Papers by Author: Yun Liang Yu

Abstract: Studing the identification methods of sedimentary microfacies by the implement of self-organizing neural network model. Picking up the geometrical characteristic parameters and image characteristic parameters of the logging curves. Establishing the relationship of sedimentary microfacies patterns and well logging curves shapes by characteristic parameters. Developing the Sedimentary microfacies patterns identification system and applying it to 1000 wells of the southern area of Daqing Changyuan, the recognition rate can reach 90%, it can prove the validity of the method.

Abstract: Choose a wavelet to transform the logging curves, which can obtain a series of scale and depth corresponding to wavelet coefficients. From the view of fourier series expansion and spectrum energy, the wavelet coefficients of optimal scale should be the largest proportion of total spectral energy. Based on the theory above, method of average modulus based on wavelet transform is put forwarded. Find the optimal scale factor through wavelet transforming of logging curves, and establish a correspondence between oscillations characteristic of wavelet coefficients curve of the optimal scale factor and sequence stratigraphy boundaries. This method provides a new way to recognize sequence stratigraphy boundaries.

Abstract: The mutation and form of logging curve can be represented by the variation of modulus maxima coefficients of wavelet transform within different scales exactly, then we can use wavelet multiscale edge detection theory to analyze characteristics of sequence stratigraphy boundaries in logging curves. Obtain the modulus maximum of approximation coefficient matrix and detail coefficient matrix after decompositing GR and SP curve in every scales.Compare and amend the modulus maximum of approximation coefficient matrix and detail coefficient matrix reciprocally, applicate the Mallat alternation foldover algorithm to reconstruction logging curve eventually ,we can get the fusion curves in different scales. The fusion curves can greatly enhance characteristics of sequence stratigraphy boundaries in logging curves.