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Mud motors are widely used in directional drilling and their failure during operation leads to costly non-productive time. There is currently no existing literature investigating the correlation between stalls detected using downhole sensors and concurrent signals produced in surface sensor data. Current motor stall detection algorithms using surface sensors are still rudimentary and error-prone. The objective of this study was to develop a robust stall detection algorithm using insights gained from correlating downhole and surface data. Previous studies have indicated that stalls are a major contributing factor to elastomer damage in a mud motor. Using downhole sensor datasets from multiple operators, we first identified all instances of motor stalls. These events are typically characterized by a sudden reduction in downhole vibration and rotation at the bit, accompanied by a sudden increase in torque. We then proceeded to map these stall events to time-synchronized surface data to identify the associated behavior of surface parameters during a stall, noting the differences in behavior during rotary and slide drilling periods. We analyzed 268 distinct stall events in the downhole data as well as several clusters of micro-stalls (characterized by a momentary spike in downhole torque coupled with a downward spike in downhole RPM that last less than one second, typically lasting for a few milliseconds at most). Mapping these events to the surface data helped identify a set of primary signals produced at the surface during every motor stall, and secondary signals that are produced in a majority of motor stalls. The primary surface signals we observed during a stall included a sharp spike in differential pressure and a sharp decline in weight on bit (WOB), typically within a 10-second window. Secondary surface signal observed in over 70% of motor stalls include decrease in rate of penetration (ROP). Statistical analysis of the downhole and surface signals demonstrated a strong correlation (p < 0.05) between the length of a motor stall and the magnitude of the differential pressure increase produced at the surface. Our analysis of stalls in downhole datasets demonstrated that the vast majority of surface-detectable stalls occurred during slide drilling, while rotary drilling contained significant clusters of micro-stall events that were too short to produce identifiable signals at the surface. This study builds upon existing literature and understanding of mud motor failure by correlating time-synchronized events in downhole and surface data. We have compiled our observations to create a comprehensive framework for detecting motor stalls at the surface using a set of surface signals. Our findings establish a robust way to detect motor stalls from surface data, which should be of significant value to lower-cost well construction operations and real-time monitoring of drilling operations.
Mud motors are widely used in directional drilling and their failure during operation leads to costly non-productive time. There is currently no existing literature investigating the correlation between stalls detected using downhole sensors and concurrent signals produced in surface sensor data. Current motor stall detection algorithms using surface sensors are still rudimentary and error-prone. The objective of this study was to develop a robust stall detection algorithm using insights gained from correlating downhole and surface data. Previous studies have indicated that stalls are a major contributing factor to elastomer damage in a mud motor. Using downhole sensor datasets from multiple operators, we first identified all instances of motor stalls. These events are typically characterized by a sudden reduction in downhole vibration and rotation at the bit, accompanied by a sudden increase in torque. We then proceeded to map these stall events to time-synchronized surface data to identify the associated behavior of surface parameters during a stall, noting the differences in behavior during rotary and slide drilling periods. We analyzed 268 distinct stall events in the downhole data as well as several clusters of micro-stalls (characterized by a momentary spike in downhole torque coupled with a downward spike in downhole RPM that last less than one second, typically lasting for a few milliseconds at most). Mapping these events to the surface data helped identify a set of primary signals produced at the surface during every motor stall, and secondary signals that are produced in a majority of motor stalls. The primary surface signals we observed during a stall included a sharp spike in differential pressure and a sharp decline in weight on bit (WOB), typically within a 10-second window. Secondary surface signal observed in over 70% of motor stalls include decrease in rate of penetration (ROP). Statistical analysis of the downhole and surface signals demonstrated a strong correlation (p < 0.05) between the length of a motor stall and the magnitude of the differential pressure increase produced at the surface. Our analysis of stalls in downhole datasets demonstrated that the vast majority of surface-detectable stalls occurred during slide drilling, while rotary drilling contained significant clusters of micro-stall events that were too short to produce identifiable signals at the surface. This study builds upon existing literature and understanding of mud motor failure by correlating time-synchronized events in downhole and surface data. We have compiled our observations to create a comprehensive framework for detecting motor stalls at the surface using a set of surface signals. Our findings establish a robust way to detect motor stalls from surface data, which should be of significant value to lower-cost well construction operations and real-time monitoring of drilling operations.
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.
An approach that combines data-driven analysis and modeling was used to select the optimal mud motor power section tailored to specific drilling requirements. Additionally, a novel methodology was developed for estimating the CO2 footprint of drilling operations based on operational data. Practical demonstrations verified that the strategic utilization of the optimal power section enhances drilling efficiency and mitigates the CO2 impact associated with drilling processes. Our focus revolves around choosing the optimal power section for the drilling, considering both drilling conditions and limits. Additionally, we estimatethe CO2 footprint for the entire drilling process after the job to underscore the positive impact of selecting the right power section. To achieve these goals, we integrate an advanced physical model of the power section with the capabilities of machine learning and data science. This power section model empowers us to predict its performance and durability in advance, facilitating an optimal choice based on expected drilling conditions. The established workflow for CO2 footprint estimation utilizes surface drilling data, ensuring precise results. To optimize the power section for drilling, we employ a modeling method coupled with a machine-learning approach. This aids in selecting a suitable power section type and determining drilling parameters based on specific requirements and equipment specifications, contributing to heightened drilling efficiency. Leveraging digital capabilities enables strategic implementation to minimize greenhouse gas emissions. Enhancing drilling efficiency via optimal mud motor power section selection improves rate of penetration, reduces nonproductive time, and substantially cuts field failures. This cumulatively shortens total drilling time, leading to both direct and indirect CO2 emissions reductions. To gauge the carbon footprint during drilling operations, we construct a physical model. This model predicts rig power consumption by considering surface drilling parameters (as pressure, flow rate, etc.). It estimates power consumption by components like mud pumps, top drive, and draw works, using system efficiency data. Additionally, a model encompassing generator sets and diesel combustion is employed to estimate CO2 emissions. Application of the proposed methodology to a real field example demonstrated the impact of enhanced drilling performance with optimal power sections versus conventional ones, along with effective greenhouse gas emissions reduction. The entirety of the results and conclusions highlights the substantial value of digital technologies and a smart approach in selecting drilling equipment for the energy transition.
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