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

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*** 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. ***