S Tabasi; A KamkarRouhani; M.M Khorasani
Abstract
Archie’s equation, which is the most fundamental equation for water saturation calculation,consists of three factors: Cementation factor (m), saturation exponent (n) and tortuosity (a). Cementation factor is a function of the shape of pores. Hence, the study of pore type is important in determining ...
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Archie’s equation, which is the most fundamental equation for water saturation calculation,consists of three factors: Cementation factor (m), saturation exponent (n) and tortuosity (a). Cementation factor is a function of the shape of pores. Hence, the study of pore type is important in determining the Archie’s coefficients. In order to achieve more precise and reliable results in Archie’s coefficient determination and then water saturation accurately, the rocks must be rated based on texture and porosity type, where the coefficients should be constrained for each class. In this paper, fractal method is used to rate the resistivity log data and calculate Archie’s coefficient in an exploration well of a hydrocarbon reservoir in southwest of Iran. The results show three different zones based on porosity type and texture of the rocks. Then the Genetic algorithm method is used to calculate the Archie’s coefficients in each of the zones separately. The results show that this method is able to consider the complex behavior of each of the coefficients in the calculations.
M Amighpey; B Vosooghi; M Motagh
Abstract
An earthquake with the magnitude of 5.9 ML shocked the Qeshm island located in the Persian Gulf on 27 November, 2005 at 13:53:22 local time. The earthquake occurred due to the reactivation of a NE-SW fault with a major reverse mechanism accompanied by a minor strike-slip component. Another earthquake ...
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An earthquake with the magnitude of 5.9 ML shocked the Qeshm island located in the Persian Gulf on 27 November, 2005 at 13:53:22 local time. The earthquake occurred due to the reactivation of a NE-SW fault with a major reverse mechanism accompanied by a minor strike-slip component. Another earthquake of 5.5 MW occurred on the same day at ca. 20:00 local time. The interesting feature of this earthquake is that the calculated mechanism for its strongest aftershock, which occurred ca. 6 hours after the main-shock, was a strike-slip mechanism that is completely different from the pure reverse mechanism for the main-shock. This study uses inversion of InSAR observation of earth surface displacement field boundary values to solve parameters of these 2 earthquakes. The results show activation of southern part of the Qeshm fault caused by the first earthquake along 7 km of its length. This event induced the second earthquake by activation of another strike-slip fault which is parallel to the Gavarzin anticline. Estimated slip was 96 cm for the first earthquake and 9 cm for the second one. Based on the estimated parameters of the these 2 earthquakes, the maximum displacement induced by the first earthquake was 6.7 cm in west, 4.6 cm in south and 16.4 cm in vertical directions on the earth surface. The maximum displacement of the second earthquake in west, south and vertical directions were 1.3, 1.6 and 1.4 cm respectively.
A. Kamkar Rouhani; M. Zakeri
Abstract
In order to obtain more accurate results from application of the method of artificial neural networks, instead of selection of the best network determined by trial and error process, we suitably combine the results of several networks that is called committee machine, to reduce the error, and thus, increasing ...
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In order to obtain more accurate results from application of the method of artificial neural networks, instead of selection of the best network determined by trial and error process, we suitably combine the results of several networks that is called committee machine, to reduce the error, and thus, increasing the accuracy of the output results. In this research, ensemble combination of single artificial neural networks has been used in order to estimate the effective porosity of Kangan gas reservoir rock in South Pars hydrocarbon field. To achieve this goal, well logging data of 4 wells in the area at the depth interval corresponding to Kangan formation were used. Acoustic, density, gamma ray and neutron porosity well log data were assigned as the input of the networks while the effective porosity data were considered as the output of the networks. Back- propagation single neural networks having different structures were trained using regularization method and their results were assessed. Then, the networks with the best results, i.e. contained minimum mean of squares of errors in the test step, were selected for making ensemble combinations. To determine the weighting coefficients of the networks in the linear ensemble combinations, we applied three methods of simple averaging, Hashem’s optimal linear combination and non-analytical optimal linear combination employing genetic algorithm, and their results were compared. The best ensemble combination, in which we had the maximum reduction in mean of squares of errors of the test step compared to the best single neural network, was an optimal linear four-network combination obtained by using genetic algorithm optimization method. This best ensemble combination, compared to the best single neural network, reduced the mean of squares of errors in the training and test steps 3.6% and 11.2%, respectively.
