eprintid: 19162
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/91/62
datestamp: 2024-06-04 14:11:36
lastmod: 2024-06-04 14:11:36
status_changed: 2024-06-04 14:05:02
type: article
metadata_visibility: show
creators_name: Naulia, P.S.
creators_name: Roy, A.
creators_name: Watada, J.
creators_name: Aziz, I.A.
title: Clustering Cum Polar Coordinate Feature Transformation (C-PCFT) Approach to Identify Pores in Carbonate Rocks
ispublished: pub
keywords: Carbonates; Carbonation; Computerized tomography; Deep learning; Fracture; Pixels; Proven reserves; Sedimentary rocks; Support vector machines, Cum polar coordinate feature transformation; Deep learning; Deep-CNN; Detectron2; Feature transformations; Gray scale; Hough Transformation; Image-analysis; Images processing; Microfractures; Micropores; Pixel intensities; Polar coordinate; Shape; Support vectors machine; Yolov5; Yolov8, Image enhancement
note: cited By 0
abstract: Most of the world's oil reserves and natural gas are stored within carbonate rock's pores and fractures. Pores and fractures are quite popular for predicting the amount of petroleum under an adequate trap condition. Hence, their petrophysical properties, such as shape and size, are paramount for accurately predicting the reservoir's state and condition. Current modelling techniques are mostly based on manual and expert judgement which are time-consuming and cost-intensive. In this study, we devised a robust and scalable image processing framework that uses the combination of pixel-based clustering approach with a polar coordinate feature transformation technique to intelligently identify the pores of carbonate rock samples. We reported that such a method can be effective in detecting pores of different shapes and sizes in an automated fashion. We rigorously tested the proposed method on the computed tomography-scanned micro-images of a carbonate rock sample, and the results demonstrate improved identification accuracy of the proposed method than the current deep learning counterparts. Another key advantage compared to deep learning methods, the proposed method does not require extensive training on data, which saves time and effort without being computationally too expensive. © 2013 IEEE.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168657396&doi=10.1109%2fACCESS.2023.3308821&partnerID=40&md5=feaaa2787cba36f09f2bcf81446e8395
id_number: 10.1109/ACCESS.2023.3308821
full_text_status: none
publication: IEEE Access
volume: 11
pagerange: 98486-98499
refereed: TRUE
citation:   Naulia, P.S. and Roy, A. and Watada, J. and Aziz, I.A.  (2023) Clustering Cum Polar Coordinate Feature Transformation (C-PCFT) Approach to Identify Pores in Carbonate Rocks.  IEEE Access, 11.  pp. 98486-98499.