Automated Drill Rig Control Using MWD Feedback and Bit Changes
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Solution Overview
Problem
Automated drill rigs in mines require operator oversight to manage Measurement While Drilling (MWD) data, leading to inefficiencies due to the need for manual decision-making on drill bit changes, which are time-consuming and may not optimize drilling operations based on real-time geological conditions.
Innovation Solution
A system and method that senses drilling and geological data to automatically optimize drilling operations by changing drill bits and adjusting drilling parameters, such as drill bit selection and hole order, based on estimated geological properties to maximize efficiency and minimize time, using a processor as a decision engine to factor in the time required for bit changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operator oversight is used to monitor MWD data and make decisions on drill bit changes, then operational flexibility and adaptability are maintained, but productivity decreases due to time-consuming manual decision-making and bit changes
Solution Approach 1:
The drilling system performs self-monitoring and self-optimization through automated sensors that track MWD data and a decision engine that autonomously determines when drill bit changes are necessary, eliminating the need for continuous manual operator intervention and enabling the system to service itself
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor drilling parameters in real-time, the processor analyzes the data to detect deviations from optimal performance, and the system automatically adjusts drilling parameters or triggers bit changes based on the analyzed feedback
2Productivity
If drill bit changes are made frequently to optimize for varying geological conditions, then drilling efficiency and adaptability improve, but loss of time due to bit changes increases
Solution Approach 1:
The system performs preliminary analysis of MWD data to predict upcoming geological changes and proactively schedules drill bit changes before performance degradation occurs, rather than reacting after efficiency drops, thereby optimizing the timing of bit changes to minimize downtime
Solution Approach 2:
The system dynamically changes drilling parameters such as rotational speed, feed rate, and hammer energy based on real-time geological conditions detected by sensors, allowing optimization of drilling efficiency without necessarily requiring physical drill bit changes
3Productivity
If automated systems are implemented to optimize drilling operations, then productivity and precision improve, but device complexity increases
Solution Approach 1:
The decision engine serves multiple functions by simultaneously analyzing various MWD parameters, determining geological conditions, optimizing drilling parameters, and scheduling drill bit changes, consolidating what could be separate complex systems into a single multi-functional unit
Solution Approach 2:
The processor acts as an intermediary between the sensors that collect MWD data and the drilling system that executes changes, translating raw sensor data into optimized drilling decisions without requiring direct complex connections between all system components
Data Source
AI summary
A system 10 for controlling a drilling operation by a drill rig 12 includes a receiver 16 for receiving sensed data related to the drilling operation. A processor 18 is in communication with the receiver 16, the processor 18 processing the sensed data to estimate at least one geological property of interest of a zone 20 in which the drill rig 12 is active. The processor 18 is configured to operate as a decision engine 22 to optimize the drilling operation automatically by changing at least one drilling related parameter during the drilling operation based on the at least one geological property of interest.