PATTERN RECOGNITION BASED FAULT DETECTION OF ADVANCED DRILLING TOOLS

摘要

Modern petroleum drilling equipment operates in increasingly severe environments, with down-hole temperatures in excess of 200°C and high impact vibration events being common. Additionally, rig operators are asking tools to perform mission profiles that have previously been impossible, thereby increasing the stress on the down-hole tools. All the while, customers are beginning to contractually demand high reliability to help them prevent costly down-hole failures and ensure profitability. The current periodic maintenance practices are proving to be insufficient or cost intensive to meet these new challenges. Because of this, industry is shifting towards simple condition based maintenance approaches, which use design guidelines and rough operational thresholds to assess individual tool health. While there is value in the latter approach, there is a large amount of tool performance and environmental data collected during operation that has yet to be effectively incorporated into the health assessment process. This paper presents a new, empirical model based approach for detecting faults prior to failure in components of bottom hole assembly (BHA) tools. This approach can be briefly described as using real world examples of "good" runs to establish a statistical definition of un-faulted tool operation. To determine whether or not another tool is operating normally, a statistical test is used to determine if the tool is operating in a nominal (i.e. statistics are similar to un-faulted behavior) or degraded (i.e. statistics are not similar to un-faulted behavior) mode. In this way, it is possible to assess tool health on the basis of its actual performance, as opposed to its expected performance for quantized environmental factors (i.e. elevated static loads like bending, dynamic loads like lateral vibration, the combination of static and dynamic loads, etc.). This approach is demonstrated with operational data collected from a rotating steering system tool. The developed system will allow service providers to make more agile maintenance decisions and provide operators the means to incorporate reliability into the well planning and operations processes, enabling monetary savings for both parties.