Drilling Machine Coal Seam Detection via MWD Data
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Solution Overview
Problem
Current methods for detecting coal seams in open pit mining are often subjective, costly, and prone to inaccuracies due to manual processes or geophysical logging, which can lead to undesired damage to exploitable coal during blasting.
Innovation Solution
A system that uses a drilling machine equipped with sensors to collect high-frequency data, which is then processed using machine learning algorithms to detect rock-coal transitions, allowing for real-time identification and control of drilling operations to avoid coal seams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes or geophysical logging are used to detect coal seams, then detailed coal seam information can be obtained, but the process becomes subjective, costly, and prone to inaccuracies
Solution Approach 1:
The patent replaces manual detection methods and complex geophysical logging equipment with a simplified sensor-based system that measures drilling parameters (torque, power, rate of penetration) and uses machine learning algorithms to automatically identify coal seams, eliminating subjectivity and reducing operational complexity
Solution Approach 2:
The drilling machine performs self-detection of coal seams by monitoring its own drilling performance data, eliminating the need for separate geophysical logging operations and manual interpretation, thereby reducing cost and improving accuracy
2Loss of information
If geophysical logging methods are inserted into the blasthole post-drilling, then coal seam information can be obtained, but additional cost and time are required, and depth errors occur due to misalignment
Solution Approach 1:
The system performs coal seam detection during the drilling operation itself by continuously monitoring drilling parameters, rather than performing separate post-drilling geophysical logging, thereby eliminating additional time consumption and depth alignment errors
Solution Approach 2:
The patent uses drilling performance data as an intermediary to indirectly detect coal seams, avoiding the need for direct post-drilling geophysical logging and the associated time losses and depth misalignment issues
3Ease of operation
If geophysical logs are interpreted to identify coal seams, then coal seam location can be determined, but the process is inaccurate, time-consuming, and influenced by technician skills
Solution Approach 1:
The patent replaces subjective human interpretation of geophysical logs with automated machine learning algorithms that objectively analyze drilling parameter patterns, eliminating technician skill dependency and improving identification accuracy
Solution Approach 2:
The system uses machine learning models trained on historical drilling data to provide automated, consistent interpretations of real-time drilling parameters, eliminating variability introduced by different technicians and improving operational ease
4Object-affected harmful factors
If explosives are placed in waste rock zones instead of coal seams, then coal damage is avoided, but detailed three-dimensional geospatial information is required to make this distinction
Solution Approach 1:
The system identifies coal seams during the drilling phase before explosive placement, providing real-time depth and location information that enables accurate positioning of explosives in waste rock zones and avoidance of coal seams, thereby preventing coal damage
Solution Approach 2:
The drilling machine provides continuous feedback on coal seam location and depth, enabling dynamic adjustment of explosive placement decisions to ensure explosives are positioned in waste rock zones rather than coal seams
Data Source
AI summary
A system, apparatus, and method for controlling operation of a drilling machine includes determining a rock-coal transition and enabling both the real-time control of the blasthole drilling operation of the drilling machine responsive to the determination of the rock-coal transition or using the rock-coal transition information for mine planning in a post-processing application. Such controlling can include stopping the drilling operation of the drilling machine prior to or upon reaching the coal. Mine planning allows for more efficient removal of the exploitable coal. The determining and controlling can be performed in real time based on specialized transformation of Monitor-While Drilling (MWD) data from one or more sensors of the drilling machine while the drilling machine is drilling. The mine planning application is based on processing the Monitor-While Drilling (MWD) data from one or more sensors of the drilling machine after the drilling machine has completed the drilling of a blasthole or blastholes.