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Kaydani H.,Shahid Bahonar University of Kerman | Najafzadeh M.,Kerman Graduate University of Technology | Hajizadeh A.,Amirkabir University of Technology | Hajizadeh A.,National Iranian South Oilfield Company
Journal of Natural Gas Science and Engineering | Year: 2014

Miscible gas injection is one of the most efficient enhanced oil recovery (EOR) methods in petroleum industry. Minimum miscibility pressure (MMP) is a key parameter in any gas injection design project. Experimental Measurement of MMP is a costly and time-consuming method; so searching for a quick, not expensive and reliable method to determine gas-oil MMP is inevitable. This paper Present a fast and vigorous method using a new approach based on multi-gene genetic programming (MGGP) to determine carbon dioxide minimum miscibility pressure (CO2 MMP) for carbon dioxide injection processes. Then, new correlations for MMP calculation of both pure and impure CO2 streams using the MGGP, have been developed. Consequently, the MGGP models have been validated and compared with the other conventional model results, to evaluate different techniques. It was founded that the new developed correlations predict accurate values of CO2 MMP compare with the experimental slim-tube CO2 MMP test results, with the lowest average relative and absolute error and also higher correlation coefficient among all evaluated CO2 MMP correlation results. © 2014 Elsevier B.V.


Kaydani H.,Shahid Bahonar University of Kerman | Hagizadeh A.,National Iranian South Oilfield Company | Mohebbi A.,Shahid Bahonar University of Kerman
Petroleum Science and Technology | Year: 2013

Dew point pressure (DPP) is one of the most important parameters to characterize gas condensate reservoirs. Experimental determination of DPP in a window pressure-volume-temperature cell is often difficult especially in case of lean retrograde gas condensate. Therefore, searching for fast and robust algorithms for determination of DPP is usually needed. This paper presents a new approach based on artificial neural network (ANN) to determine DPP. The back-propagation learning algorithms were used in the network as the best approach. Then equations for DPP prediction by using weights of the network were generated. With the obtained correlation, the user may use such results without a running the ANN software. Consequently, this new model is compared with results obtained using other conventional models to make evaluation among different techniques. The results show that the neural model can be applied effectively and afford high accuracy and dependability for DPP forecasting for the wide range of gas properties and reservoir temperatures. © 2013 Taylor & Francis Group, LLC.


Kaydani H.,Shahid Bahonar University of Kerman | Mohebbi A.,Shahid Bahonar University of Kerman | Mohebbi A.,University of Adelaide | Hajizadeh A.,National Iranian South Oilfield Company | Dakhelpour J.,National Iranian South Oilfield Company
Energy Sources, Part A: Recovery, Utilization and Environmental Effects | Year: 2014

Minimum miscibility pressure is a fundamental parameter in miscible injection projects. Experimental determination of minimum miscibility pressure is very costly and time-consuming; therefore, attempts have been made to utilize artificial neural networks for determination of minimum miscibility pressure. Despite the wide range of applications and flexibility of artificial neural networks, design and structural optimization of neural networks is still strongly dependent upon the designer's experience. To mitigate this problem, this article presents a new approach based on a hybrid neural genetic algorithm to determine the minimum miscibility pressure for pure and impure carbon dioxide injections. Then, equations for minimum miscibility pressure prediction by using the optimize weights of network have been generated. With the formulas obtained, the user may use such results without running the artificial neural network software. The new model yielded the accurate prediction of the experimental slim-tube carbon dioxide minimum miscibility pressure with the lowest relative mean squared error and average absolute errors among all tested carbon dioxide minimum miscibility pressure correlations. © Taylor & Francis.

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