Abstract: The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

Abstract: An abnormal trend detection system for detecting one or more hazards may provide for a safe and effective drilling operation as any of the one or more hazards may be avoided. Several indicators may be defined including a first, second, third and fourth indicator. The indicators are used to identify in a trend analysis abnormal trends. One or more thresholds may be defined. When a trend analysis indicates that a threshold has been reached or exceeded an alarm may be triggered, a drilling operation may be altered or a combination thereof.

Abstract: A method of optimizing drilling and drilling instructions for different segments of a wellbore that includes: calculating mathematical approximations of energy transfer loss between a top drive and a BHA using a neural network and a plurality of drilling records for segments of wellbores that are similar to the segments of the wellbore to be drilled; calculating drilling instructions based on each mathematical approximation; drilling the wellbore using the drilling instructions; monitoring the energy transfer loss; and optimizing the drilling instructions using one of the mathematical approximations to minimize the energy transfer loss. Optimizing the drilling instructions includes using Bayesian optimization techniques.

Abstract: Embodiments of the subject technology provide for receiving real-time drilling data comprising different drilling parameters measured during a drilling operation. The subject technology calculates a kick detection parameter based at least in part on the different drilling parameters. The subject technology detects an occurrence of a kick during the drilling operation when the kick detection parameter deviates from a trend formed by previously calculated kick detection parameters. Further, the subject technology activates an alarm during the drilling operation in response to detected occurrence of the kick to facilitate preventing a blowout.

Abstract: A method of optimizing drilling and drilling instructions for different segments of a wellbore that includes: calculating mathematical approximations of energy transfer loss between a top drive and a BHA using a neural network and a plurality of drilling records for segments of wellbores that are similar to the segments of the wellbore to be drilled; calculating drilling instructions based on each mathematical approximation; drilling the wellbore using the drilling instructions; monitoring the energy transfer loss; and optimizing the drilling instructions using one of the mathematical approximations to minimize the energy transfer loss. Optimizing the drilling instructions includes using Bayesian optimization techniques.

Abstract: Embodiments of the subject technology provide for receiving real-time drilling data comprising different drilling parameters measured during a drilling operation. The subject technology calculates a kick detection parameter based at least in part on the different drilling parameters. The subject technology detects an occurrence of a kick during the drilling operation when the kick detection parameter deviates from a trend formed by previously calculated kick detection parameters. Further, the subject technology activates an alarm during the drilling operation in response to detected occurrence of the kick to facilitate preventing a blowout.

Abstract: An abnormal trend detection system for detecting one or more hazards may provide for a safe and effective drilling operation as any of the one or more hazards may be avoided. Several indicators may be defined including a first, second, third and fourth indicator. The indicators are used to identify in a trend analysis abnormal trends. One or more thresholds may be defined. When a trend analysis indicates that a threshold has been reached or exceeded an alarm may be triggered, a drilling operation may be altered or a combination thereof.

Abstract: The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

Abstract: Apparatuses, methods, and systems include rotary drilling a first segment of a wellbore by rotating a drill string with a top drive forming a part of a drilling rig apparatus for a first period of time; obtaining data from a sensor disposed about the drilling rig apparatus while rotary drilling for at least a part of the first period of time; and based on the data from the sensor, determining a proposed oscillation revolution amount for the drill string to reduce friction of the drill string in the downhole bore without changing the direction of a bottom hole assembly while slide drilling.

Abstract: An apparatus for reducing vibration includes a damping assembly configured to be fixedly attached to a downhole component. The downhole component is configured to rotate within a borehole in an earth formation, and the damping assembly has a damping frequency that is tuned relative to a selected natural vibration frequency of the rotating downhole component to reduce vibration due to component rotation.

Abstract: Apparatuses, methods, and systems include rotary drilling a first segment of a wellbore by rotating a drill string with a top drive forming a part of a drilling rig apparatus for a first period of time; obtaining data from a sensor disposed about the drilling rig apparatus while rotary drilling for at least a part of the first period of time; and based on the data from the sensor, determining a proposed oscillation revolution amount for the drill string to reduce friction of the drill string in the downhole bore without changing the direction of a bottom hole assembly while slide drilling.

Abstract: An apparatus for reducing vibration includes: a damping assembly configured to be fixedly attached to a downhole component, the downhole component configured to rotate within a borehole in an earth formation, the damping assembly having a damping frequency that is tuned relative to a selected natural vibration frequency of the rotating downhole component to reduce vibration due to component rotation.