Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises
Abstract
:1. Introduction
2. Literature Overview and State of the Art in MWD Field
3. Application of Machine Learning to MWD Data in the Mining Industry
4. Challenges and Obstacles
- Cross-correlation of recorded drilling parameters does not stay constant and varies depending on specific geological conditions and utilized drilling equipment;
- Difficulty of identifying drilling parameters, independent from other drilling indicators and monitoring system, accounting for mineral and geological factors that influence their variation, eliminating errors and factors irrelevant to the rock mass properties. Among existing studies, there are contradictions regarding the question of which parameters should be used to describe rock properties under different conditions;
- Verification of parameters, measured by the MWD system, in many cases confirms data reliability. It should be mentioned, that in order to be able to use MWD data under new geological conditions or in new site, periodic laboratory core studies or geophysical logging are necessary to clearly indicate the type of rock, since, for example, the penetration rate can be the same for different types of rock However, despite the diversity and high precision of MWD data verification tools, including optical [4], mechanical [75], and geophysical [1] methods, there are problems of comparison between thus obtained data and information collected by a MWD system; e.g., in case of detected structural irregularities and faults in the rock mass, the parameters recorded by the system can be distorted due to jamming of the drill tool in highly fractured zones, idle rotation in the cavities, and rod deviation in the process of drilling [54];
- All the practical applications of MWD systems refer to the qualitative and quantitative characterization of the rock mass before blasting. From the data obtained and the strength characteristics and structural features of the rock mass, it is possible to adjust the design parameters of D&B operations in order to improve their efficiency. However, direct estimation of dependency between results of D&B operations and measured MWD parameters is also necessary for prediction purposes as, besides geometric characteristics of the site, rock strength parameters and their structural features, D&B parameters themselves (type of explosive, powder factor, charge design, among others) affect the results;
- Machine learning methods applied to MWD data limit the number of input parameters to simplify the final algorithm and reduce calculation and training time. Selection of input parameters from the scope of available data is a difficult task, especially because it may vary from drill rig and drilling technique.
5. Discussion About Future Directions of MWD Research
- Existing methods of MWD data processing and analysis mostly use statistical analysis tools, which rule out automatic processing of data in case of changing mining, geological, and engineering conditions. It means that when an actual change takes place, e.g., change of drill rig mode, even on the same site, it is necessary to process the data all over again, i.e., define dependent and independent parameters, identify the most significant parameters dependent on rock properties, among others. Hence, a relevant task of applied research is to develop a learning algorithm, which would use machine learning methods and based on available input data, associated with specific conditions, would be able to correlate, process, and select parameters coming from the drill rig and utilize them for subsequent estimation of rock characteristics, and prediction of optimal D&B parameters and blasting results. In the meantime, the production of a training dataset, collected in the process of algorithm operation, could simplify data collection for future applications under similar conditions.
- Prediction of fragment size distribution in the blasted rock mass based on MWD data is a relevant production problem. Modern methods of fragment size prediction, based on theoretical and empirical models [76,77] and even machine learning methods [78,79], use only D&B parameters and existing classifications of rock strength and structural irregularities, which sometimes fail to take into account the entire unique structure and heterogeneity of the rock mass to be blasted. However, if prediction of fragment size distribution in the muck pile utilized MWD data collected from each blasthole, and then combined them into a block model, taking into account rock strength variable characteristics and structural irregularities, it might be able to provide a better estimation of the blast results. Extensive assessment of MWD data cleaning algorithms and interpretation into mechanical and structural rock characteristics, development of machine learning algorithms incorporating explosives characteristics, and verification with fragmentation data are topics where research efforts would certainly offer a high return.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Category of Rocks According to a “Unified Classification” | Specific Energy Consumption of Rotary Drilling (Tangaev) | Typical Examples of Rocks | |
---|---|---|---|
E, MJ/m | MJ/m3 | ||
IV | - | - | Heavy clay. Loam with crushed stone and gravel. Very soft coals |
V | - | - | Clay siltstones. Weak mudstones. Clay marl. Soft coals |
VI | 2, 16 | 48 | Dolomites affected by weathering. Carbonaceous shales. Medium coals |
VII | 2, 88 | 64 | Dense siltstones. Unchanged dolomites. Soft limestones. Highly weathered shales. Coals above medium strength |
VIII | 3, 60 | 80 | Anthracites. Soft iron ores. Shales. Weathered tuffs |
IX | 4, 22 | 96 | Completely weathered granites, granodiorites. Weathered sandstones, limestones |
X | 5, 04 | 112 | Apatite ore. Strongly weathered granites, dunites. Serpentines. peridotites |
XI | 5, 94 | 132 | Destroyed gneisses. Coarse-grained, marbled, dolomitized limestones. Slates. Pyrite and manganese ores |
XII | 6, 06 | 148 | Apatite-nepheline ore. Anhydrites. Weathered: gabbros, gneisses, granites, diabases. Copper-pyrite ores |
XIII | 8, 28 | 184 | Weakly weathered: granites, diabases. Coarse magnetite iron ores. |
XIV | 9, 52 | 216 | Medium-grained weathered andesites. Gabbro modified. Coarse-grained: gneisses, granites, granodiorites. |
XV | 11, 52 | 256 | Medium-grained granites, granodiorites, diabases. Silicated dolomites. Marbles. |
XVI | 13, 50 | 300 | Medium-grained gabbros, gneisses, dunites, peridotites, porphyrites. Highly silicified limestones |
XVII | 16, 56 | 370 | Medium-grained basalts. Fine-grained: gabbro, granite, granodiorite, diabase. Siliceous limestones and sandstones. |
XVIII | 19, 52 | 440 | Dense andesites. Fine-grained basalts, diorites, skarns. Fine -grained titanium-magnetite ores |
XIX | 24, 84 | 550 | Very dense: andesites, basalts, diabases, diorites. Microgranites, microquartzites. Dense hematite ores. |
XX | 26, 64 | 600 | Unchanged drain: andesite, jaspilite, basalt, iron ore. Drain quartz. Microgranites |
No. | Hardness Coefficient According to M. M. Protodyakonov | Minimum Specific Drilling Energy, MJ/m3 | Maximum Specific Drilling Energy, MJ/m3 | Specific Blasting Energy, MJ/m3 |
---|---|---|---|---|
1 | 6 | 30.48 | 42.68 | 2.62 |
2 | 7 | 42.68 | 54.87 | 2.89 |
3 | 8 | 54.87 | 67.06 | 3.16 |
4 | 9 | 67.06 | 79.26 | 3.38 |
5 | 10 | 79.26 | 91.45 | 3.59 |
6 | 11 | 91.45 | 109.74 | 3.80 |
7 | 12 | 109.74 | 128.03 | 3.98 |
8 | 13 | 128.03 | 152.42 | 4.11 |
9 | 14 | 152.42 | 176.8 | 4.23 |
10 | 15 | 176.80 | 213.38 | 4.36 |
11 | 16 | 213.38 | 256.06 | 4.52 |
12 | 17 | 256.06 | 304.83 | 4.67 |
13 | 18 | 304.83 | 359.7 | 4.80 |
14 | 19 | 359.7 | 420.67 | 5.00 |
15 | 20 | 420.67 | 493.83 | 5.09 |
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Isheyskiy, V.; Sanchidrián, J.A. Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises. Minerals 2020, 10, 925. https://doi.org/10.3390/min10100925
Isheyskiy V, Sanchidrián JA. Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises. Minerals. 2020; 10(10):925. https://doi.org/10.3390/min10100925
Chicago/Turabian StyleIsheyskiy, Valentin, and José A. Sanchidrián. 2020. "Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises" Minerals 10, no. 10: 925. https://doi.org/10.3390/min10100925
APA StyleIsheyskiy, V., & Sanchidrián, J. A. (2020). Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises. Minerals, 10(10), 925. https://doi.org/10.3390/min10100925