Oil Reservoir Properties Estimation by Fuzzy-Neural Networks
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概要
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Porosity and permeability are two fundamental reservoir propertieswhich relate to the amount of fluid contained in a reservoir and itsability to flow. These properties have a significant impact on petroleumfields operations and reservoir management. Up to now, more thantwenty reservoirs have been found in basement rocks all over the world,which were known as un-usual reservoir. They were named un-usualreservoir because of the small number are compared with clastic andcarbonate reservoirs. Study on basement reservoir always is difficulttask, especially estimation of reservoir properties due to complex natureof the geological model. In this paper, we suggest an efficient method todetermine reservoir properties from well log by using fuzzy logic andneural networks. The ranking technique based on fuzzy logic is used fornoise rejection of training data for neural networks. By learning thenonlinear relationship between selected well logs and coremeasurements, the neural network can perform a nonlineartransformation to predict porosity or permeability with high accuracy.The approach is demonstrated with an application to the well data inA2-VD prospect, Southern offshore Vietnam. The results show that thistechnique can make more accurate and reliable reservoir propertiesestimation than conventional computing methods. The study plays animportant role in projects of development of basement reservoirs in thefuture.
- 九州大学大学院工学研究院の論文
- 2007-09-20
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