Image Logging Geological Interpretation System

As a new generation of logging technology, image logging shows us electronic image todisplay the sidewall or the strata of well information directly. Image logging plays animportant role in the interpretation and valuation of complex oil and gas reservoirs. With theincreasing development of image logging application in the interpretation and valuation ofgeology, the research of image logging has received great attention. This paper studied andimplemented key technologies in the image logging interpretation system.The main research achievements and innovative points in this paper are as follows:Firstly, this paper discussed the LIS data file format of image logging on basis ofelectronic image logging data file and established the class library framework of the parse andstorage adopted the objected-oriented analytical method and the database-based storage modelby analyzing the physical and logical structure of LIS format.Secondly, the main factors that affect image quality are analyzed. Some methods, such asthe acceleration correction, image data equalization processing and bad electrode removed,are adopted to eliminate the image compression, image stretching, image misalignment,image aliasing, image loss and image unbalance and enhance the image quality. The goodeffects of the FMI imaging is obtained by the color code defined in this paper.Thirdly, this paper emphatically researched on the anisotropic filtering based-on thepartial differential equations to realize the enhancement of the logging image on the basis ofthe tensor diffusion-based anisotropic filtering. The method achieved good results because theprocess combined strength image gray with the spatial orientation characteristics to detectimage and reduced the effect of the image blurring and edge degradation which produced inthe traditional isotropic enhancement algorithm in de-noising.Finally, this paper focused on the crack identification method based on cellular automataand achieved automatic identification of cracks in the logging image by customizing thereasonable transformation equation, according to the characteristics of the logging image that the texture of the logging imaging is complex and it is full of random noise, and advantages ofthe cellular automata that has homogeneity and parallelism. The method obtained a betterresults as well as higher recognition efficiency compared with the automatic thresholdsegmentation method and the fuzzy c-means algorithm (FCM) method.The imaging logging interpretation system is established based on Visual C++2010platform and MFC foundation class library and completed the function modules such as datainterpretation and storage, data correction and mapping, image enhancement, imagesegmentation and so on. The system can be used in manager FMI electronic image loggingdata management, FMI imaging obtained and geological interpretation has certain researchsignificance and practical value.