Earth-Boring Tool Parameter Control for Vibration and Wear
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Solution Overview
Problem
Existing earth-boring tools face inefficiencies and potential damage due to the difficulty in adjusting operating parameters in response to varying rock formations, often leading to excessive vibration and wear, which traditional reinforcement learning models fail to adequately address.
Innovation Solution
A machine learning-based prediction model using reinforcement learning and a reward function that considers factors like tool wear, vibration, and cutter durability to optimize operating parameters, such as WOB and RPM, to enhance drilling efficiency and prevent tool damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional reinforcement learning models are used to control earth-boring tools, then automation extent is improved, but tool reliability deteriorates due to excessive vibration and wear
Solution Approach 1:
The patent implements a feedback mechanism where sensors continuously monitor drilling parameters (weight on bit, rotational speed, vibration, torque) and feed this data back to the reinforcement learning model. The model adjusts operating parameters in real-time based on this feedback to prevent excessive vibration and wear, thereby maintaining tool reliability while achieving automation. This closed-loop control system resolves the contradiction by enabling automated control that actively prevents harmful conditions.
Solution Approach 2:
The reinforcement learning model dynamically adapts drilling parameters (weight on bit, rotational speed, feed rate) based on real-time formation conditions and tool response. Rather than using fixed or pre-programmed parameters, the system continuously optimizes parameters to match changing conditions, preventing both excessive vibration (which causes wear) and maintaining efficient drilling. This dynamic adaptation enables both automation and tool protection.
2Productivity
If drilling parameters are increased to improve drilling speed, then productivity is improved, but tool wear and vibration increase leading to reduced reliability
Solution Approach 1:
The system dynamically adjusts drilling parameters (weight on bit, rotational speed, feed rate) based on real-time feedback from sensors monitoring vibration, torque, and formation conditions. When vibration or wear indicators increase, the model automatically reduces parameters to protect the tool. When conditions are favorable, it increases parameters to maximize drilling speed. This dynamic optimization resolves the contradiction by enabling high productivity only when tool reliability is maintained.
Solution Approach 2:
The reinforcement learning model continuously changes operating parameters (weight on bit, rotational speed, feed rate) based on learned patterns from training data and real-time sensor feedback. The model identifies optimal parameter combinations that maximize drilling rate while staying below vibration and wear thresholds. This parameter optimization enables the system to achieve high productivity without exceeding tool reliability limits.
3Reliability
If adaptive parameter adjustment is implemented to reduce tool wear, then device complexity increases, but traditional methods lack the sophistication to handle varying rock formations
Solution Approach 1:
The reinforcement learning model operates autonomously, making decisions about parameter adjustments without requiring complex external control systems or human intervention. The model learns optimal control strategies during training and then self-manages the drilling parameters during operation, reducing tool wear through intelligent decision-making. This self-service capability achieves tool protection without proportionally increasing control system complexity.
Solution Approach 2:
The patent replaces complex mechanical control systems with an intelligent software-based reinforcement learning model. Rather than using sophisticated mechanical feedback mechanisms and physical control devices, the system uses computational algorithms to analyze sensor data and adjust parameters. This substitution reduces the mechanical complexity of the control system while achieving sophisticated adaptive parameter adjustment for tool wear reduction.
Data Source
AI summary
An earth-boring tool system may include a drill string including at least one drilling tool. The earth-boring tool system may also include at least one processor and at least one non-transitory computer-readable storage medium storing instructions to cause the earth-boring tool system to receive first drilling environment data, train an operational drilling model based, at least in part, on the first drilling environment data and a reward function defining one or more rewards or punishments based, at least in part, on one or more drilling parameters including bit wear, rate of penetration (ROP), Stick Slip, cutter durability, or a reference baseline drilling policy, receive second drilling environment data, and determine, via the operational drilling model, one or more first actions based on the second drilling information data, the one or more first actions configured to change one or more operating parameters of the earth-boring tool system.