Research On Mud Stone And Shale Fractures Information Processing Based On Logging Attributes

Fractured mudstone and shale reservoirs have been found in both China and other countries. Now a lot of scholars pay attention on the fractures in mudstone and shale because it can play a role as a concealed conducting system underground. And it is a very difficult problem to predict and locate the fractured mudstone and shale reservoirs for its strong concealment. Until now there is no mature technology to solve this problem.In recent years, seismic attribute analysis technology has been widely used and developed because of its excellent features that seismic attribute can get rich underground information and extract useful hidden information form the seismic data. Compared with seismic data, logging data have higher resolution and can record the physical information more objectively. So in the paper I use conventional logging curves for the attributes information processing in order to make use of the logging attributes to eliminate the data distortion and extract the hide mudstone and shale fracture information. This information can provide data support for fractures classification and favorable section prediction.Research of this paper is based on the theory of logging attribute analysis, then considering the special case of mudstone and shale fractures I made the determination of which logging curve are sensitive logs to the mudstone and shale fractures based on the conventional logging signal features of fractures combined with the fracture identification cross plot; I made detailed study of basic theory of wavelet transform attributes, frequency spectrum attributes, integral attributes of the logging attributes; calculate wavelet high frequency attribute by using multi-resolution wavelet analysis; calculate the Lipschitz exponent a of a function though measuring the absolute value of its wavelet coefficients at different scales, so that exponent can reflect the singularity attributes of logging signal; make the analysis of frequency spectrum attributes by the Short-time Fourier Transform; study the method which can calculate the reflection coefficient in the prediction error attribute based on the MESE and make the selection of filter order P; then use the energy decay of Stoneley wave and fluid flow indexes that come from array sonic with core analysis data to calibrate hidden fracture information which extracted by logging attribute analysis technology; establishing classification standards of the mudstone and predicting shale fractures development...