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Relative permeability of the petroleum reservoirs is a key parameter for various aspects of the petroleum engineering area like as reservoir simulation, history matching and etc. Due to this fact, various approaches such as experimental, theoretical and numerical approaches have been studied however; such experimental methods are time consuming, complicated and expensive. Based on the addressed disadvantages, robust, rapid, simple and accurate model is needed to represent gas/oil relative permeability through petroleum reservoirs. In this research communication we utilized the concept of various intelligent approaches such as least square support vector machine (LSSVM) which is high attended branches of artificial intelligent approaches. To develop and test the proposed LSSVM approach massive experimental relative permeability data from literature survey was faced to the addressed model. The suggested LSSVM method has low deviation from relevant measured values and statistical factors of the addressed model solutions were calculated. According to the determined statistical factors, the results of the proposed LSSVM approach prove and certify the high performance and low uncertainty of the addressed model in prediction gas/oil relative permeability in petroleum reservoirs. Finally, the suggested LSSVM model could help us to prepare more precise and accurate relative permeability curves without extensive experiment and furthermore, could lead to provide high performance reservoir simulation with low uncertainty. © 2014 Elsevier Ltd. All rights reserved. Source


Danaee I.,Petroleum University of Technology of Iran
Journal of Industrial and Engineering Chemistry | Year: 2013

Nucleation and growth of palladium on graphite electrode was studied by electrochemical techniques. Crossovers in cyclic voltammograms demonstrate that the deposition of palladium proceed via a nucleation/growth mechanism. In the early stage of the deposition, two-dimensional (2D-3D) nucleation and growth proceeding through instantaneous and a multitude of progressive steps followed the initial double layer charging. Non-linear fitting methods were applied to obtain the kinetic parameters in the light of Bewick, Fleischmann, and Thirsk theory for 2D and Armstrong, Fleischmann, and Thirsk model for 3D nucleation and growth process. © 2012 The Korean Society of Industrial and Engineering Chemistry. Source


Asoodeh M.,Petroleum University of Technology of Iran | Bagheripour P.,Islamic Azad University at Gachsaran
Rock Mechanics and Rock Engineering | Year: 2012

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone. © 2011 Springer-Verlag. Source


Ghiasi M.M.,Petroleum University of Technology of Iran
Journal of Natural Gas Chemistry | Year: 2012

Production, processing and transportation of natural gases can be significantly affected by clathrate hydrates. Knowing the gas analysis is crucial to predict the right conditions for hydrate formation. Nevertheless, Katz gas gravity method can be used for initial estimation of hydrate formation temperature (HFT) under the circumstances of indeterminate gas composition. So far several correlations have been proposed for gas gravity method, in which the most accurate and reliable one has belonged to Bahadori and Vuthaluru. The main objective of this study is to present a simple and yet accurate correlation for fast prediction of sweet natural gases HFT based on the fit to Katz gravity chart. By reviewing the error analysis results, one can discover that the new proposed correlation has the best estimation capability among the widely accepted existing correlations within the investigated range. © 2012 CAS/DICP. Source


Ahmadi M.H.,University of Tehran | Ahmadi M.A.,Petroleum University of Technology of Iran
Energy Conversion and Management | Year: 2014

Optimum ecological and thermal performance assessments of an Ericsson cryogenic refrigerator system are investigated in different optimization settings. To evaluate this goal, ecological and thermal approaches are proposed for the Ericsson cryogenic refrigerator, and three objective functions (input power, coefficient of performance and ecological objective function) are gained for the suggested system. Throughout the current research, an evolutionary algorithm (EA) and thermodynamic analysis are employed to specify optimum values of the input power, coefficient of performance and ecological objective function of an Ericsson cryogenic refrigerator system. Four setups are assessed for optimization of the Ericsson cryogenic refrigerator. Throughout the three scenarios, a conventional single-objective optimization has been utilized distinctly with each objective function, nonetheless of other objectives. Throughout the last setting, input power, coefficient of performance and ecological function objectives are optimized concurrently employing a non-dominated sorting genetic algorithm (GA) named the non-dominated sorting genetic algorithm (NSGA-II). As in multi-objective optimization, an assortment of optimum results named the Pareto optimum frontiers are gained rather than a single ultimate optimum result gained via conventional single-objective optimization. Thus, a process of decision making has been utilized for choosing an ultimate optimum result. Well-known decision-makers have been performed to specify optimized outcomes from the Pareto optimum results in the space of objectives. The outcomes gained from aforementioned optimization setups are discussed and compared employing an index of deviation presented in this work. © 2014 Elsevier Ltd. All rights reserved. Source

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