The ability to analyze drilling data to obtain continuous monitoring statistics of the drilling process and make prompt decisions are two important elements of a successful drilling operation. A mud motor is one of the important components of the downhole assembly, which enables the drill bit to penetrate the rock during drilling a well. Correctly predicting mud motor failure and the remaining useful life of the components are essential for obtaining drilling efficiency, avoiding costly operational expenses, and achieving timely maintenance. The remaining useful life indicator with low uncertainty identifies the life cycle of mud motors by preventing redundant maintenance and costly drilling operation failures. This paper presents an industry-unique prognostics and health-management (PHM) solution for monitoring and maintaining the mud motor condition. This solution combines three algorithms, including a power section PHM algorithm, lower-end critical connections PHM algorithm, and mud motor degradation algorithm. The workflow solution allows for obtaining valuable information about the mud motor condition at the system and component levels. The power section PHM algorithm, based on a remaining useful life prediction for the mud motor's power section, provides information about the elastomer condition inside of the stator as a percentage of the remaining life cycle. The lower-end critical connections PHM algorithm estimates the remaining useful life of the mud motor's lower-end connections. Both algorithms are component level; i. e., they help to improve managing the life cycle of the appropriate components. The mud motor degradation algorithm is a system-level algorithm. This algorithm uses drilling data to compute the severity of mud motor degradation; thus, identifying possible problems with the mud motor as a complete system. The PHM solution helps to prevent expensive mud motor failure. Furthermore, the solution provides the opportunity to perform additional drilling runs before the motor components must be retired or removed for maintenance. The significant advantage of applying the PHM solution is it only makes use of existing drilling measurements and does not require any special downhole equipment. The mud motor PHM solution is currently in use by one of the biggest oil & gas service company worldwide. In addition to presenting the three algorithms, this paper presents field application case studies that demonstrate the commercial value and efficiency gains achieved by their use. Significant sustainability benefits have been achieved by using the power section and mud motor degradation algorithms due to their assistance in drilling applications.
Mud motors are widely used for directional and performance drilling. Due to the extremely challenging operating conditions, they are prone to failures, resulting in unnecessary maintenance repair costs as well as unpredictable and very costly drilling failure. Until now, the oil and gas industry has lacked reliable procedures to monitor and maintain the health of the mud motor power sections. Recently, we systematically addressed this problem with an industry unique prognostic health management solution, which not only tracks remaining useful life (RUL), but also creates a new failure prevention scheme for operators. The key objective of this solution is to reduce maintenance costs and improve mud motor fleet reliability. It's 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 for real-time applications. The new methodology was tested in the field with great success. The verification of the created solution was completed based on numerous field data from Saudi Arabia and Argentina. Comparison of the predicted mud motor fatigue values with the actual observed post-job conditions and job failures demonstrated high fidelity of the developed models. The whole solution is currently being integrated into a drilling platform including the maintenance system, the well construction planning, and the execution. The first application of the workflow was deployed in the field in Colombia targeting reduction of maintenance cost and failure avoidance. The result was outstanding, with the initial deployment bringing about 27% of projected yearly maintenance savings and 10% of projected yearly failure reduction. It enables using the equipment to the full extent with increased drilling performance without sacrificing reliability. In addition, it optimizes the entire fleet management with reduced cost of logistics and maintenance. The findings of this paper demonstrate the value of the mud motor PHM solution for the oil and gas industry by providing accurate prognosis of power section health, leading to reduced costs, minimized NPT, and increased operational reliability.
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.
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