| Gas pipeline early warning is an important basis for oilfield production decision department to adjust production operation plan.Due to the complexity of pipeline operation data,the large amount of data,and the limited level of hardware and software technology,gas pipeline early warning is subject to false alarms or missed alarms,there are false alarms or missed alarms for gas pipeline warning.So more and more researchers at home and abroad have started research on gas pipeline early warning methods with the aim of improving the accuracy of pipeline early warning.At present,the main method of gas pipeline early warning is to infer and screen the future development trend of pipeline operation data by field experts based on manual experience,which can avoid the occurrence of faults to a certain extent,but the inaccuracy of the inferred data change trend,incomplete coverage of early warning knowledge and inaccurate knowledge description make the gas pipeline early warning low in accuracy and poor in efficiency.Therefore,constructing an intelligent early warning model for gas pipelines,designing an intelligent early warning system for gas pipeline operation,inferring the state of future pipeline operation data in a timely and accurate manner,and achieving the purpose of early warning have become the urgent needs in the current gas pipeline early warning work.To solve the above problems,this paper presents an in-depth analysis of the gas pipeline early warning business process,proposes a dynamic intelligent early warning method for gas pipeline operation and designs a gas pipeline intelligent early warning model,and analyzes and implements the model components.Firstly,in-depth analysis of gas pipeline early warning standards,processes and difficulties,design of the general framework of the gas pipeline intelligent early warning model,clear research focus on knowledge-based reasoning for early warning methods and dynamic early warning based on prediction,research on data models related to early warning.Secondly,the early warning knowledge is analyzed,and then the knowledge base is built on this basis,and the early warning inference machine is designed based on the combination of rules and case reasoning.In addition,the extended isolated forest algorithm is adopted to realize the knowledge self-updating content when the knowledge in the knowledge base cannot be automatically updated.Thirdly,missing value filling,anomaly processing and normalization processing were carried out for the collected pipeline operation data,and then prediction variables and related influence variables were selected to provide high-quality data set for training the prediction model.Then,a comprehensive analysis method is designed for the early warning results of gas pipelines.The characteristics of pipeline operation data are taken as the basis for the selection of algorithm set,and the future change trend of pipeline operation data is predicted by Ar MI-Lst M model,SA-LSTM model,CNN-LSTM model and BO-CNN-GLSTM model respectively.Combined with the knowledge reasoning method,the predicted value state was judged respectively,and the early warning results were obtained.Then the quantitative analysis method based on reliability and qualitative method based on repetition were used to analyze the early warning results comprehensively.Finally,on the premise of gas pipeline early warning business,combined with the early warning model to design and implement the gas pipeline intelligent early warning system,and through practical application results to verify the effectiveness of the early warning method proposed in this paper.Finally,on the premise of gas pipeline early warning business,combined with the early warning model to design and implement the gas pipeline intelligent early warning system,and through practical application results to verify the effectiveness of the early warning method proposed in this paper.The application results show that the intelligent early warning system of gas pipeline can better solve the current problem of gas pipeline early warning,the system can accurately early warning,greatly improve the work efficiency,can effectively reduce the failure caused by the hidden danger detection is not timely,and can ensure the safe operation of the pipeline. |
