SBIR Phase I: Artificial Intelligence-Based Acoustic Emission Monitoring for Bit Wear During Deep Drilling

* 9660288星期日 这个小型企业创新研究第一阶段项目将解决开发智能随钻测量（MWD）设备的可行性，用于井下监测钻头磨损和警告即将发生的钻头故障，包括轴承故障和磨损钻头。 MWD设备的工作原理采用基于人工智能（AI）的声发射（AE）技术。 这一发展将大大降低目前深钻作业的成本，并可能最终促进智能钻井系统和无人驾驶钻井过程的发展。 在第一阶段研究中，将进行现场钻探实验。 在钻进过程中，在钻头-岩石界面处产生的AE信号将在钻头磨损的不同阶段被监测。 首先，AE信号将研究的频率带宽，幅度的动态范围，和持续时间为进一步的仪器开发。 然后，通过（1）开发用于AE信号监测的合适仪器，（2）观察来自钻头磨损的不同阶段的AE信号中的可识别特征，以及（3）使用合适的模式识别（AI）算法，通过观察到的AE特征来识别钻头磨损、地层变化和即将发生的钻头失效的程度，来证明其可行性。 在进行研究后，预计将使用基于AI的AE技术开发新设备。 该装置将能够感知钻头磨损程度、地层变化和即将发生的钻头失效警告。 该装置可显著降低深海和陆地钻井的操作成本，具有很好的商业潜力。 本研究提出的概念可方便地应用于“智能钻井系统”和无人钻井过程。 ***

*** 9660288 Sun This Small Business Innovation Research Phase I project will address the feasibility for the development of an intelligent measurement-while-drilling (MWD) devices for downhole monitoring of bit wear and warning of impending bit failure, including both bearing failure and worn bit. The operating principle of the MWD device uses artificial intelligence (AI) based acoustic emission (AE) technologies. This development will considerably reduce the cost in the current deep drilling operation and may eventually facilitate the development of smart drilling systems and unmanned drilling processes. In the Phase I study, a field drilling experiment will be conducted. AE signals generated at the bit-rock interface will be monitored in different stages of bit wear during drilling processes. First, the AE signals will be studied with regard to frequency bandwidth, amplitude of dynamic range, and duration for further instrumentation development. The feasibility will then be demonstrated by (1) development of suitable instrumentation for AE signal monitoring, (2) observation of recognizable features in AE signals from different stages of bit wear, and (3) using suitable pattern recognition (AI) algorithm to identify the degree of bit-wear, formation change and impending bit failure by the AE features observed. After studies are conducted, it is anticipated that a new device will be developed using AI-based AE technology. The device will be capable of sensing the degree of bit wear, formation change and warning of impending bit failure. The device may significantly reduce the operating cost in deep ocean and land drilling and has very good commercial potential. The concept developed in this research may be conveniently used in the "smart drilling system" and unmanned drilling processes. ***