Deep Learning and Data Assimilation for Real-Time Production Prediction in Natural Gas Wells
This work addresses operational decision-making for natural gas production, but it is incremental as it combines existing methods (LSTM and EnKF) for a specific domain application.
The paper tackles the challenge of predicting gas production from mature wells with complex end-of-life behavior by applying a modified deep LSTM model and using the Ensemble Kalman Filter (EnKF) to update predictions based on new observations, resulting in better Jeffreys' J-divergences with the EnKF scheme.
The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters. Additionally, due to changes in the system in time and in order to increase the accuracy and robustness of the prediction, the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions based on new observations. The developed approach was tested on the data from two mature gas production wells in which their production is highly dynamic and suffering from salt deposition. The results show that the flow predictions using the EnKF updated model leads to better Jeffreys' J-divergences than the predictions without the EnKF model updating scheme.