Production prediction at ultra-high water cut stage via Recurrent Neural Network

Abstract

A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network (FCNN) is incapable of preserving the correlation of time series data, the Long Short-Term Memory (LSTM) network, which is a kind of Recurrent Neural Network (RNN), was utilized to establish a model for oil field production prediction. By this model, oil field production can be predicted from the relationship between oil production index and its influencing factors and the trend and correlation of oil production over time. Production data of a medium and high permeability sandstone oilfield in China developed by water flooding was used to predict its production at ultra-high water cut stage, and the results were compared with the results from the traditional FCNN and water drive characteristic curves. The LSTM based on deep learning has higher precision, and gives more accurate production prediction for complex time series in oil field production. The LSTM model was used to predict the monthly oil production of another two oil fields. The prediction results are good, which verifies the versatility of the method.

Keywords

• production prediction
• ultra-high water cut
• machine learning
• Long Short-Term Memory
• artificial intelligence