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

Pratama Iskandar Utomo*, Masanori Kurihara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number4768
JournalEnergies
Volume15
Issue number13
DOIs
Publication statusPublished - 2022 Jul 1

Keywords

  • AR
  • CO storage
  • CO-EOR
  • data-driven
  • LSTM
  • MLP
  • time series forecasting/prediction

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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