The Design And Implementation Of The Electric Image Logging Interpreation Software

Electrical imaging logging technology is a new technology which aims to measure those physical information, such as sound and electricity, about the borehole and present those information in the digital diagram form. The technology is widely used in fields like geological exploration, and oil and gas exploration. Image logging interpretation system is the most important part of the Electrical imaging logging technology. It is used to develop different kinds of images of different geological information by the analyzing and processing of the acquired data. It can also process those images to provide deciding information for those geological exploration staff. It plays an important role in the explanation of the precision and accuracy of geological explanation.this paper designed and realized the image logging interpretation system through the analysis of the whole system of the image logging interpretation system and the major parts of it, including data analysis, image development, and crack recognition by taking the electronic image logging data as the research data,The major parts of this research is as follows1. This paper first reviewed the studies about.....and pointed out the existing problems in these researches. Then the image logging interpretation system was analyzed by using UML based on the importance of image logging data and the process of image logging data. Finally, the framework and function module of image logging interpretation system is developed based on the Visual Studio 2010 and MFC.2. this paper realized the precession, storage and management of LIS data of Electrical imaging log by the analysis of the physical structure and logical stricture of LIS data through the object-oriented method and the Database management model.3. the process of the data of imaging log has been done and the quality of the process of the data has been improved by using technology like the image data equalization processing、acceleration correction and bad electrode removed. Images of different type of better quality have been got by using the combination of the method of color calibration, different color spectrum, and the bilinear difference of data4. The Histogram of the imaging log has been modified and the noise has been eliminated by the using of digital image process technology. The crack has been recognized by people by using curve fitting. The crack has been showed out automatically according to the intensity configured based on the different intensity thresholds with the intensity slicing algorithm.