The forecasting of gas production from mature gas wells, due to their complex end-of-life behaviour, is challenging and often associated with uncertainties (both measurements and modelling uncertainties). Yet, having good forecasts are crucial for operational decision making. In this paper, we present a purely black-box based approach, which combines the use of a data assimilation method, the Ensemble Kalman Filter (EnKF) and a modified deep LSTM model as the prediction model within the approach. This approach is tested on two mature gas wells in the North Sea which were suffering from salt precipitation. Results showed that the approach of combining a deep LSTM model within EnKF can be effective when deployed in a real-time production optimization environment. We observed that having the EnKF increases the robustness of the forecasts by the black box prediction model while reducing computational cost of retraining the black-box models for every individual well.


Article metrics loading...

Loading full text...

Full text loading...


  1. Evensen, G.
    [2003] The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367.
    [Google Scholar]
  2. Sak, H., Senior, A. W., and Beaufays, F.
    [2014] Long short-term memory recurrent neural network architectures for large scale acoustic modeling. INTERSPEECH.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error