Automated Image Processing of Petrographic Thin Sections for Digital Reservoir Description: A Bridge to Correlate with Core and NMR Data

Abstract

Carbonates exhibits diverse flow characteristics at pore scale. Petrographic study reveals micro-level heterogeneities. Thin sections are key to assess reservoir quality although these are images and interpretations in text format. Thin section microscopic analysis is descriptive and subjective. To an extent, optical point counting is routinely used quantitatively to estimate porosity, cement, and granular features. Overall, thin section descriptions require specialist human skill and an extensive effort, as it is repetitive and time consuming. Thus, a manual process limits the overall progress of rock quality assessment. There is no recognized method to handle thin sections for direct input with conventional core data due to its image and descriptive nature of data. An automated image processing is one of the emerging concepts designed in this paper to batch process thin sections for digital reservoir descriptions and cross correlating the results with conventional core analysis data. Thin section images are photomicrographs under plane polarized light. Initially, denoise and image enhancement techniques were implemented to preserve elemental boundaries. Computational algorithms mainly, multilevel thresholding and pixel intensity clustering algorithms were programmed to segment images for extracting elements from segmented regions. The extracted elements were compared with original image for labeling. The labeled elements are interpreted for geological elements such as matrix, pores, cement, and other granular content. The interpreted geological elements are then measured for their physical properties like area, equivalent diameter, perimeter, solidity, eccentricity, and entropy. 2D-Porosity, polymodal pore size distribution, mean pore size, cement and granular contents were then derived for each thin section image. The estimated properties were compared with conventional core after calibrating with laboratory NMR data. The whole process is automated in a batch process for a specific reservoir type and computational cost is analyzed for optimization. 2D-porosity is in excellent agreement with core porosity, thus reducing uncertainty that arises from visual estimations. Scale related issues were highlighted between 2D porosity and core porosity for some samples. Polymodal pore size distributions are in good correlation with NMR T2 distribution compared to MICP distributions. The correlation coefficient was understood to be equivalent to surface relaxivity. A digital dataset consisting of 2D porosity, eccentricity, entropy, mean pore size, cement and grain contents is automatically extracted in csv format. The digital dataset, which was previously in text format in conventional analysis, is now a rich quantitative dataset. This paper demonstrated a unique and customized solution to extract digital reservoir descriptions for geoscience applications. This significantly reduced the subjectivity in visual descriptions. The solution presented is scalable to large number of samples with significant reduction in turnaround and effort compared to conventional techniques. Additional merit is that the result from this method has direct correlation to conventional core data for improving rock typing workflows. This paper presents a novel means to use thin section images directly in digital format in geoscience applications.

Predicting and Analyzing Rock Mechanical Properties Using Image Processing Techniques

“…Many traditional methods struggle with the fuzzy boundaries and complex backgrounds in rock microscopic images, leading to inaccurate segmentation results that may affect subsequent predictions of mechanical properties [16][17][18][19]. Furthermore, the extraction of parameters of rock microstructures often lacks effective automation tools, limiting the capability for large-scale data processing and analysis, thereby affecting the construction and optimization of prediction models [20][21][22][23][24].…”