Abstract

Predicting EUR in unconventional tight-shale reservoirs with prolonged transient behavior is a challenging task. Most methods used in predicting such long-term behavior have shown certain limitations. However, long short-term memory (LSTM) – an artificial recurrent neural network (RNN) architecture used in deep learning – has proven to be well-suited to classifying, processing, and making predictions based on time series data with lags of unknown duration between important events. This study compares LSTM and reservoir simulation forecasts.

Available unconventional tight-shale reservoir data is analyzed by LSTM and predictions obtained. A reservoir simulation model based on the same data is used to compare the LSTM forecast with results from a physics-based model. In the LSTM forecasting, any operational interferences to the well are taken into account to make sure the machine learning model is not impacted by interferences that do not reflect the actual physics of the production mechanism on the behavior of the well.

The forecasts from the LSTM machine learning model and the physics-based reservoir simulation model are compared. The LSTM model shows a good level of accuracy in predicting long-term unconventional tight-shale reservoir behavior using the physics-based reservoir simulation model as a benchmark. An analysis of the comparison shows that the LSTM machine learning model provides robust predictions with its long-term forecasting capability. This allows for better data-driven forecasting of EUR in unconventional tight-shale reservoirs. A detailed analysis is done using the forecast results from LSTM and the reservoir simulation model, and the key drivers of the EUR response are evaluated and outlined.

Deep learning applications are limited in the oil and gas industry. However, it has key advantages over other conventional machine learning methods; especially where relationships are in time and space and not very clear to the modeler. This study provides a detailed insight into deep learning applications in the oil and gas industry by using LSTM for long-term behavior prediction in unconventional shale reservoirs.

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