The Monitoring of Abnormal Fluid Properties Based on PCA Technique as an Alternative Strategy to Support Autonomous Drilling Operations

Abstract

SummaryThe well drilling process requires constant monitoring to ensure that the properties of the drilling fluids remain within acceptable ranges for safe and effective operation of the well drilling process. The present work developed a principal component analysis (PCA)-based methodology for diagnosing anomalies in drilling fluids, and detecting and identifying abnormal drilling fluid properties during well drilling operations. The main novelty of the present work regards the application of multivariate techniques for diagnosing anomalies (faults) in drilling fluids, increasing the literature on fault diagnosis techniques applied to the petroleum industry, and producing a promising methodology for field applications. The proposed technique was implemented and validated in a pilot drilling fluid production unit through continuous online monitoring of the conductivity, density, and apparent viscosity of drilling fluids. Model training was carried out with data collected during assisted normal operation, allowing detection of abnormal conditions with less than 1% of false positives and less than 0.5% of false negatives. Additionally, the proposed methodology also allowed the correct diagnosis of the observed faults. The results indicated that PCA-based approaches can be used for the online monitoring of drilling fluid properties and fault diagnosis in real well drilling operations.