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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.

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.

Danaee I.,Petroleum University of Technology of Iran
Journal of Electroanalytical Chemistry | Year: 2011

The kinetics of palladium electrocrystallization from 0.01 M Pd(NH 3)4Cl2, NH4Cl, and NH4OH bath (pH = 10) is studied by means of cyclic voltammetry (CV), impedance spectroscopy (EIS) and electrochemical noise (EN). Crossovers in cyclic voltammograms demonstrate that the deposition of palladium proceed via a nucleation/growth. A model for palladium deposition with multi-step mechanism is proposed to account for electrode kinetics. Electrodeposition mechanism takes place trough interfacial reactions with reaction intermediates. Parameters of impedance model in this system can be calculated from the fitting of experimental data to the Faradaic impedance function derived theoretically. With the aid of an equivalent circuit it is possible to determine the charge transfer resistance and the resistance and the capacity of lattice formation. The electrochemical noise is shown to originate from the random birth of edges and therefore birth rate of monolayers was obtained. © 2011 Elsevier B.V. All rights reserved.

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.

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.

Danaee I.,Petroleum University of Technology of Iran | Noori S.,K. N. Toosi University of Technology
International Journal of Hydrogen Energy | Year: 2011

The mechanism and kinetics of the hydrogen evolution reaction (HER) on graphite modified with Ni and NiMn electrode (G/Ni and G/NiMn) in 0.1 M NaOH solution were studied using the methods of steady-state polarization, electrochemical impedance spectroscopy, cyclic voltammetry and open circuit potential transient. The addition of Mn to Ni significantly increases the catalytic activity in HER due to higher real surface area and higher intrinsic activity. The simulation of the data obtained from these methods, using nonlinear fitting procedure allowed us to determine the rate constants of Volmer, Heyrovsky and Tafel steps associated with the mentioned reaction. The kinetics results indicate that HER mechanism for G/NiMn electrode at low negative potentials is a serial combination of Volmer and parallel Tafel and Heyrovsky steps. At high negative potentials where the hydrogen coverage reaches its limiting value, a Tafel line with the slope of -125 mV dec-1 is obtained. In this potential region the mechanism of the HER follows Volmer-Heyrovsky while the Tafel step has negligible contribution. Open circuit potential measurements for G/Ni and G/NiMn at different charging currents show that hydrogen absorption into the electrode material occurs. © 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

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.

Ahmadi M.A.,Petroleum University of Technology of Iran | Shadizadeh S.R.,Petroleum University of Technology of Iran
Fuel | Year: 2012

Asphaltene precipitation affects enhanced oil recovery processes through the mechanism of wettability alteration and blockage. Asphaltene precipitation is very sensitive to the reservoir conditions and fluid properties, such as pressure, temperature and injected fluid molecular weight. In this work, the model based on a feed-forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) as an artificial intelligence modeling tool to predict asphaltene precipitation due natural depletion. Particle swarm optimization (PSO) is used to decide the initial weights of the neural network. The PSO- ANN model is applied to the experimental data from one of northern Persian Gulf oil field has been used to develop this model. The predicted results from the PSO-ANN model and BP-ANN were compared to the experimental precipitation data. The average relative absolute deviation between the model predictions and the experimental data was found to be less than 4%. A comparison between the prediction of this model and the alternatives showed that the PSO-ANN model predicts asphaltene precipitation more accurately. © 2012 Elsevier Ltd. All rights reserved.

Ahmadi M.A.,Petroleum University of Technology of Iran
Fluid Phase Equilibria | Year: 2012

The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs reduce considerably the rock permeability and the oil recovery. In the present paper, the model based on a feed-forward artificial neural network (ANN) to predict asphaltene precipitation of the reservoir is proposed. After that ANN model was optimized by unified particle swarm optimization (UPSO). UPSO is used to decide the initial weights of the neural network. The UPSO-ANN model is applied to the experimental data reported in the literature. The performance of the UPSO-ANN model is compared with scaling model. The results demonstrate the effectiveness of the UPSO-ANN model. © 2011.

Mirzaee A.,Petroleum University of Technology of Iran | Salahshoor K.,Petroleum University of Technology of Iran
Journal of Process Control | Year: 2012

In this paper, a new active fault tolerant control (AFTC) methodology is proposed based on a state estimation scheme for fault detection and identification (FDI) to deal with the potential problems due to possible fault scenarios. A bank of adaptive unscented Kalman filters (AUKFs) is used as a core of FDI module. The AUKF approach alleviates the inflexibility of the conventional UKF due to constant covariance set up, leading to probable divergence. A fuzzy-based decision making (FDM) algorithm is introduced to diagnose sensor and/or actuator faults. The proposed FDI approach is utilized to recursively correct the measurement vector and the model used for both state estimation and output prediction in a model predictive control (MPC) formulation. Robustness of the proposed FTC system, H ∞ optimal robust controller and MPC are combined via a fuzzy switch that is used for switching between MPC and robust controller such that FTC system is able to maintain the offset free behavior in the face of abrupt changes in model parameters and unmeasured disturbances. This methodology is applied on benchmark three-tank system; the proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated leading to an offset free behavior in the presence of sensor/actuator faults that can be either abrupt or drift change in biases. Analysis of the simulation results reveals that the proposed approach provides an effective method for treating faults (biases/drifts in sensors/actuators, changes in model parameters and unmeasured disturbances) under the unified framework of robust fault tolerant control. © 2012 Elsevier Ltd. All rights reserved.

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