Condition-Based Maintenance of Oilfield Cement Pumper: A Data-Driven Approach

Abstract

Abstract Cement-pumping equipment is used to perform pressure-pumping services, which are vital in the oil well construction process. This equipment has a complicated design that involves the integration of several original equipment manufacturer (OEM) components. Not only are they expensive to manufacture, but also because they operate under stressful conditions, they incur high maintenance costs. The efficient and timely condition-based maintenance of these assets are critical to increase equipment life and availability, to mitigate any major repair expenditures and to prevent costly nonproductive time. Until recently, we have been following a traditional scheduled maintenance process and lacked a data-driven process and methods to monitor the equipment and perform condition-based maintenance. To address this, we analyzed the historical acquisition data from this equipment and developed prognostics health management (PHM) models that diagnose the performance degradation of the critical components and alert us to perform condition-based maintenance. In this paper we describe the method and application of the four PHM models we developed and implemented in 2023. The first three distinct models monitor the performance of three different centrifugal pumps designated for distinct functionalities within our system. The fourth model monitors the performance of the radiator that dissipates heat from the coolant that regulates the temperature of lubrication systems for the engine and transmission. All four models were tested on historic data and successfully deployed to identify deviation from healthy operating zones in the production jobs. The results are promising, given the models have identified 22 defects since deployment. The Dataiku platform was used for data processing, analysis, and algorithm development for models. The first three models were developed using a polynomial regression method along the root mean square error (RMSE) metric. The fourth model was developed using a curated dataset to delineate the zone of interest and to define the thresholds for detecting deviations. The results of the PHM models were visualized on interactive dashboards, statistically significant outliers are analyzed in real time, and used to alert the operations and maintenance teams in the field.