure, and temperature conditions, the use of predictive methods becomes important. In this communication, a novel approach was proposed to develop for prediction of viscosity of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide,... 

Dew-point pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to present a reliable, computer-based predictive model for prediction of dew-point pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of... 

Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The... 

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... 

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from... 

Viscosity of crude oil is a fundamental factor in simulating reservoirs, forecasting production as well as planning thermal enhanced oil recovery methods which make its accurate determination necessary. Experimental determination of reservoir oil viscosity is costly and time consuming. Hence, searching for quick and accurate determination of reservoir oil viscosity is inevitable. The objective of this study is to present a reliable, and predictive model namely, Least-Squares Support Vector Machine (LSSVM) to predict reservoir oil viscosity. To this end, three LSSVM models have been developed for prediction of reservoir oil viscosity in the three regions including, under-saturated, saturated... 

Deposition of barium sulfate (or BaSO4) has already been recognized as a devastating problem facing process industries and oilfield operations, mainly owing to its low solubility in aqueous solutions. Predicting and also preventing the overall damage caused by BaSO4 precipitation requires a profound knowledge of its solubility under different thermodynamic conditions. The main aim of this study is to develop a solubility prediction model based on a hybrid of least squares support vector nachines (LSSVM) and coupled simulated annealing (CSA) aiming to predict the solubility of barium sulfate over wide ranges of temperature, pressure and ionic compositions. Results indicate that predictions of... 

Demands for hydrocarbon production have been increasing in recent decades. As a tertiary production processes, chemical flooding is one of the effective technologies to increase oil recovery of hydrocarbon reservoirs. Retention of surfactants is one of the key parameters affecting the performance and economy of a chemical flooding process. The main parameters contribute to surfactant retention are mineralogy of rock, surfactant structure, pH, salinity, acidity of the oil, microemulsion viscosity, co-solvent concentration, and mobility. Despite various theoretical studies carried out so far, a comprehensive and reliable predictive model for surfactant retention is still found lacking. In this... 

Compressibility factor (z-factor) values of natural gasses are essential in most petroleum and chemical engineering calculations. The most common sources of z-factor values are laboratory experiments, empirical correlations and equations of state methods. Necessity arises when there is no available experimental data for the required composition, pressure and temperature conditions. Introduced here is a technique to predict z-factor values of natural gasses, sour reservoir gasses and pure substances. In this communication, a novel mathematical-based approach was proposed to develop reliable model for prediction of compressibility factor of sour and natural gas. A robust soft computing... 

Vapor liquid equilibrium (VLE) data has significant role in designing processes which include vapor and liquid in equilibrium. Since it is impractical to measure equilibrium data at any desired temperature and pressure, particularly near critical region, thermodynamic models based on equation of state (EOS) are usually used for VLE estimating. In recent years due to the development of numerical tools like artificial intelligence methods, VLE prediction has been find new alternatives. In the present study a novel method called Least-Squares Support Vector Machine (LSSVM) used for predicting bubble/dew point pressures of binary mixtures containing carbon dioxide (CO 2) + cyclic compounds as... 

In oil and gas fields, it is a common practice to flow liquid and gas mixtures through choke valves. In general, different types of primary valves are employed to control pressure and flow rate when the producing well directs the natural gas to the processing equipment. In this case, the valve normally is affected by elevated levels of flow (or velocity) as well as solid materials suspended in the gas phase (e.g., fine sand and other debris). Both surface and subsurface chokes may be installed to regulate flow rates and to protect the porous medium and surface facilities from unusual pressure instabilities.In this study a reliable, novel, computer based predictive model using Least-Squares... 

The burning of fossil fuels such as gasoline, coal, oil, natural gas in combustion reactions results in the production of carbon dioxide. The phase behavior of the carbon dioxide + water system is complex topic. Unlike methane, CO2 exhibits a minimum in the water content. These minima cannot be predicted by existing methods accurately. In this communication, two mathematical-based procedures have been proposed for accurate computation of CO2 water content for tempe-ratures between 273.15 and 348.15 K and the pressure range between 0.5 and 21 MPa. The first is based on least squares support vector machine (LSSVM) algorithm and the second applies multilayer perceptron (MLP) artificial neural... 

Geologic sequestration of CO2 in deep saline aquifers is becoming increasingly important as a method with the greatest potential to economically sequester large volumes of anthropogenic CO2. The interfacial tension (IFT) between the formation brine in the aquifer and the injected CO2 phase has a significant influence on the displacement, and its precise determination is essential for accurate modeling and evaluation of such a process. This paper presents two new mathematical models to calculate the brine/CO2 IFT. The two models differ in input parameters; pressure, temperature, and salinity for the first model, and pressure, temperature, and brine composition for the second one. The proposed... 

Precipitation of strontium sulfate (or SrSO4) has already been distinguished as one of the most costly and critical problems which may occur in process industries and oilfield operations. Costs due to scaling and remedial actions that need to be taken afterward are generally high owing to low solubility of SrSO4 in aqueous solutions. Therefore, a thorough understanding of the SrSO4 thermodynamic behavior under various operating conditions is vital to predict or even avoid the overall damage caused by scaling. The primary aim of this work is to develop a model based on Least Squares Support Vector Machine (LSSVM) and Coupled Simulated Annealing (CSA) referred to as CSA-LSSVM to predict... 

In this communication, a hybrid model based on Least Square Support Vector Machine (LSSVM) was constructed to predict CO2 corrosion rate. The input parameters of the model are temperature, CO2 partial pressure, flow velocity and pH. The data used for training and testing of the developed model are 612 and 109 data, respectively. In order to benefit LSSVM from Kernel learning, we compared three kernel functions to select the most efficient one. Furthermore, Coupled Simulated Annealing (CSA) optimization technique was adapted to choose the best optimal values of the model parameters. The results elucidate that Gaussian Kernel functions is the desired function which can afford high accuracy for... 

As noteworthy properties of amine aqueous solutions, the densities and viscosities of aqueous N-Methyldiethanolamine (MDEA) solutions and mixtures of MDEA with 2-Amino-2-methyl-1-propanol (AMP), Diisopropanolamine (DIPA), Monoethanolamine (MEA), and Diethanolamine (DEA) were estimated under CO2 gas loading using Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Artificial Neural Network (MLPANN), Support Vector Machine (SVM), and Least Square Support Vector Machine (LSSVM). The density and viscosity were estimated as a function of temperature, CO2 loading, pressure, and molecular weight of mixtures. In this regard, the actual data points were collected from the... 

A thorough understanding and accurate prediction of non-Newtonian fluid flow dynamics in rotating annular media are of paramount importance to numerous engineering applications. This is in particular relevant to oil and gas industry where this type of flow could occur during, e.g., drilling, well completion, and enhanced oil recovery scenarios. Here, mathematically we report on physical-based (numerical) and data-driven (intelligent) modeling of three-dimensional laminar flow of non-Newtonian fluids driven by axial pressure gradient in annular media that consist of a coaxially rotating inner cylinder. We focus on the dynamics of pressure loss ratio (PLR)—the ratio of total pressure loss in... 

Quasi-periodic and chaotic behavior, along with the control of chaos for a Gyrostat satellite (GS), is investigated in this work. The quaternion-based dynamical model of the GS is first derived, and then the influences of the reaction wheels in the GS structure, under the gravity gradient perturbation that causes a route to chaos through quasi-periodicity mechanism, is investigated. For the suppression of chaos in the system, a chaos control system with the quaternion feedback is designed for the GS based on the extension of the Ott-Grebogi-Yorke (OGY) method using the linearization of the Poincaré map. In the extended OGY controller, the Poincaré map is estimated using the Least Square...