Extraction and Application of Decline Characteristics Based on Long Short-Term Memory Neural Networks to Improve the Performance of Shale Gas Production Prediction Model

Dae-Mo Lee; Hyo-Jin Shin; Jong-Se Lim

doi:10.32390/ksmer.2023.60.6.504

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2023 Vol.60, Issue 6 Preview Page Next Page

Research Paper

Extraction and Application of Decline Characteristics Based on Long Short-Term Memory Neural Networks to Improve the Performance of Shale Gas Production Prediction Model 셰일가스 생산량 예측 모델의 성능개선을 위한 장단기기억 신경망 기반 생산감퇴특성 추출 및 활용 연구

31 December 2023. pp. 504-515

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Abstract

Various studies have used decline curve analysis (DCA) to predict shale gas production, with recent proposals focusing on artificial intelligence-based machine learning techniques. Future production is generally predicted using production history as input data, but few studies have examined how to improve predictive performance by processing specific data from given data. This study used Long Short-Term Memory (LSTM) neural networks to predict the production of shale gas, and predictive performance was improved by extracting and using decline characteristics to reflect the decline trend of shale gas. Future predictions of shale gas production based on Long Short-Term Memory neural networks can be improved by processing data on decline characteristics from production history and using them as input data.

Keywords

shale gas

long short-term memory neural networks

decline characteristics

predicting production

셰일가스 생산량을 예측하기 위해 생산감퇴곡선분석법을 이용한 다양한 연구가 진행되었으며, 최근에는 인공지능 기반의 머신러닝 기법이 제안되고 있다. 일반적으로 생산이력을 입력자료로 활용하여 미래의 생산량을 예측하는 방식이 적용되고 있지만, 주어진 자료에서 특정한 자료를 가공하여 예측성능을 개선하는 연구는 부족한 실정이다. 이 연구에서는 장단기기억 신경망을 이용하여 셰일가스의 생산량을 예측하였으며, 생산이력으로부터 셰일가스의 감퇴경향을 반영할 수 있도록 생산감퇴특성을 추출한 후 활용하여 예측성능을 개선하였다. 향후 장단기기억 신경망 기반의 셰일가스 생산량 예측 시 생산이력으로부터 생산감퇴특성에 관한 자료를 가공하고 입력자료로 이용함으로써 개선된 결과를 도출할 수 있을 것이다.

키워드

셰일가스

장단기기억 신경망

생산감퇴특성

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 : 60
No :6
Pages :504-515
Received Date : 2023-10-10
Revised Date : 2023-11-16
Accepted Date : 2023-12-27
DOI :https://doi.org/10.32390/ksmer.2023.60.6.504

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

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