Micro-resistivity Image Logging Processing And Interpretation Methods Research

Electrical imaging log with the feature of high resolution and intuitive image has been widely used in the terms of complex reservoir exploration and heterogeneous reservoir evaluation, while the electrical imaging logging data and image processing technology influenced the accuracy and precision of the geological interpretation. Meanwhile the research in these regions-the establishment of imaging template library for typical geological phenomena and automatically matching identification, automatically quantitative calculation of geological parameters, identification the formation cycle through electrical imaging log-have been increasingly paid attention with the further application of electrical imaging log in geological interpretation.This paper firstly takes the data measured by ERMI as the research object, studying the preprocessing correction as well as the image generation and display method: focusing on improved Kalman filter model-acceleration correction based on stuck identification, eliminating the image compression tension and sawtooth originating from the complex movement of the tools; firstly considerring the equalization process on the electrical imaging log data from the plates and the gap between them, better getting rid of the vertical strips and black bands on the images; for the large range of the ERMI data, putting forward the best probability distribution function, based on Gauss distribution, enhancing the histogram of the image, effectively solving the phenomenon of bright and dark bands on the images and finally generating new images which can compare to the effects fo the Schlumberger results.Based on these, studying the methods to identify pore, cave and fracture from the electrical imaging data and quantitatively calculate the relevant geological parameters: In the field of automatical identification of pore space, making use of Maximum Interclass Variance to automatically segmenting the images and then automatically picking up the pore space profile by Freeman 8-chain storage which can increase the speed and the accuracy of the calculation. In the field of identification of fracture, introducing the method combinating the improved Hough transform with Human Computer Interaction to identify the fracture and calculate the fracture parameters. In order to provide reference and guid for the geological interpretation, building the interpretation library linking the typical imaging features to the geological phenomena which can improve the efficiency and reliablity of the electrical imaging log interpretation through automatically giving the highest similarity template by using Bp neural network identification method; Meanwhile, proposing Black-Turkey, MC-CLEAN signal analysis method to deal with the electrical imaging log data to identify formation cycle by spectrum analysis, which can provide more detailed stratigraphic cycles than the use of conventional logging data, obtain the results consistent with the Milankovitch cycle and offer a new approach and ideas for the analysis of high resolution sequence stratigraphy; Finally independently developing a set of electrical imaging processing and interpretation software in the platform of .NET successfully attached to the logging processing and interpretation platform in China Ocean Oilfield Services company and achieving the good results by processing and verifying the measured data on-site and comparing the results with these of domestic and foreign similar commercial software so that it can be applied in oilfields.