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    • List of Articles Abouzar Mohsenipour

      • Open Access Article

        1 - Analysis of Electrical Rock Type Bangestan Reservoir in Marun Oil Field
        Abouzar Mohsenipour Bahman Soleimani Ehsan Abharakpour Ghodratollah   Nikkhah
        Studies of the electrical Rock Type a very important role in the development process plays a field.In these studies, theporo-perm Cores data and well log data used for reservoir simulations. In the present research, the flow of four flow units was determined in the res More
        Studies of the electrical Rock Type a very important role in the development process plays a field.In these studies, theporo-perm Cores data and well log data used for reservoir simulations. In the present research, the flow of four flow units was determined in the reservoir using porosity and permeability data from well logging core by regional index method. In some wells, using well logging the basic model of electrical rocktype was determined with three methods of MRGC, SOM, and DYNAMIC. The determined facies by different methods were correlated with the flow unit. Finally, SOM method was selected, which has the best concordance. The initially created electrofacieswere reduced to 4 electrofacies due to the similarity of some parameters such as effective porosity and gamma logging. To ensure the accuracy of the electrical rock type by neural networks, these electrofacies were correlated with capillary pressure data. After confirming the determined electrofacies by capillary pressure, these facies were propagated in other wells in this field. This created a model, which was able to separate different parts of the reservoir. In this model, different parts of the reservoir were determined in terms of reservoir quality. Manuscript profile
      • Open Access Article

        2 - Permeability estimation using petrophysical logs and artificial intelligence methods: A case study in the Asmari reservoir of Ahvaz oil field
        Abouzar Mohsenipour Bahman Soleimani iman Zahmatkesh Iman  Veisi
        Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calcula More
        Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters. Manuscript profile