A New Laboratory SCAL Device and Processing Method for OBM Vug Density Quantification

Abstract

Vugular porosity, per this work is defined as a set of secondary porosity development in carbonate rocks that could have developed through alteration of rock-fabric, commonly by proceses such as dolomization and dissolution (including mouldic porosity). It is one of the important secondary porosity types and its characterization can influence rock model petro-physical behavior. Traditionaly vugular porosity can be quantified on water-based mud (WBM) high resolution electrical imaging logs by counting the image pixels that has electrical resistivity lower than the rock background. With the popularity of non-conductive drilling fluid (OBM) and improvement of electrical image logging technology, it is possible nowadays to acquire similar high resolution electrical images in OBM environment. Vugs can be identified visually on these new types of electrical images. However open vugs filled with OBM can appear resistive on those images and invalidate the basic assumptions of secondary porosity quantification software that open vugs should have lower electrical resistivity. At the same time, the new generation of electrical imaging tools uses megahertz logging frequencies to decrease the capacitance of the OBM to acquire formation images. With this frequency range, both electrical conductivity and dielectric permittivity impact the image log acquired. This challenges our understanding of the tool response in terms of image color contrast and opens the question of how to integrate such OBM electrical images into current geologic interpretation software that commonly uses the image resistivity value thresholds. In this case, a new laboratory device that features the same logging frequency has been developed and presented in this paper. The device was used to investigate images of secondary porosity features acquired in OBM in controlled laboratory conditions. It is demonstrated that by integrating both resistivity image and dielectric permittivity image, it is possible to overcome the OBM filled resistive vug limitation and account for all fluid-filled effective vugs. A new post processing method is introduced by combining the effects of resistivity and dielectric permittivity and generating a new image called the rock Hayman factor image. On the Hayman factor image, it is possible to differentiate fluid-filled vugs from cemented vugs. Vug density quantification in WBM is based on resistivity threshold values to characterize conductive and resistive heterogeneity patches and can be highly user dependent. In OBM environment, mud filled features will appear resistive and may decrease the contrast with rock resistive matrix. This can make current vug quantification software miss parts of open vugs. Two popular vug separation and quantification methods (Gray-scale reconstruction transform plus watershed transform method and Sobel gradient edge detection plus Otsu's image thresholding method) are investigated first on a vuggy limestone outcrop and then on a downhole log as a case study to derive a vug density quantification workflow. The new Hayman factor image has proven on the laboratory data its ability to quantify both vugs beneath the rock surface and on the rock surface filled with OBM. Integrating the Hayman factor image, the procedure to set heterogeneity patches can be well-defined and not rely on user settings. The two image class separation methods can give very similar vug density quantified. Best practices of parameter adjustments on these two image classification methods are provided that can achieve the best results based on both laboratory data and downhole data. In this paper investigation results including both laboratory measurements and downhole logging data analysis of the OBM electrical image vug density quantification are presented. A new post-processing Hayman factor image is designed to quantify rock resistivity and the dielectric permittivity relationship and help vug density quantification. Application of this new image and best practices of how to set processing parameters using this image are investigated and summarized. Using the new vug characterization workflow involving Hayman image can improve OBM well vug quantification accuracy and repeatability.