Predicting Gas Well Production Based on CNN-SLTM Algorithm
The prediction of gas well production is of great significance to evaluating production capacity of gas well and estab-lishing reasonable drainage and production system.The method of yield prediction based on empirical model has great limitations in use conditions and environment.In this paper,a fusion algorithm based on CNN and LSTM is proposed to predict gas well produc-tion from the perspective of data.The CNN algorithm is used to extract spatial features of data,the LSTM algorithm is used to extract time features of data.Meanwhile,the relationship between gas well production and production parameters is analyzed based on the mechanism model,and the characteristic parameters are preprocessed to improve the accuracy of the algorithm.Extensive experi-mental results are presented to show that compared with the traditional CNN algorithm and LSTM algorithm,the performance of the proposed algorithm achieves better prediction performance on the data,and the error between the actual daily production and the predicted daily production is less than 5%.
forecast gas well productionbig data analysisrecurrent neural networklong and short term memory neural network
万方数据
维普
