Hybrid esp failure prediction using fuzzy logic for data improvement and augmentation

Abstract

Systems and methods are described using a trained ML model to monitor, detect failure within, and schedule a remediation procedure (RP) for an operating ESP within a well. ESP status data including a time series comprising ESP input variables representing ESP state are collected from a sensor. Using fuzzy logic, the ESP status data is cleaned to remove abnormal data and used to generate fuzzy logic-based labels, each representing an ESP condition associated with ESP state. The fuzzy logic-based labels are segregated into processed labels used to populate each ML model feature. A selected, trained ML model with improved accuracy for ESP monitoring, failure detection, and RP scheduling for the ESP (based on specific ML model, well, and ESP), accepts the ML model features as input. An ESP failure alert is generated by the ML model based on the ESP status data. The RP is scheduled before ESP catastrophic failure.