The paper presented a new method for prediction of inter well parameters interpolation by using neural network technique based on microfacies study.

Inversion of multi-logging parameters under constrain of relative wave impedance data volume and application.;

In the past, the prediction of the reservoir production capacity by logging parameters was based on a single testing well.

The causes producing logging parameters and containing water of the low resistance reservoir are analyzed.

The signature recognition of comprehensive logging parameters in sedimentary microfacies of clastic reservoirs.;

The optimum DSM logging parameters are summarized through a large number of logging practices in order to conlribute to the application and popularization.

, the authors expound that, as a method of nonlinear inversion, Seimpar Inversion could carry out wave impedance inversion without using convolution equation, and also inverse logging parameter by seismic records under the logging constrained.

Application of log parameter quantification technology to interpretation of the physical properties of the Putaohua reservoir in the Yushulin oilfield

By comparing the prediction results of GM(1,1) and BP neural network, the combination of GM and neural model is feasible in oil spectral analysis parameter prediction, which can overcome the deficiency of single model and get good effect.

Parameter prediction models (PPM) and parameter recovery models (PRM) were developed based on the three Weibull distribution parameters and stand attributes.

The system can realize such functions as parameter prediction binaural auralization and model visualization and therefore it can help the acoustic consultants to know a virtual sound field from not only vision, but also acoustic indexes and hearing which will lead to the best design plan.