Time-Series Forecasting of a CO2-EOR and CO2 Storage Project Using a Data-Driven Approach

Utomo Pratama Iskandar*, Masanori Kurihara


研究成果: Article査読

3 被引用数 (Scopus)


This study aims to develop a predictive and reliable data-driven model for forecasting the fluid production (oil, gas, and water) of existing wells and future infill wells for CO2-enhanced oil recovery (EOR) and CO2 storage projects. Several models were investigated, such as auto-regressive (AR), multilayer perceptron (MLP), and long short-term memory (LSTM) networks. The models were trained based on static and dynamic parameters and daily fluid production while considering the inverse distance of neighboring wells. The developed models were evaluated using walk-forward validation and compared based on the quality metrics, span, and variation in the forecasting horizon. The AR model demonstrates a convincing generalization performance across various time series datasets with a long but varied forecasting horizon across eight wells. The LSTM model has a shorter forecasting horizon but strong generalizability and robustness in forecasting horizon consistency. MLP has the shortest and most varied forecasting horizon compared to the other models. The LSTM model exhibits promising performance in forecasting the fluid production of future infill wells when the model is developed from an existing well with similar features to an infill well. This study offers an alternative to the physics-driven model when traditional modeling is costly and laborious.

出版ステータスPublished - 2022 7月 1

ASJC Scopus subject areas

  • 制御と最適化
  • エネルギー(その他)
  • 工学(その他)
  • エネルギー工学および電力技術
  • 電子工学および電気工学
  • 燃料技術
  • 再生可能エネルギー、持続可能性、環境


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