EOR IOR Research Institute

Tehrān, Iran

EOR IOR Research Institute

Tehrān, Iran
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Ahmadi M.A.,EOR IOR Research Institute | Soleimani R.,Islamic Azad University at Neyshabur | Bahadori A.,Southern Cross University of Australia
Energy Sources, Part A: Recovery, Utilization and Environmental Effects | Year: 2016

ABSTRACT: Natural gas is a very important source of energy. In natural gas processing, accurate prediction of methanol loss to the vapor phase during natural gas hydrate inhibition is necessary to compute the total methanol injection rate required to effectively prevent the formation of natural gas hydrate. A reliable prediction tool that has the capability to accurately predict methanol losses to the vapor phase is thus needed. In order to address this matter, the current research was aimed at assessing the ability and feasibility of a robust computational intelligence paradigm. Based on a total of 326 dataset collected from the reliable literature, methanol loss to the vapor phase was predicted using artificial neural network (ANN) linked with particle swarm optimization (PSO) which is employed to determine the optimal values of the ANN weights. Success of the introduced hybrid intelligence model (or PSO-ANN) was confirmed with overall mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) values of 0.16421, 0.33210, and 0.99696, respectively. © 2016 Taylor & Francis Group, LLC.

Ahmadi M.A.,Petroleum University of Technology of Iran | Ahmadi M.A.,EOR IOR Research Institute | Pouladi B.,Amirkabir University of Technology | Barghi T.,French Institute of Petroleum
Journal of Natural Gas Science and Engineering | Year: 2016

CO2 injection is a most promising method for enhancing oil recovery from petroleum reservoirs especially from depleted one. CO2 injection can be employed as secondary and tertiary enhanced oil recovery (EOR) methods. The most prominent advantage of CO2 injection is reducing greenhouse gases in the atmosphere and consequently reducing environmental problems. Different approaches have been employed to study the performance of CO2 in increasing oil production including miscible/immiscible injection, carbonated water injection (CWI) and CO2 injection into aquifer. In these methods different parameters can effect such as minimum miscible pressure (MMP), injection rate and so forth. Therefore, the main goal of this paper is to study of different CO2 injection methods and the effect of operational factors on the performance of each method by a numerical simulation model. In this study, a synthetic reservoir is considered for numerical simulation of CO2 injection process. To assess this goal, three different CO2 injection strategies including CO2 injection into the bottom aquifer, CO2 injection into oil column (pay-zone) and simultaneous CO2 injection into aquifer and pay-zone are defined. Based on the results obtained from three aforementioned injection scenarios, simultaneous CO2 injection into aquifer and pay-zone leads to higher oil recovery factor in comparison with other scenarios. Critical injection rate for each scenario was determined and employed in the case of simultaneous CO2 injection into aquifer and pay-zone. Moreover, at the same CO2 injection rate, injection into aquifer leads to produce more oil compared to CO2 injection into the pay-zone. © 2016 Elsevier B.V.

Ahmadi M.A.,Petroleum University of Technology of Iran | Ahmadi M.A.,EOR IOR Research Institute | Zahedzadeh M.,EOR IOR Research Institute | Shadizadeh S.R.,Petroleum University of Technology of Iran | Abbassi R.,Islamic Azad University at Marvdasht
Fuel | Year: 2015

Improving the recovery factor of conventional oil reservoirs is not a far-fetched target when injecting miscible gases is discussed in technical Enhanced Oil Recovery (EOR) plan. Considering the leading role that Minimum Miscible Pressure (MMP) factor plays in the scenario of a miscible gas injection, and the significant impact that it does have on the sweep efficiency of the injected gas is inevitable. Because of the expensive, difficult and time consuming laboratory techniques which are used to obtain the MMP, concluding a quick, robust and cheap solution to measure the MMP has been turned into petroleum researchers' priorities. In the current study, Least Square Support Vector Machine (LS-SVM) and evolutionary algorithms (for example, Genetic Algorithm (GA) and Imperialist Competitive Algorithm), both addressed in previous literatures, have been employed to estimate the MMP. A set of laboratorial data accessible in the open literature was gained to test the reliability of the proposed HGAPSO-LSSVM model which its generated results have been compared with the other proposed intelligent approaches. Moreover, the performances of both implemented solutions certify statistically the strong potential of models in prediction of the MMP. © 2015 Elsevier Ltd. All rights reserved.

Amedi H.R.,Petroleum University of Technology of Iran | Baghban A.,Islamic Azad University at Fasā | Ahmadi M.A.,Petroleum University of Technology of Iran | Ahmadi M.A.,EOR IOR Research Institute
Journal of Molecular Liquids | Year: 2016

Substituting conventional solvents for gas sweetening with ionic liquids (ILs) is an interesting way to specify superior design from energy consumption in regeneration and reduction solvent loss. In this study, based on the critical temperature (Tc), critical pressure (Pc), and molecular weight (Mw) of pure ionic liquids, a feed forward Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Radial Basis Function Artificial Neural Network (RBF-ANN) were developed to predict solubility of Hydrogen Sulfide in the presence of various ILs over wide ranges of temperature, pressure and concentration. To develop the aforementioned methods, 664 experimental data points collected from the literatures were employed. Moreover, to investigate the Hydrogen Sulfide solubility in ternary mixture containing Carbon Dioxide, Hydrogen Sulfide and ILs, MLP-ANN model was proposed. To propose MLP-ANN method for estimating H2S solubility in ternary mixture, 89 experimental data points collected from the previous published works were employed. To examine the ability of the methods suggested in this study different statistical criteria including R-Squared (R2), Mean Squared Error (MSE), Standard Deviation (STD) and Mean Absolute Relative Error (MARE) were used. The values of R2 and MSE achieved for the MLP-ANN model are 0.9951 and 0.000117 respectively. Furthermore, the values of R2 and MSE for both ANFIS and RBF-ANN methods obtained 0.901, 0.002268 and 0.9679, 0.000787 respectively. In addition, R2 and MSE of the MLP-ANN model for ternary mixtures are 0.9955 and 0.000082 correspondingly. Therefore, the ability and acceptable performance of using the MLP-ANN as an accurate model for estimating Hydrogen Sulfide solubility in ILs was showed versus other computational intelligence models. © 2016 Elsevier B.V. All rights reserved.

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