Deep learning history matching for real time production forecasting
conference paper
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.
Topics
ForecastingLong short-term memoryMachine learningNatural gas wellsOffshore gas well productionPrecipitation (chemical)Uncertainty analysisComputational costsData assimilation methodsEnsemble Kalman FilterHistory matchingOperational decision makingPrediction modelReal-time productionSalt precipitationDeep learning
TNO Identifier
861889
ISBN
9789462822719
Publisher
European Association of Geoscientists and Engineers EAGE
Source title
1st EAGE/PESGB Workshop on Machine Learning, 29-30 November 2018
Collation
7 p.
Files
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