Hybrid Approach for Health Monitoring of Mud Motor Fleet

Abstract

A mud motor is a kind of positive displacement motor (PDM) that is used to transform the hydraulic energy of drilling fluids (mud) into mechanical energy. This mechanical energy enables the drill bit to cut the rock and drill a well. It is one of the key parts of downhole assembly that is placed in the drillstring to provide additional power to the bit while drilling as its power downhole output is still unmatched. Mud motor failure is a common and costly issue in drilling operations. A proper prediction of the failure as well as an estimation of the remaining useful life (RUL) are essential for timely downhole mud motor maintenance and drilling optimization. Until now, the oil and gas industry has lacked reliable procedures to monitor and maintain the health of mud motors, resulting in unnecessary maintenance costs as well as unpredictable and costly drilling failures. Recently, Schlumberger has addressed this problem with an industry-first prognostics and health management (PHM) solution, which not only estimates the health of the mud motor and tracks RUL, but also creates a new service for clients and provides a competitive advantage. Timely mud motor retirement and maintenance will ultimately reduce failures and NPT. The proposed PHM solution is suitable for real-time implementation and combines two different sterling algorithms for reliable prediction of possible problems with the mud motors. It enables the estimation of the mud motor health both on the system level with the entire mud motor (system level PHM model) and on the subcomponent level (power section PHM model) – the most critical component of the mud motor. The system-level algorithm model leverages both surface and downhole drilling data as well as mud motor characteristic curves to compute the severity of mud motor degradation. A special mud motor degradation indicator is defined. The indicator is calculated to evaluate the degree of power section decay at each time recorded from thousands of field jobs. The trends of the degradation with respect to drilling time and drilling distance are extracted for each motor job. Based on the study of large datasets, good correlation was observed between the mud motor degradation indicator and mud motor failures. The power section PHM model uses downhole measurements to estimate the RUL of the elastomer – the life-limiting component inside the power section. It is based on a high-fidelity model and uses a hybrid approach by combining a high-fidelity physics-based model of a power section and data-driven approaches with machine learning techniques. Machine learning methods were applied to derive a reduced order surrogate model (ROM) of power sections from the original physics-based models for real-time applications. This ROM outputs the estimation of performance and fatigue characteristics of the considered power section depending on the considered drilling conditions such as differential pressure, downhole temperature, flow rate, and mud compatibility. As the result, the model analyzes accumulative risk of fatigue failure and produces real-time health information for the power section as a percentage of the remaining lifespan. The new solution for mud motor PHM was successfully verified and tested in the field. Comparison of the predicted mud motor fatigue life with the actual observed postjob conditions and job failures demonstrated good results of the developed models. The PHM enables optimization of mud motor selection, drilling configuration, and maintenance operations by minimizing RUL uncertainties while facilitating rerun decisions and avoiding overmaintenance and premature retirements. The whole solution is currently being integrated into a drilling platform including the maintenance system, the well construction planning, and the execution. It maximizes the equipment usage with increased drilling performance without sacrificing reliability and enables optimal fleet management of a drilling process for revenue maximization.