A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet

It is challenging to predict the production performance of unconventional reservoirs because of the sediment heterogeneity, intricate flow channels, and complex fluid phase behavior. The traditional oil production prediction methods (e.g., decline curve analysis and reservoir simulation modeling for...

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Vydáno v:Computers & geosciences Ročník 164; s. 105126
Hlavní autoři: Ning, Yanrui, Kazemi, Hossein, Tahmasebi, Pejman
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2022
Témata:
ARIMA
artificial intelligence
basins
LSTM
Machine learning
neural networks
Oil production prediction
oils
prediction
sediments
time series analysis
Time series forecasting
Time series forecasting
Oil production prediction
LSTM
ARIMA
Machine learning
ISSN:0098-3004
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Shrnutí:It is challenging to predict the production performance of unconventional reservoirs because of the sediment heterogeneity, intricate flow channels, and complex fluid phase behavior. The traditional oil production prediction methods (e.g., decline curve analysis and reservoir simulation modeling forecasting) are subjective. This paper presents a machine learning-based time series forecasting method, which considers the existing data as time series and extracts the salient characteristics of historical data to predict values of a future time sequence. We used time series forecasting because of the historical fluctuations in production well and reservoir operations. Three algorithms were studied and compared to address the limitations of traditional production forecasting: Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM) network, and Prophet. This study starts with the representative oil production data from a well located in an unconventional reservoir in the Denver-Julesburg (DJ) Basin. 70% of the data was used for model training, whereas the remaining 30% of data was used to evaluate the performance of the above-mentioned methods. Then, the decline curve analysis and reservoir simulation modeling forecasting were applied for comparison. The advantages of the machine-learning models include a simple workflow, no prior assumption about the reservoir type, fast prediction, and reliable performance prediction for a typical fluctuating declining curve. More importantly, the ‘Prophet’ model captures production fluctuation caused by winter impact, which can attract the operator's attention and prevent potential failures. This has rarely been explored and discussed by previous studies. The application of ARIMA, LSTM, and Prophet methods to 65 wells in the DJ Basin show that ARIMA and LSTM perform better than Prophet—probably because not all oil production data include seasonal influences. Furthermore, the wells in the nearby pads can be studied using the same parameter values in ARIMA and LSTM for predicting oil prediction in a transferred learning framework. Specifically, we observed that ARIMA is robust in predicting the oil production rate of wells across the DJ Basin. •ARIMA and LSTM models perform better than Prophet in oil rate prediction.•Prophet model captures production fluctuation caused by seasonality.•R-squared is not an appropriate metric to evaluate non-linear regression models.•The oil rate of wells in nearby pads can be studied using the same parameter values in ARIMA and LSTM.•ARIMA is robust in predicting the oil rate of wells across the unconventional reservoirs.
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ISSN:0098-3004
DOI:10.1016/j.cageo.2022.105126