| Formation porosity is an important parameter in oil-gas storage of reservoir, and it is very significant to formation evaluation with exact measuring data of porosity. In order to determine formation acoustic porosity accurately and provide logging interpretation with the evaluation and prediction of oil-gas storage, the neural computation method superior to that of traditional is studied, and it has been of important application value to determine acoustic porosity in acoustic logging. The main works of this dissertation are as follows:(1) Traditional methods to determine acoustic porosity are analyzed in detail, and the limitation of traditional methods is pointed out. The nonlinear relation between slowness acquired from acoustic logging and formation porosity is described, and it is a modeling problem of nonlinear-system to determine formation porosity with acoustic slowness. For the neural computation technique is very adapted to nonlinear-system modeling, so it is feasible to determine formation porosity by used of neural computation.(2) Four idiographic models, such as the neural network based on BP algorithm, neural network based on LM algorithm, RBF neural network and support vector machines (SVM), are discussed. The simulation result shows the predictive ability of RBF neural network and SVM is strong.(3) The practical application is carried out by used of the logging data in typical oil well, the main steps includes, selection and pre-processing of the sample set( contains acoustic porosity, core porosity, etc), selection of hidden-layer nodes in BP neural network, design of model parameters in RBF neural network and SVM, prediction of acoustic porosity for un-recognized formation, and so on. The application result shows the neural computation method to determine formation porosity is superior to traditional methods, and the predictive ability of RBF neural network and SVM is strong, especially that the prediction accuracy of SVM is highest.
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