Detection of Borehole Breakout Depth in Image Logs Using Machine Learning Algorithms

Juyeol Yeom; Hayoung Kim; Kyungbook Lee; Chandong Chang; Yeonguk Jo

doi:10.32390/ksmer.2023.60.4.223

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2023 Vol.60, Issue 4 Preview Page Next Page

Research Paper

Detection of Borehole Breakout Depth in Image Logs Using Machine Learning Algorithms 머신러닝을 이용한 이미지로그의 압축파쇄대 깊이 검출모델 개발

31 August 2023. pp. 223-230

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Abstract

This study developed a classification model for the detection of borehole breakout depths in image logs using machine learning algorithms. Approximately 1 km of image log data acquired in Gyeongju was preprocessed to make it suitable for applying machine learning algorithms. This included removing missing values and data within casing intervals and aligning the resolution among image log data — that is, vertical and horizontal resolutions were set at 0.01 m and 2.5°, respectively. Decision tree-based algorithms, random forest, and XGBoost were then applied to the preprocessed 99,090 data entries, which was divided into training (80%) and testing (20%) groups. The trained models were evaluated based on the false negative (FN) of the confusion matrix. The XGBoost model showed a superior predictive performance with 87 FNs when compared to the random forest model’s 162 FNs. The developed model can enable experts to perform detailed reviews of detected depths via the model.

Keywords

breakout

image log

machine learning

confusion matrix

본 연구는 머신러닝을 이용해 이미지로그의 압축파쇄대 깊이를 검출하는 분류모델을 개발하여 전문가 분석효율을 높이는 것이 목적이다. 경주지역 내 시추공에서 취득된 약 1 km 이미지로그에 대해 케이싱 구간 및 결측치를 제거하고 이미지로그 자료간 해상도(수직 및 수평 해상도가 각각 0.01 m와 2.5°)를 일치시키는 전처리과정을 수행하였다. 이를 통해 얻어진 총 99,090개 자료를 8:2 비율로 학습과 테스트 자료로 설정한 후, 의사결정나무 기반의 랜덤포레스트와 XGBoost를 적용하였다. 모델평가는 테스트자료에 대한 혼동행렬의 false negative(FN)를 통해 이뤄졌으며, XGBoost 모델은 FN이 87개로 랜덤포레스트 모델의 FN 162개에 비해 향상된 예측성능을 보였다. 이렇게 개발된 모델은 전문가의 압축파쇄대 상세 검출을 도와 분석 효율성을 제고할 수 있을 것으로 기대된다.

키워드

압축파쇄대

이미지로그

머신러닝

혼동행렬

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Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 60
No :4
Pages :223-230
Received Date : 2023-08-07
Revised Date : 2023-08-21
Accepted Date : 2023-08-28
DOI :https://doi.org/10.32390/ksmer.2023.60.4.223

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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