Unsupervised Learning Based Lithology Classification from Well Log Data: A Primary Focus on Self-Organizing Map

Min Jun Kim; Juan Lee; Yongchae Cho; Hyunggu Jun

doi:10.7582/GGE.2025.28.2.055

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2025 Vol.28, Issue 2 Next Page

Research Article

Unsupervised Learning Based Lithology Classification from Well Log Data: A Primary Focus on Self-Organizing Map 비지도 학습을 이용한 물리검층 자료부터의 암상 분류: 자기조직화 지도 알고리즘 중점으로

31 May 2025. pp. 55-63

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Abstract

Well logging is the process of obtaining information regarding the subsurface through boreholes. It measures properties such as density, porosity, fluid saturation etc, which are useful in identifying and classifying the various lithologies within the subsurface. Lithology classification and identification are crucial for reservoir characterization and oil and gas exploration. However, conventional methods, such as core sampling, are time consuming and expensive. In this research, a method of classifying lithology from well log data is developed using an unsupervised machine learning algorithm, self-organizing map (SOM). Various input features are considered to train the model, and lithology classification predictions are made and compared with pre-existing lithology data to evaluate the prediction accuracy. To minimize the impact of hyperparameters, we employ an ensemble approach by constructing the SOM 100 model. This proposed method aims to reduce the uncertainty associated with a single model and enhance the reliability of lithology classification prediction.

Keywords

Well Logging

Self-Organizing Map

Unsupervised Learning

Classification

Lithology

물리검층은 시추공을 통해 지중에 대한 정보를 얻는 과정을 의미한다. 밀도, 공극률, 유체포화도 등과 같은 특성들을 측정하고, 이는 특히 지중에 존재하는 다양한 암상을 식별하고 분류하는데 있어 매우 유용하다. 암상 분류는 저류층 특성화 및 석유나 가스 탐사에 매우 중요한 역할을 한다. 하지만 코어 샘플링 등과 같은 전통적인 분류 방법은 시간과 비용이 많이 소요된다. 본 연구에서는 머신러닝 알고리즘을 활용하여 물리검층 자료로부터 암상을 분류하는 방법을 연구하였다. 이를 위해 자기조직화 지도(Self-Organizing Map, SOM)라는 인공신경망 알고리즘을 적용하였으며, 비지도 학습 방식으로 진행하였다. 다양한 입력 변수를 고려하여 모델을 학습시켰으며, 이를 통해 암상 분류를 예측한 후 사전에 주어진 암상 자료와 비교하여 예측 정확도를 평가하였다. 또한, 하이퍼파라미터의 영향을 최소화하기 위해 앙상블 기법을 활용하여 SOM 100 모델을 구성하였다. 이 방법을 통해 단일 모델이 가지는 불확실성을 줄이고, 보다 신뢰도 높은 암상 분류 결과를 도출하고자 하였다.

키워드

물리검층

자기조직화 지도

비지도 학습

분류

암상

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Information

Publisher :Korean Society of Earth and Exploration Geophysicists
Publisher(Ko) :한국지구물리물리탐사학회
Journal Title :Geophysics and Geophysical Exploration
Journal Title(Ko) :지구물리와 물리탐사
Volume : 28
No :2
Pages :55-63
Received Date : 2025-02-05
Revised Date : 2025-03-05
Accepted Date : 2025-05-19
DOI :https://doi.org/10.7582/GGE.2025.28.2.055

Geophysics and Geophysical Exploration ISSN:1229-1064(Print) 2384-051X(Online) 지구물리와 물리탐사

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