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

The progressive cavity pumping system (PCP) is considered one of the most efficient artificial lifting technologies in terms of energy consumption, in addition to being very versatile in relation for the type of material it can extract, and can be used for oil extraction. heavy, liquids with a high sediment content and in the extraction of oil with a certain fraction of gas, being more used in shallow wells. Monitoring of equipment conditions is done via surface sensors. 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 this article is to propose an innovative approach based on scientific data analysis and visualization for monitoring the performance and diagnosing operational failures of wells with a progressive cavity pumping system (PCP). In this research, real data on electrical and mechanical variables that characterize the operational condition of the wells under study were obtained. The set of attributes also includes data relating to the reservoir’s production. To allow the characterization of the system’s behavior with so many attributes, visualization techniques such as radar graphs and heatmaps were used. This approach allows you to visualize and relate multidimensional data related to the BCP operation, in addition to facilitating understanding related to the behavior of attributes when the system operates with some type of failure, as well as in its normal operation. The results obtained indicate that the proposed approach made it possible to visually identify patterns of operating modes of this type of system, enabling fault detection and performance analysis with their trends. Thus, it can be concluded that the use of scientific data visualization techniques can greatly contribute to the efficient monitoring of performance and mode of operation of pumping systems using progressive cavities.