A novel target-adaptive frequent pattern growth algorithm for oil well failure analysis

Oil well failures can not only lead to significant economic losses but also present serious safety risks. By identifying and intervening in failure patterns, the occurrence of failures can be minimized. This paper analyzes 1522 failure records collected over three years, identifying 17 causes, including corrosion, aging, and scaling. We propose a novel target-adaptive frequent pattern growth algorithm (TAFP-Growth), comprising two core components: target frequent pattern generation and adaptive rule mining. Initially, the algorithm constructs a target-tree to identify frequent patterns. It designates the target as the rule's consequent and assigns other items as the antecedent, which helps determine the relationships among frequent patterns. By employing adaptive thresholds, the algorithm effectively addresses the difficulties of mining arising from sample imbalance. When applied to oil well failure records, this algorithm significantly speeds up the rule mining process and reduces memory consumption. Out of 143,554 mined rules, 60,997 were related to corrosion failures, while only 6 were related to operation failures. Compared to traditional methods, the TAFP-Growth algorithm demonstrates higher efficiency and reliability in processing industrial data, providing a fast and reliable solution for analyzing failure patterns. This method provides significant support for improving management and decision-making in the oil industry.