Correlation Mining of Hidden Hazards in Drilling Based on Support Matrix Apriori Algorithm

doi:10.11885/j.issn.1674-5086.2020.04.20.02

Abstract

Abstract: It is very important to use data mining technology to study the distribution rule and inherent mechanism of hidden trouble in drilling operation. Aiming at frequent itemsets loss of complex hidden danger data and low generation efficiency, an Apriori algorithm based on support matrix is proposed. First, we introduce a boolean matrix in the transaction database to prevent repeated database scanning. Secondly, the support matrix is constructed by multiplying the transaction matrix to obtain support and simplify the calculation method of support. Finally, the connection strategy of the algorithm is optimized, which simplifies the generation process of frequent itemsets, and continuously reduces the matrix structure in the calculation process. Experiments on UCI datasets show that the improved Apriori algorithm can effectively improve the efficiency of execution. This algorithm is applied to the associated mining of historical drilling hazard data, the mining results can provide reasonable basis for safety managers, identify effectively hidden dangers and risk control, which is of great significance and worth of popularization and application.

Key words: data mining, hidden danger of drilling, Apriori algorithm, association rule, support matrix

CLC Number:

TE28

WANG Bing, HUANG Dan, LI Wenjing. Correlation Mining of Hidden Hazards in Drilling Based on Support Matrix Apriori Algorithm[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2022, 44(2): 113-122.

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URL: http://journal15.magtechjournal.com/Jwk_xnzk/EN/10.11885/j.issn.1674-5086.2020.04.20.02

http://journal15.magtechjournal.com/Jwk_xnzk/EN/Y2022/V44/I2/113

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