Drilling process abnormity early warning model based on dynamic principal component analysis

Abstract

The invention discloses a drilling process abnormity early warning model based on dynamic principal component analysis. The method comprises the steps of obtaining original data;, performing standardized preprocessing on the original data; forming an augmented matrix according to the standardized and preprocessed original data; forming an initial model according to a dynamic principal component analysis method and the augmented matrix, using the initial model to monitor abnormal data; if the detected data is normal, updating the initial modelaccording to the moving window principle, and if thedetected data is abnormal, analyzing and judging the fault cause according to the residual contribution rate. According to the technical scheme provided by the invention, the accuracy of abnormity detection is improved, and the early warning time delay is reduced, so that the problem of low abnormity early warning precision in the drilling process in the prior art is solved, and the effective early warning of abnormity in the drilling process is realized. Moreover, according to the technical scheme provided by the invention, real dynamic detection is realized, the method has adaptability, andthe abnormal detection effect is improved.

Description

technical field [0001] The invention relates to the field of drilling technology, in particular to an abnormal early warning model of drilling process based on dynamic principal component analysis. Background technique [0002] Petroleum is not only the "blood of industry", but also a very important raw material related to various fields such as life, technology and economy, and is one of the most important energy sources in our country. Drilling technology is one of the main technologies in the process of oil exploration and oil field development. With the rapid development of industrial automation, the degree of continuity and automation of drilling equipment is getting higher and higher. However, there are huge safety risks in the drilling process. If the abnormality cannot be detected in time, once a fault occurs, the equipment will be damaged, the production process will be forced to be interrupted, and catastrophic consequences such as huge economic losses or casualtie...

AI Technical Summary

Problems solved by technology

[0003] The existing technology has proposed many methods and models for process monitoring and abnormal early warning, among which data-driven methods mainly include Principal Component Analysis (PCA), Independent Principal Component Analysis (ICA), Partial Least Squares (PLS) and Fee Scheer's criterion method (FDA) and so on, but these methods have the problems of a large number of false positives and false alarms and long delays in the early warning of drilling process abnormalities

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