머신러닝 기반 생산기간에 따른 셰일가스 궁극가채량 예측의 불확실성 추이 분석
Analysis of Uncertainty Trend for Estimated Ultimate Recovery Prediction of Shale Gas with Various Production Periods based on Machine Learning
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초록
셰일저류층의 불균질성, 나노 스케일의 유체투과도 및 유동 메커니즘의 복잡성으로 인해 저류층시뮬레이션과 같은 물리적 모델 기반 분석보다는 머신러닝을 활용한 생산자료 분석법이 활발히 사용되고 있다. 이 연구에서는 랜덤 포레스트 모델을 활용하여 입력자료에 따른 Barnett Shale의 가스생산정에 대한 생산성 분석을 수행하였다. 입력자료는 저류층 물성, 완결조건, 생산정 정보 등의 정적자료와 가스의 초기 최대 생산량, 6개월 및 12개월 월별 생산량 등 동적자료로 구성되었다. 다양한 입력자료에 대한 랜덤 포레스트 모델의 궁극가채량 예측오차를 비교하였으며, 예측의 불확실성분석을 위해 P90/P50/P10을 도출하여 신뢰구간의 변화 추이를 분석한 결과, 랜덤 포레스트 모델의유용성과 활용성을 확인하였다.
Abstract
On account of the heterogeneity of shale reservoirs, their nano-scale permeability, and the complexity of their flow mechanism, production data analysis using machine learning has been widely used rather than physics-based models such as reservoir simulations. In this study, we performed the estimated ultimate recovery (EUR) prediction of gas wells in the Barnett Shale basin, using random forest (RF) models with various input data. Static data such as reservoir properties, completion information, and well information, and dynamic data such as the initial peak rate of gas, and monthly gas rates for 6 and 12 months, were used as the input data. The EUR prediction errors of the RF models for different input data were compared, and P90/P50/P10 was derived to analyze the change in confidence interval for the uncertainty analysis of the prediction. Conclusively, the usefulness and applicability of the RF model for decision-making processes was confirmed.
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