A Productivity Prediction Method for Tight Gas Wells Based on Knowledge Graph and Random Forest Algorithm
The productivity prediction of gas well is influenced by various factors such as geology and engineering.Traditional methods like mathematical analysis and numerical simulation struggle to quickly and accurately predict the productivity of tight gas wells.To address this issue,an innovative method combining knowledge graph and the random forest algorithm is proposed based on big data and machine learning concepts to develop a productivity pre-diction method of tight gas wells.Data preprocessing standardizes different types of basic data,and entity recognition and linking technologies integrate entities from various data sources into the knowledge graph.Relationship extrac-tion and modeling techniques are used to establish relationships and attributes among entities,developing a complete knowledge graph for accurate productivity prediction.On this basis,a productivity prediction model for tight gas wells is developed using the random forest machine learning algorithm,and the model predicts the productivity of tight gas wells in the Qiulin Block with an accuracy of 89.7％.This method allows for rapid and accurate productivi-ty predictions in the early stages of development,significantly improving prediction accuracy and providing decision support for productivity deployment and high-yield well cultivation in tight gas development.
万方数据
维普
