Optimization of Heavy Oil Well Rotation Timing Based on Long Short-Term Memory Neural Network Algorithm
Based on the production characteristics of steam huff and puff in heavy oil wells and the historical data of steam injection and production in Shengli Oilfield,a sample library is constructed according to the classification of liquid volume and water content.A long short-term memory network(LSTM)algorithm is used to establish a prediction model for the timing of heavy oil well rotation,and the model is continuously trained and optimized to predict the production indicators of heavy oil wells in the next 90 days.This achieves multi-dimensional intelligent prediction of heavy oil well steam injection rotation,improves the accuracy of heavy oil well production prediction,the accuracy of the best timing prediction for heavy oil steam injection rotation,and the efficiency of the preparation of the best measures for heavy oil steam injection rotation.It enhances the intelligent decision-making,analysis,and management capabilities of heavy oil wells,and improves the development level of oil production management areas.
heavy oil rotationrotation timing predictioncycle parameter prediction
万方数据
维普
