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Porosity and permeability are two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. These properties have a significant impact on p...etroleum fields operations and reservoir management. Up to now, more than twenty reservoirs have been found in basement rocks all over the world, which were known as un-usual reservoir. They were named un-usual reservoir because of the small number are compared with clastic and carbonate reservoirs. Study on basement reservoir always is difficult task, especially estimation of reservoir properties due to complex nature of the geological model. In this paper, we suggest an efficient method to determine reservoir properties from well log by using fuzzy logic and neural networks. The ranking technique based on fuzzy logic is used for noise rejection of training data for neural networks. By learning the nonlinear relationship between selected well logs and core measurements, the neural network can perform a nonlinear transformation to predict porosity or permeability with high accuracy. The approach is demonstrated with an application to the well data in A2-VD prospect, Southern offshore Vietnam. The results show that this technique can make more accurate and reliable reservoir properties estimation than conventional computing methods. The study plays an important role in projects of development of basement reservoirs in the future.続きを見る
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