Recognition Of Concealed Targets Based On Gray Intelligence

In this paper, some scientific research item of oil field are used as the background. And it is used as object that Economizing cost of production and Increasing productivity. We have studied a lot of work for recognising oil&gas reservoir. At present ,Man have some unclear and indeterminacy cognition about underground oil reservoir, it make waste a lot of money every year. So it is very important that man realize a good method for recognising oil&gas reservoir clearly. It can heighten production efficiency and save a lot of money for country.Using logging method to explore the oil&gas reservoir is an indispensable method in oil&gas exploration.To ascertain the depth and characters of the reservoir,get logging signals which correlated with stratum characters from different depths underground is needed.Those signals reflect the characters of stratum in different sides.In order to recognise the reservoir from different stratums and to analyze and appraise the oil&gas character of the reservior,the oil&gas explorers need to dispose,analyze and appraise the logging information with the seism and the rock-core information based on having deeply analyzed the oil geology in the logging area.The oil&gas reservoir recogition and character analyses are a quite complicated system project. Construct the gray pattern recognition system which is of the intention of oil&gas reservoir recognition to recognise the oil&gas reservoir intelligently.After studying gray correlation analyse method and BP neural network, Grey Cascade BP neural network model is proposed to recognise oil&gas reservoir,we achieve satisfactory results. The main contents are as follows:1. Study gray slide double wavelets model,introduce its model and algorithm, After recognise complicated oil&gas reservoir with one-dimensional gray slide double wavelets model and two-dimensional gray slide double wavelets model,the results show its efectiveness and practical value.2.Study Grey Cascade BP neural network model.reservoir and anti-reservior are recognised by the first training,oil reservior,gas reservior and water reservior are recognised by the second training.we select training sample with gray correlation alasyse method and distance method.3.We use Grey Cascade BP neural network model to recognise complicated oil&gas information,and find oil reservior gas reservior and water reservior from logging information. 4. Gray slide double wavelets models and grey cascade BP neural network model are developed in visual C++6.0.