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2024 Vol.61, Issue 2 Preview Page

Research Paper

Analysis of the Shale Gas Production Prediction Model Based on Machine Learning Using Productivity Factors

생산성 영향인자를 활용한 기계학습 기반 셰일가스 생산량 예측모델 분석

Da-Yeong Lee1
,
Hyo-Jin Shin2
,
Jong-Se Lim2*
이 다영1
,
신 효진2
,
임 종세2*
1Department of Ocean Energy & Resources Engineering, National Korea Maritime & Ocean University, Busan, Korea
2Department of Energy & Resources Engineering, National Korea Maritime & Ocean University, Busan, Korea
1한국해양대학교 해양에너지자원공학과
2한국해양대학교 에너지자원공학과
*Corresponding Author
30 April 2024. pp. 111-123
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Abstract

Owing to the uncertainties caused by complex flow mechanisms in shale gas reservoirs, production prediction using machine learning has recently been performed. However, efficiently collecting and analyzing production-related factors is difficult. In this study, the productivity factors of different criteria were identified using feature selection, and gas production was predicted using the selected factors in various machine learning models. The error rate improved when productivity factors such as total fluid, measured depth, and lower perforation based on the filter method were applied to the random forest model. In addition, the potential for extending the proposed feature selection process and machine learning algorithms in Marcellus Shale was identified, and a reliable production prediction was expected to be possible by utilizing limited variables even at the sites where production information was difficult to obtain.

Keywords
shale gas
feature selection
productivity factor
machine learning
production prediction

셰일가스 저류층에서의 복잡한 유동 메커니즘으로 불확실성이 존재하여 최근 기계학습을 활용한 생산량 예측을 수행하고 있으나, 생산관련인자를 효율적으로 수집하고 분석하는 데는 어려움이 있다. 이 연구에서는 특징 선택을 활용하여 서로 다른 기준의 생산성 영향인자를 파악하고, 이를 다양한 기계학습 알고리즘에 적용하여 Barnett Shale의 가스 생산량을 예측하였다. 이에 Filter 기법 기반의 총 주입유체량, 측정 깊이, 하부 천공 위치 등 생산성 영향인자를 random forest 모델에 적용할 경우 오차율을 개선할 수 있음을 확인하였다. 이와 더불어 Marcellus Shale에서 제안된 특징 선택 과정 및 기계학습 알고리즘의 확장 가능성을 파악하였으며, 이 연구를 통해 생산 정보 취득이 어려운 경우에도 제한된 변수를 활용하여 신뢰성 있는 생산량 예측이 가능할 것으로 사료된다.

키워드
셰일가스
특징 선택
생산성 영향인자
기계학습
생산량 예측
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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 : 61
  • No :2
  • Pages :111-123
  • Received Date : 2023-12-07
  • Revised Date : 2024-02-28
  • Accepted Date : 2024-03-26
  • DOI :https://doi.org/10.32390/ksmer.2024.61.2.111
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