Efficient Analysis And Prediction Of Gas Reservoir Development Dynamics Based On Deep Learning Algorithm

Dynamic analysis of gas reservoir development is the key to final recovery factor estimation and gas reservoir production planning,and has an important impact on gas field development planning and economic evaluation.The most important work is production decline analysis.At present,the methods of production decline analysis mainly include numerical simulation method and decline curve method.Numerical simulation methods require large time and labor costs.At present,most of the decreasing curve methods used in the field are based on certain assumptions,and the applicable conditions are harsh and have certain limitations.The development of deep learning provides a new method of sequence data research for dynamic analysis of gas reservoir development.In this paper,according to the time series characteristics of gas well production data,three deep learning algorithms are selected to establish prediction models,and a deep learning single well prediction model is established for each gas well.Secondly,the model hyperparameters are divided into key hyperparameters and secondary hyperparameters by qualitative analysis.Through quantitative analysis of the model hyperparameters,the optimal values of the secondary hyperparameters of the model are determined,and then the optimal value range of the key hyperparameters of the model is determined.A particle swarm automatic optimization model hyperparameter method is established,and the key hyperparameters of the model are finely optimized and adjusted to improve the prediction effect of the deep learning model.Third,the existing empirical equations of oil and gas production prediction are added to the neural network training process as a constraint in the neural network training,so as to improve the interpretability and prediction ability of the long short-term memory neural network model.Finally,according to the single well production performance analysis and prediction method studied in this paper,the software and interface are designed to improve the analysis and prediction efficiency of this method.Through the research on the dynamic analysis model developed by deep learning,the following understandings and achievements have been obtained in this paper:(1)Among the single-well production performance prediction models established by the three deep learning algorithms,the model suitable for single-well production performance analysis is the long-short-term memory neural network model.(2)The particle swarm automatic optimization model hyperparameter method established in this paper greatly simplifies the optimization process of neural network model in an automatic way,and improves the prediction effect.(3)The empirical equation of oil and gas production prediction is added to the neural network as a constraint,which improves the interpretability and prediction ability of the long short-term memory neural network model.(4)The single-well production performance analysis and prediction software established in this study can efficiently use the model to predict future production performance and facilitate the application and popularization of this research method.