Abstract
Remote monitoring of drilling safety risks is an important part of oil drilling engineering. Accurate prediction of drilling risks can provide a reliable guarantee for safe drilling operations. Although the conventional collapse stuck risks monitoring technology is based on sensor detection, the risk assessment method is still based on the qualitative analysis of manual experience and lacks theoretical support. In order to solve this problem, based on the engineering parameters of the remote sensor network, this paper analyzes the characteristics of the collapse stuck, and establishes the collapse stuck risks evaluation model of the fuzzy dynamic cluster algorithm to optimize Fuzzy C-means clustering algorithm. The result of fuzzy dynamic cluster is tested by F statistics, and the result is used as the initial condition of the Fuzzy C-means clustering algorithm, and the method of iterative optimization is used to calculate the final classification result. The concept of nearness degree is introduced to calculate the nearness degree of prior unknown data to cluster center vector. According to the principle of maximum nearness degree, the risk level of prior unknown data is classified, so as to realize the risk prediction and evaluation of the collapse stuck accident. It provides theoretical support for remote monitoring and risk assessment of collapse stuck of oil drilling engineering.
Published Version
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