Abstract: A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Abstract: A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Abstract: A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy.

Abstract: A suppression module, a suppression assembly, and a drilling tool are disclosed. The suppression module includes a spindle having a first end and a second end, an ear plate slideably disposed between the first end and the second end, a spring positioned between the ear plate and the first end, and an activation mechanism coaxially disposed around the second end of the spindle. The activation mechanism engages the ear plate, activating the spring, or disengages the ear plate, deactivating the spring. The suppression assembly includes at least two suppression modules in series coupled to a processor. The suppression modules are activated or deactivated by the processor, each of the suppression modules being activated or deactivated independent of the other suppression modules. The drilling tool includes a drill pipe, a drill bit, a vibration sensor, and a suppression assembly between the drill pipe and the drill bit.