A Study on the Quantitative Processing Methods of Imag Data
Authors: Qi Xingzhong , Zhao Jun , Xiao Chengwen , Liu Ruilin 
Published: December 2007

Abstract
In order to draw the quantitative information from FMI data a important step is to segment images of FMI. That means that first of all the sub-images which can reflect cavities and cracks separated from the images of FMI, and then the sub-images are processed to extract the parameters. In this paper, it has been thought that several algorithms including strangeness point multi-threshold algorithm, algorithm based on transition region, hopfield network algorithm and threshold algorithm based on fuzzy scattering degree among images were effective to separate the useful targets such as cavities and cracks from background (basement rock) by studying a lot of image segmentation algorithms. Meanwhile, in order to acquire the shape parameters of cavity and crack the method of tracking the outline of cavities and cracks to mark the edge and the stuffing algorithm according to the marked edge are studied too. On the based of these study a method to calculate the target's length, width, density of crack and density of cavities is put forward. All these method have already been transformed into programs on the GeoFrame platform in SUN workstation. By comparing the processed results of the eight wells of Tarim oilfield with the core analytical data it indicates a good effect.

Introduction
The quantitative calculation of parameters in cavity and crack reservoirs is always an unsolved problem in well logging circle. In recent years FMI has been put out to contribute to resolve the problem because it could not only offer a lot of audio-visual information for qualitative explanation but also extract some parameters to evaluate reservoirs quantitatively. At present it is in the stage of studying for image processing and parameter abstraction. Fracview and spot models of Schlumberger corporation were presented. They can get density, width, crack rate, facial porosity of cavity and distribution of porous diameter on the base of artifical picking up images, but the amout of work is very large because of artifical operation for some reservoirs in which cavities and cracks are well-developed and results are different by different operators. In order to reduce man-made factor as fully as possible a image segmentation technology is used to separate automatically useful targets (cavities and cracks) from background (basement rock). After a small amount of interaction processing some false appearances are removed, and then some appropriate methods are adopted to process the sub-images segmented and to calculate parameters.
In this paper it has been studied on various threshold segmentation algorithms based on grey scale histogram, including P-tile algorithm, Ridle algorithm, strangeness point multi-thresholds segmentation algorithm, multi-threshold segmentation algorithm based on entropy, segmentation algorithm based on transition region, hophield network algorithm, threshold algorithm based on fuzzy scattering degree among images and image edge detection algorithm, to separate pores, cavities and cracks from background.
In order to obtain the characteristic parameters of the shapes of cavities and cracks for quantitative analysis the sub-images must be further processed. The method of tracking the outline of cavities and cracks to mark the edges and the stuffing algorithm according to the marked edges are studied. On the base of these study a method to calculate the target's length, width, circularity, density of crack and density of cavity is put forward. All of them have been applied to practice.