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

Petroleum Exploration and Development 2020年第5期47卷 1084-1090页

作者： WANG Hongliang MU Longxin SHI Fugeng DOU Hongen PetroChina Research Institute of Petroleum Exploration&Development Beijing 100083China

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 *** 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 *** 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 *** 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 *** LSTM based on deep learning has higher precision,and gives more accurate production prediction for complex time series in oil field *** LSTM model was used to predict the monthly oil production of another two oil *** prediction results are good,which verifies the versatility of the method.

关键词： production prediction ultra-high water cut machine learning Long Short-Term Memory artificial intelligence

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