Electronics that operate under downhole drilling conditions, such as printed circuit board assemblies (PCBAs) in measurementwhile-drilling (MWD)/logging-while-drilling (LWD) tools, can experience a significant amount of extremely high temperatures and vibration stresses over their lifetimes, which may induce failures during deployment. This study demonstrates the methodologies on integrated physics of failure (PoF) for improving the reliability performance of MWD/LWD tools.PoF discussed in this study is for identifying and understanding failure modes, failure mechanisms, and failure sites of drilling electronics for their particular life-cycle loading conditions. This PoF study is a systematic methodology, integrated with data acquisition technologies, conditional-based maintenance (CBM) and failure analysis techniques to actualize the real-time life management on improving maintenance decisions, and providing guidance to incorporate reliability into the well-planning and operation processes, thus enabling monetary savings for the oil company and the MWD/LWD service provider.Through data acquisition technologies, the drilling environmental profile is captured by the sensor and recorded in the memories of the MWD/LWD tool. Based on the drilling environmental profiles, the drilling stress and loading conditions are retrieved and calculated for estimating the remaining life of the drilling electronics. With CBM strategy, electronic units assessed with high risk results are excluded from being deployed to the field to avoid potential failure in the field. PoF is followed up to investigate the failure mechanism of units returned from field. Real-case studies on MWD/LWD tools operated onshore the U.S. and in the Asia Pacific region are presented.The objective of this study is to enable real-time electronics reliability assessment on MWD/LWD tools under their actual application conditions. When combined with PoF models, CBM strategy is capable of making continuously updated predictions on the remaining life of electronics based on the in-situ drilling environmental and operational conditions. IntroductionThe effectiveness of integrated PoF and CBM strategies could provide operational benefits through detecting and identifying the potential or early-stage electronics parts or system failures, and make the proactive repair or maintenance feasible. It will enable the field operations group to have capability to avoid downhole tool failures at the rig site, therefore reducing risk of failure and subsequently reducing associated costs.Compared to wireline applications, the LWD/MWD operation requires closer field control of downhole dynamics. From the sub level to individual PCBA level, use of a tracking system is highly recommended. This approach will make the real-time life management of LWD/MWD tools feasible through the remaining life calculation from bottom level to system level.Until now, to ensure the reliability performance of LWD/MWD tools, planned maintenance (PM) was adopted as the method for determining maintenanc...
The capability to optimize drilling performance by predicting the life of drilling components is integral to preventing costly downhole tool failures and ensuring success of any drilling operation. Drilling tools are subject to various parameters such as vibration, temperature, revolutions per minute (RPM) and torque. These parameters can greatly fatigue even the most robust tool depending on the where and how the tool is operated. As a result, there is a need to predict time to failure of components operating in a downhole drilling environment. Analyzing operational data, inclusive of the parameters above, prior to or during maintenance actions and before starting drilling jobs, provides unique insight into how to improve the drilling performance of tools and to reduce downtime. Life prediction provides a cutting-edge way to identify precursors to costly failures in the field and enables proactive guidance during maintenance periods for parts which may otherwise have been disregarded strictly on maintenance procedures. Statistical models that relate operating environment to the component life and are derived from failure data of fielded components, introduce a new way to optimize the efficiency of drilling tools. Utilizing lifetime prediction to optimize drilling performance is a groundbreaking methodology developed to determine life of components operating in benign and harsh drilling environments by incorporating statistical aspects such as those caused because of variation in operating stress and maintenance upgrades. Since the algorithm utilizes field data, the need for costly laboratory experiments are also eliminated. Each model developed is unique to the specified part and can be calibrated for the best fit. In this methodology, a Bayesian-based model selection technique is developed that incorporates operating environment variables after each successful drilling run to dynamically select a model that gives the best survival probability for that component. Dynamic model selection ensures maximum utilization of a component, while avoiding failure to improve the overall reliability of the tool while in the field. The paper describes the methodology used to estimate the life of components in drilling systems by employing operational data, drilling dynamics and historical information.
Drilling tools are subject to numerous operational parameters such as revolutions per minute (RPM), vibration (lateral, stickslip and axial), pressure, torque and temperature. These parameters can greatly fatigue even the most robust tool depending on where and how the tool is operated. Lifetime prediction methodologies represent an affordable and statistically significant way to estimate the probability of failure (risk) of drilling tools in a cost effective way. Understanding the potential risk is vital to ensuring reliability, performing the most efficient maintenance on the equipment and improving drilling performance. Sophisticated risk-modeling techniques reduce uncertainty in drilling operations by making use of readily available operational field data, thus eliminating the need for costly laboratory experiments. Blind spots in the decision making process are eliminated by proactively identifying precursors to costly failures in the field. Preemptive guidance during maintenance periods, for parts that may have otherwise been overlooked based strictly on procedure, is enabled. Statistical models that relate the operating environment to component life are derived from field component failure data, and introduce a fresh way to boost the drilling tool efficiency. A Bayesian-based model selection technique is also developed which incorporates operating environment variables after each successful drilling run to dynamically select the model that gives the best survival probability, ensuring maximum utilization of a component, while avoiding failure and improving the overall reliability of the tool in the field. The implementation of lifetime prediction methodologies also leads to lowered life-cycle and maintenance costs, reduced risk and improved operational performance. The paper presents the methodology used to estimate the probability of failure of drilling tools and further illustrates how to reach risk-informed decisions.
Drilling technology for oil and gas exploration has evolved continuously based on feedback from operational experience. With the operators' focus on drilling more challenging unconventional wells, the biggest drivers are efficiency and operating cost. Operators want more real-time information during drilling to provide early warning of potential problems and take corrective actions. Service providers are meeting these challenges with technology improvements that are more robust and automated. The main focus for service providers is improving reliability throughout the service life of the tool while reducing maintenance cost. Consequently, in the past few years there is growing interest in the oil and gas industry towards developing technology and tools to gather more real-time downhole data and use analytical algorithms for fault diagnostics and health prognostics of components in drilling systems. This paper develops the framework and algorithms for constructing data-driven component life models and utilizing them to optimize operational efficiency and extend the life of the drilling system. The key driver behind this approach it to minimize the overall life cycle cost of tools which includes the cost of maintenance and cost of failure. The optimization variables are maintenance intervals and operational parameters (e.g. rpm, weight on bit, etc.) that should be tuned to achieve a desired level of drilling efficiency and reliability. Mathematical models for predicting the life of critical components in the drilling system is developed a-priori by using design qualification test data, operational data, drilling dynamics and historical FRACAS (Failure reporting analysis and corrective action system) information. The framework developed in this paper utilizes these predictive life models for making operations and maintenance decisions at various stages during the life cycle of the tools. The methodology developed in this paper is used to optimize the operational parameters and maintenance intervals for two designs of the bottomhole assembly namely (a) rotary steerable system without motor and (b) rotary steerable system with motor. Tradeoff of maintenance cost and operational performance is studied for different level of operational parameters. The results presented in this paper show that significant improvements in operational efficiency and maintenance intervals can be optimized by using downhole operations and predictive analytics.
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