Field Validation of Scalable Condition-Based Maintenance (CBM) of Mud Pumps

Abstract

Although mud pumps are critical rig equipment, their health monitoring currently still relies on human observation. This approach often fails to detect pump damage at an early stage, resulting in non-productive time (NPT) and increased well construction cost when pumps go down unexpectedly and catastrophically. Automated approaches to condition-based maintenance (CBM) of mud pumps to date have failed due to the lack of a generalized solution applicable to any pump type and/or operating conditions. This paper presents a field-validated generally applicable solution to mud pump CBM. Field tests were conducted during drilling operations in West Texas and Japan, to verify the feasibility of the developed pump CBM solution. An accelerometer and acoustic emission (AE) sensor were attached to pump modules, and data was collected during drilling operations. Anomaly detection deep-learning (DL) models were trained during run-time to pinpoint any abnormal behavior by the pump and its elements. The models were trained only with normal state data, and a damage score characterizing the extent of damage to the mud pump was calculated to identify the earliest signs of damage. The system correctly identifies the degradation of the pump and produces alerts to notify the rig crew of the damage level of key mud pump components. During the field tests, different hyper-parameters and features were compared to identify the most effective ones for identifying damage while at the same time delivering low false positive rates (i.e., false alarms during normal state pump operation). The developed CBM system thus provides a generalized solution for pump monitoring, capable of working for different pumps and different operating conditions, and only requires several hours of normal state data with no prior pump data information. This system eliminates the environmental, health and safety (EHS) concerns that can occur during human-based observations of mud pump health, and avoids unnecessary NPT associated with catastrophic pump failures. The final version of this system is expected to be a fully self-contained magnetically attachable box containing sensors and processor, generating simple indicators for recommending pro-active pump maintenance tasks when needed. This is the first successful attempt to validate a universally applicable DL-based CBM system for mud pumps in the field. The system allows more reliable continuous and automated pump monitoring by detecting damage in real-time, thereby enabling timely and pro-active mud pump maintenance and NPT avoidance.

Fault diagnosis of three-cylinder mud pump based on transfer learning

“…Although mud pumps are critical rig equipment, their health monitoring currently still relies on human observation. This approach often fails to detect pump damage at an early stage, resulting in non-productive time (NPT) and increased well construction costs when pumps go down unexpectedly and catastrophically [2]. Therefore, fault diagnosis is of great significance to improve production efficiency and reduce accident rates for complex mechanical systems [3].…”