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Deep Learning History Matching For Real Time Production ForecastingNormal access

Authors: K. Loh, P. Shoeibi Omrani and R. van der Linden
Event name: First EAGE/PESGB Workshop Machine Learning
Session: Latest Developments
Publication date: 30 November 2018
DOI: 10.3997/2214-4609.201803016
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 476.41Kb )
Price: € 20

Summary:
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.


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