Research on Key Equipment Abnormality Detection and Early Warning Technology for Drilling Rigs
Offshore drilling rigs make important contributions to the development of national oil and gas resources,and ensuring the safe operation and maintenance of key equipment on the rigs is a basic requirement for the development of oil and gas resources.Aiming at the problems of in-accurate single-parameter characterization of equipment state and difficulty in threshold determi-nation in the traditional equipment abnormality detection and early warning methods,from the perspective of multi-parameter correlation relationship,the feature extraction method based on random forest was studied,the multi-dimensional health memory matrix based on the concept of similarity clustering was constructed,and the method of determining the multi-level alarm thresholds for the equipment was realized by utilizing the principle of probability graph,and finally,the proposed method was tested by using the simulation data of the mud pumps,which validated that the method's timeliness,accuracy,and leakage rate were better than that of the conventional early warning methods and that it was effective for the identification of the abnor-mality parameters.
drilling riganomaly detectionmultivariate state estimationrandom forest
NETL
NSTL
万方数据
维普
