Progressive Cavity Pump, Exploratory Data Analysis, Data Visualization, Fault Detection. 

The progressive cavity pumping system (PCP) is considered an efficient artificial lift technology in terms of energy consumption, in addition to being very versatile in terms of the type of material it can extract. It can be used to extract heavy oils, liquids with a high sediment content, and to extract oil with a certain fraction of gas. Since it is a lifting system that is designed for consistent pumping with minimal wear on components, the equipment's conditions are monitored via sensors installed at the head and bottom of the well. In some cases, the diagnosis of a possible well failure is made based on visual analysis by the operator. In this sense, the objective of the project is to propose an approach based on scientific analysis and visualization of the system's operating data so that it is possible to improve the performance and diagnosis of operating failures of wells with a progressive cavity pumping lift system. The project obtained real data on the parameters that characterize the operating conditions of the wells under study, in addition to including data related to the reservoir's production in the set of attributes. To characterize the behavior of a system with so many attributes, data analysis and visualization techniques were used in conjunction with association rules. This approach allows visualizing and relating multidimensional data from an operation, in addition to facilitating understanding of the behavior of attributes when the system operates with some type of failure, as well as during normal operation. The results obtained indicate that the proposed approach allowed visually identifying patterns of operating modes of this type of system, enabling failure detection and performance analysis with their trends.