Research On The Model Using RS Theory-Information Entropy-Wavelet Neural Network In The Oil Gas Reservoir Prediction

The oil gas reservoir prediction is a important task in the geophysical exploration domain, the prediction accuracy will directly determine the success or failure of exploration development, so it always is valued by the oil gas exploration development expert. Therefore it is important part to seek a better method for predicting the oil gas reservoir.In recent years, many researchers have proposed some methods for predicting the oil gas reservoir, such as Pattern Recognition, Neural Networks, Fuzzy Pattern Recognition, Gray Process of Genetic Recognition and so on. Indeed, these methods have obtained the certain effect in the actual oil gas exploration development, But there is also some questions such as the effective attribute selection, the network optimization as well as a subordination function determination and so on.At present, the applicable conditions are different that the methods using seismic data to reservoir prediction methods, the prediction results are different, respectively has own advantages and limitations. But no matter what predicting methods, all with relate to a large number of data which was observed and collected, which need to be processed or inducted or classified. Therefore it inevitably involves to the concept like incomplete or inaccurate knowledge and so on. The RS theory is new mathematical tool for dealing with the fuzzy and uncertain knowledge mathematical instrument, only depends on observation data itself to delete the redundant information, withdraws the effective attribute. But the fault-tolerant ability and the promoted ability are relatively weak, and only can process the quantification data, and information entropy can be used as a measure of the attributes importance when the knowledge is classified. So this paper gives a new discretization method. Wavelet Neural Network has the advantages of both of wavelet analysis and neural network, with strong and fault-tolerant ability and the promoted ability as well as greater recognition and ability to function approximation. Therefore, this paper will make full use of three advantages of the RS theory, the information entropy and the wavelet neural network, and set up a new prediction model that mainly aim at the important part in the oil gas reservoir prediction, and analyzes the feasibility and effectiveness of the model as an example of the Aran belt of the Kashan area in Iran.