Optimized long short-term memory (LSTM) network for performance prediction in unconventional reservoirs

Abstract

Unconventional resources play an increasingly important role in the global energy supply. Performance prediction is crucial for adjusting development methods in unconventional reservoirs to ensure high production. However, daily production prediction of tight gas wells relies on many factors and complex variation patterns exist, which makes it difficult to predict using conventional methods. Therefore, this study proposes a hyper-parameters optimized long short-term memory (LSTM) network to effectively forecast daily gas production. Bayesian optimization is adopted to optimize the essential configuration of the LSTM. The model is trained by five features (wellhead tubing pressure, wellhead casing pressure, reservoir temperature, water production and gas rate) for seventy-five tight gas wells in the Ordos Basin, China, and is then validated on a test dataset with six wells in the same region. The average mean square error (MSE) of the test dataset is as low as 0.006, which indicates high prediction accuracy. Compared with cases with different input features, the five-feature case provides the most stable result for time-series performance prediction. We then comprehensively evaluate the daily gas production prediction of the LSTM network, including verifying it with the auto-regression integrated moving average (ARIMA), the artificial neural network (ANN), and the standard recurrent neural network (RNN). The results demonstrate that the optimized LSTM network can handle sequential data with time-series dependencies to improve the accuracy of performance prediction.