Time-Series Forecasting of a CO 2 -EOR and CO 2 Storage Project Using a Data-Driven Approach

Utomo Pratama Iskandar and Masanori Kurihara
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Utomo Pratama Iskandar: Department of Earth Sciences, Resources and Environmental Engineering, Graduate School of Creative Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
Masanori Kurihara: Department of Earth Sciences, Resources and Environmental Engineering, Graduate School of Creative Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan

Energies, 2022, vol. 15, issue 13, 1-22

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 CO 2 -enhanced oil recovery (EOR) and CO 2 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.

Keywords: CO 2 -EOR; CO 2 storage; data-driven; time series forecasting/prediction; AR; MLP; LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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