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Congjun Feng,Hua Yang,Renhai Pu,Yaning Wang,Daxing Wang,Xiaowei Liang,Mengbo Zhang,Yougen Huang,Shixiang Fei 한국지질과학협의회 2017 Geosciences Journal Vol.21 No.2
Using core, well logging, geological analysis, production, and test data, this study characterizes a method of well logging for identifying lithology, oil or water layers, and thickness of oil layers of tight sandstone reservoir in the Chang 7 member of the Upper Triassic Yanchang Formation, southwestern Ordos Basin, China. This reservoir consists of two rock types: fine sandstone and siltstone. The fine sandstone has distinct oil traces and flecks, which strongly indicate the presence of oil, and this rock is therefore the superior reservoir. The siltstone exhibits essentially no oil shows and thus is an unproductive layer. A method of density and neutron curve normalization and overlay was used to identify the tight fine sandstone, tight siltstone, mudstone, and shale. The criteria for identifying the tight fine sandstone are a natural gamma value of less than 93 API (American Petroleum Institute units) and a difference between the normalized curves exceeding 0.05. A resistivity-porosity plot was used to identify oil or water layers relatively effectively. The criteria for identifying the tight oil layers are (1) a resistivity exceeding 28 Ω·m and a porosity exceeding 9.5% or (2) a resistivity between 20 and 28 Ω·m, a porosity exceeding 9.5%, and an oil saturation exceeding 65%. Based on the lithologic identification via normalization and overlay of the density and neutron curves and the criteria for distinguishing the tight oil layers, the thickness of the tight fine sandstone oil reservoir was accurately determined by overlaying the normalized difference curve on the resistivity curve.
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A Study of Digit Recognition Algorithm for Meter based on Rough Set and Neural Network
Xiaochen Zhang,Yuanchang Zhong,Jiajia Shen,Kun Li,Congjun Feng 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.8
Due to the low recognition accuracy, the remote meter reading technology based on camera direct reading has been developed slowly. Although there is a variety of features data for recognizing digit in image using BP neural network, some of data cannot be used to recognize digit accurately. Moreover, the BP network has a slow rate of convergence, low accuracy and easily fall into local minimum. To solve the above questions, a new digit recognition algorithm of meter based on rough set and neural network which are optimized by genetic algorithm is proposed. The improved genetic rough set algorithm is used for reducing the data, and then the minimum feature attribute sets after reduction are input to genetic neural network for identifying digit. The experimental results show that the algorithm can effectively reduce the number of decision attributes and simplify the structure of the neural network with high identification accuracy and short training time, which improve the generalization ability and robustness of the neural network.
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