Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters
Abstract
:1. Introduction
2. Methodology
2.1. Drilling Process Monitoring (DPM) Parameters
2.2. Drilling Conditions
2.3. DPM Data
3. Regression Analysis
3.1. Two-Dimensional Regression Analysis
3.2. Three-Dimensional Regression Analysis
3.3. Regression Model Evalution
4. Analyses Using Machine Learning Methods
4.1. Random Forest
4.2. GA-SVM
5. Relationships between Rock Properties and DPM Parameters
5.1. Univariate Model of UCS
5.2. Two-Variate Model of UCS
5.3. Evaluation of UCS Prediction Models
6. Discussion
6.1. The Key DPM Parameters
6.2. UCS Prediction Model
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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N/r·min−1 | F/kN | ROP/cm·min−1 | T/N·m | SE/MJ·m−3 |
---|---|---|---|---|
40 | 10 | 3.02 | 68.195 | 81.48 |
11.5 | 3.31 | 72.011 | 78.76 | |
19 | 3.74 | 91.091 | 89.04 | |
28 | 3.69 | 113.987 | 113.48 | |
35 | 3.95 | 131.795 | 123.25 | |
40 | 4.03 | 144.515 | 132.79 | |
45 | 4.27 | 157.235 | 136.92 | |
50 | 3.61 | 169.955 | 173.99 | |
60 | 3.85 | 195.395 | 188.42 | |
65 | 4.27 | 208.115 | 181.99 | |
70 | 4.22 | 220.835 | 195.43 | |
115 | 8.5 | 3.44 | 64.379 | 191.98 |
19 | 4.44 | 91.091 | 211.82 | |
23.5 | 4.52 | 102.539 | 234.57 | |
29 | 4.81 | 116.531 | 251.06 | |
35.75 | 5.11 | 133.703 | 271.77 | |
42 | 6.38 | 149.603 | 244.96 | |
46.5 | 7.55 | 161.051 | 224.01 | |
53 | 7.01 | 177.587 | 265.73 | |
58.25 | 7.29 | 190.943 | 275.23 | |
60.5 | 5.87 | 196.667 | 350..07 | |
65 | 6.00 | 208.115 | 362.76 | |
220 | 8.7 | 3.09 | 64.888 | 410.74 |
18.25 | 4.784 | 89.183 | 366.12 | |
29 | 6.46 | 116.531 | 355.88 | |
34.85 | 7.71 | 131.4134 | 337.31 | |
40 | 6.68 | 144.515 | 427.54 | |
46.7 | 7.60 | 161.560 | 421.15 | |
51.5 | 7.83 | 173.771 | 440.06 | |
57.5 | 7.53 | 189.035 | 497.68 | |
60 | 7.21 | 195.395 | 536.97 | |
400 | 8.5 | 4.36 | 64.399 | 525.06 |
11.5 | 5.24 | 72.031 | 490.54 | |
18.8 | 6.55 | 90.602 | 492.93 | |
25 | 7.23 | 106.375 | 530.47 | |
30 | 8.09 | 119.095 | 551.47 | |
35.5 | 8.91 | 133.087 | 534.99 | |
42.5 | 13.31 | 150.895 | 435.13 | |
48.75 | 11.72 | 166.795 | 530.57 | |
53 | 10.61 | 177.607 | 584.46 | |
58 | 12.86 | 190.327 | 584.19 |
Model Type | T-F | V-F | V-T | SE-F | SE-T | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Fitting model | T = aF + b | R2 | V = aF b | R2 | V = aT b | R2 | SE = aF + b | R2 | SE = aT + b | R2 | ||
Rotating speed/r·min−1 | 40 | a | 2.544 | 1 | 2.386 | 0.7057 | 1.364 | 0.6666 | 2.049 | 0.967 | 0.8056 | 0.967 |
b | 42.75 | 0.1331 | 0.2096 | 55.2 | 20.75 | |||||||
115 | a | 2.544 | 1 | 1.584 | 0.7132 | 0.3744 | 0.6941 | 2.363 | 0.6803 | 0.9288 | 0.6803 | |
b | 42.75 | 0.3536 | 0.5493 | 167.4 | 127.7 | |||||||
220 | a | 2.544 | 1 | 1.606 | 0.8447 | 0.3172 | 0.7830 | 2.659 | 0.5109 | 1.045 | 0.5109 | |
b | 42.76 | 0.3934 | 0.6147 | 319.1 | 274.4 | |||||||
400 | a | 2.544 | 1 | 1.214 | 0.8803 | 0.09685 | 0.8749 | 1.108 | 0.1826 | 0.4354 | 0.1826 | |
b | 42.77 | 0.5788 | 0.9337 | 489.3 | 470.6 |
Regression Model | Rotating Speed /r·min−1 | R2 | RMSE | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
F-T | F-V | T-V | F-SE | T-SE | F-T | F-V (kN) | T-V (N·m) | F-SE (MJ·m−3) | T-SE (MJ·m−3) | |||
Two-dimensional | 40 | 1 | 0.7057 | 0.6666 | 0.9670 | 0.9670 | 0.005 | 0.207 | 0.2198 | 7.547 | 40.841 | |
115 | 1 | 0.7132 | 0.6941 | 0.6803 | 0.6803 | 0.005 | 0.675 | 0.6970 | 28.640 | 98.618 | ||
220 | 1 | 0.8447 | 0.7830 | 0.5109 | 0.5109 | 0.00496 | 0.591 | 0.7047 | 43.047 | 43.983 | ||
400 | 1 | 0.8803 | 0.8749 | 0.1826 | 0.1826 | 0.00498 | 1.032 | 1.0551 | 38.552 | 18.222 | ||
Three-dimensional | N-F-V | N-T-V | SE=f (F, N, V, A) | N-F-V (cm·min−1) | N-T-V (cm·min−1) | SE=f (F, N, V, A) (MJ·m−3) | ||||||
0.903 | 0.879 | 0.996 | 0.794 | 0.890 | 10.11 |
Prediction Models | Trian Set | Test Set | ||
---|---|---|---|---|
R2 | RMSE (MJ·m−3) | R2 | RMSE (MJ·m−3) | |
GA-SVM | 99.99% | 0.2578 | 99.98% | 2.5208 |
RF | 94.55% | 42.1341 | 95.91% | 48.7496 |
References | Bit Types | Bit Geometry Parameter | Data Size |
---|---|---|---|
Wang [54] | Standard diamond solid bit | Radius is 7.5 mm | 34 |
He [55] | Impregnated diamond bit | External diameter is 70mm, and external diameter is 60 mm | 17 |
Wang [56,57] | PDC bit | Cutting edge L1, L2 and L3 are 18, 18 and 27 mm, respectively, and radius is 30 mm | 42 |
Bit Types | UCS Prediction Model | |
---|---|---|
SE-UCS | Id-UCS | |
Standard diamond solid bit | ||
Impregnated diamond bit | ||
PDC bit | ||
Combine the above three bits |
UCS Models | Standard Diamond Solid Bit | Impregnated Diamond Bit | PDC Bit | Combine the Above Three Bits | |||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | ||
Univariate model | SE-UCS | 0.8845 | 9.6273 (103 MJ·m3) | 0.8067 | 0.8927 (103 MJ·m3) | 0.8916 | 0.0151 (103 MJ·m3) | 0.7333 | 12.7979 (103 MJ·m3) |
Id-UCS | 0.8614 | 0.0041 | 0.3645 | 0.0064 | 0.1466 | 0.0240 | 0.0070 | 0.0260 | |
Two-variate model | SE-Id-UCS | 0.9051 | 18.3554 (MPa) | 0.9218 | 7.9442 (MPa) | 0.8981 | 6.5318 (MPa) | 0.7506 | 22.8337 (MPa) |
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Wang, S.; Tang, Y.; Cao, R.; Zhou, Z.; Cai, X. Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters. Mathematics 2022, 10, 628. https://doi.org/10.3390/math10040628
Wang S, Tang Y, Cao R, Zhou Z, Cai X. Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters. Mathematics. 2022; 10(4):628. https://doi.org/10.3390/math10040628
Chicago/Turabian StyleWang, Shaofeng, Yu Tang, Ruilang Cao, Zilong Zhou, and Xin Cai. 2022. "Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters" Mathematics 10, no. 4: 628. https://doi.org/10.3390/math10040628
APA StyleWang, S., Tang, Y., Cao, R., Zhou, Z., & Cai, X. (2022). Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters. Mathematics, 10(4), 628. https://doi.org/10.3390/math10040628