Abstract
The main factor contributing to the heterogeneous nature of carbonate reservoir is patchiness due to areas of different porosity and permeability caused by diagenetic processes or change in litho-facies. For instance, it could be imagined as a mixture of grainstone and packstone; grainstone areas could be less permeable due to cementation of the pore space and packestone areas could be more permeable. Similarly, in bioclastic limestone, shell fragments are resistive and dense (appear as resistive spots or patches) while the leached parts of the same shells could be conductive / porous (appear as conductive spots or patches). Including textural information in porosity and permeability, calculations in carbonates are critical to build accurate understanding for reservoir productivity
Different approaches have been proposed over the years to utilize the high-resolution images quantitatively to extract some reservoir parameters, however, all the approached have been focused either on extracting textural information or fracture information or porosity information separately. Delhomme introduced a method to analyze the image texture by delineating conductive and resistive heterogeneities. This method characterizes the geometry and the electrical properties of each of the heterogeneous features as well as their connectedness. However, it did not provide direct inputs to the image-based porosity methods such as the method proposed by Newberry, Grace & Stief. Fractures-related porosity been largely based on the aperture estimation method proposed by Luthi & Souhaite.
The new workflow merges texture analysis, image porosity analysis, and fracture extraction to describe and quantify the full permeability distribution from electrical borehole images. With this workflow, it is possible to extract and classify the different types of pore space: connected / isolated vugs, pores connected to fractures, aligned at bed boundaries, or within the rock matrix. The contribution of these different pore types to the formation permeability is quantified; also, the geometrical information (size, surface proportion, contrast) of heterogeneities is calculated. The connectedness log describes the quantity of connected spots detected from the electrical borehole image and is used as a predictive measure for identifying zones of higher or lower permeability. Electrically conductive features such as stylolites or clay chips can be extracted and ignored in the porosity calculation.
Based on gap-filled borehole image a sliding window with a given overlap is used to calculate the permeability along the wellbore. Each sliding window is transformed into a normalized permeability region. A Darcy experiment is then simulated using finite element method to calculate vertical and horizontal normalized permeabilities using different sets of boundary conditions. The focus in this invention on how it could be extended the workflow to all types of borehole images to extract valuable information about porosity and connectivity that can be used as image permeability in carbonate reservoirs based on machine learning.
