This notebook runs the modified Hagedorn-Brown correlation calculated according to Brown’s book and procedure in Appendix C.
library(rNodal)
library(tibble)
# Example from Prosper oil well 01. Dry version
# roughness = 0.0006
input_example <- setWellInput(field.name = "HAGBR.MOD",
well.name = "Oilwell_01_Dry",
depth.wh = 0, depth.bh = 9275,
diam.in = 4.052,
GLR = 800, liq.rt = 983, wcut = 0.0,
thp = 100, tht = 60, bht = 210,
API = 37, oil.visc = 5.0,
gas.sg = 0.76, wat.sg = 1.07, if.tens = 30,
salinity = 23000
)
well_model <- setVLPmodel(vlp.model = "hagbr.mod", segments = 29, tol = 0.00001)
as.tibble(runVLP(well.input = input_example, well_model))## Warning in get.well.slot(hFile, field.name, well.name):
## ./data folder does not exist. Creating a temporary file## Warning in get.well.slot(hFile, field.name, well.name): Creating a local ./data folder is advised## # A tibble: 30 x 45
## i depth dL temp pres p_avg t_avg segment
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0000 0.0000 60.00000 100.0000 100.0000 60.00000 0
## 2 2 319.8276 319.8276 65.17241 123.3668 111.6834 62.58621 1
## 3 3 639.6552 319.8276 70.34483 146.1362 134.7515 67.75862 2
## 4 4 959.4828 319.8276 75.51724 168.3186 157.2273 72.93103 3
## 5 5 1279.3103 319.8276 80.68966 190.0331 179.1758 78.10345 4
## 6 6 1599.1379 319.8276 85.86207 211.4121 200.7226 83.27586 5
## 7 7 1918.9655 319.8276 91.03448 232.5725 221.9923 88.44828 6
## 8 8 2238.7931 319.8276 96.20690 253.6108 243.0916 93.62069 7
## 9 9 2558.6207 319.8276 101.37931 274.5991 264.1042 98.79310 8
## 10 10 2878.4483 319.8276 106.55172 295.6000 285.0993 103.96552 9
## # ... with 20 more rows, and 37 more variables: GOR <dbl>, Rs <dbl>,
## # gas.fvf <dbl>, gas.free <dbl>, liq.dens <dbl>, z <dbl>,
## # gas.dens <dbl>, oil.visc <dbl>, wat.visc <dbl>, mixL.visc <dbl>,
## # oil.fvf <dbl>, wat.fvf <dbl>, liq.svel <dbl>, gas.svel <dbl>,
## # NL <dbl>, CNL <dbl>, NLV <dbl>, NGV <dbl>, A <dbl>, B <dbl>, BA <dbl>,
## # ND <dbl>, X2 <dbl>, HL.psi <dbl>, X2.mod <dbl>, psi <dbl>, HL <dbl>,
## # Re.TP <dbl>, ff <dbl>, mix.dens <dbl>, mixL.volume <dbl>,
## # mixL.dens <dbl>, mixTP.dens <dbl>, mixTP.svel <dbl>, elev.grad <dbl>,
## # fric.grad <dbl>, dp.dz <dbl>p30 = 740.1965MD TVD Pres Temp Gradient
0 0 100.0 70.0
250.0 250.0 116.7 74.0 0.066728 0.17564
500.0 500.0 133.7 78.0 0.068134 0.17885
750.0 750.0 151.1 82.1 0.069378 0.18161
1000.0 1000.0 168.7 86.1 0.070492 0.18399
1000.0 1000.0 168.7 86.1 0.01
1243.4 1243.3 186.1 90.1 0.07149 0.18607
1486.8 1486.7 203.7 94.0 0.072389 0.18788
1730.1 1730.1 221.5 97.9 0.073217 0.18951
1973.5 1973.5 239.5 101.9 0.073984 0.19097
2216.9 2216.9 257.7 105.8 0.074696 0.19229
2460.3 2460.2 276.1 109.7 0.075361 0.19349
2703.7 2703.6 294.6 113.7 0.075984 0.19458
2947.0 2947.0 313.2 117.6 0.076568 0.19558
3190.4 3190.4 332.0 121.5 0.077188 0.1967
3433.8 3433.8 350.9 125.5 0.077845 0.19793
3677.2 3677.1 370.0 129.4 0.078473 0.1991
3920.6 3920.5 389.3 133.4 0.079073 0.20021
4164.0 4163.9 408.6 137.3 0.079649 0.20125
4407.3 4407.3 428.2 141.2 0.080201 0.20225
4650.7 4650.7 447.8 145.2 0.080732 0.20319
4894.1 4894.0 467.6 149.1 0.081241 0.2041
5137.5 5137.4 488.0 153.0 0.08366 0.21069
5380.9 5380.8 509.0 156.9 0.086341 0.2181
5624.2 5624.2 530.6 160.9 0.089066 0.22568
5867.6 5867.6 553.0 164.8 0.091835 0.23342
6111.0 6110.9 576.0 168.7 0.094647 0.24133
6354.4 6354.3 599.8 172.6 0.097499 0.24941
6597.8 6597.7 624.2 176.5 0.10039 0.25764
6841.1 6841.1 649.3 180.4 0.10332 0.26604
7084.5 7084.5 675.2 184.2 0.10629 0.27461
7327.9 7327.9 701.8 188.0 0.1093 0.28334
7571.3 7571.2 729.1 191.8 0.11234 0.29225
7814.7 7814.6 757.2 195.4 0.11544 0.30135
8058.0 8058.0 786.1 198.9 0.11858 0.31064
8301.4 8301.4 815.7 202.2 0.12179 0.32017
8544.8 8544.8 846.2 205.1 0.12508 0.32996
8788.2 8788.1 877.5 207.6 0.12848 0.3401
9031.6 9031.5 909.6 209.3 0.13204 0.35069
9275.0 9274.9 942.7 210.0 0.13584 0.36189Type MD ID(in) Roughness(in)
Xmas Tree 600
Tubing 1000 4.052 0.0006
SSSV 3.72
Tubing 9000 4.052 0.0006
Casing 9275 6.4 0.0006
TVD MD FormTemp(deg F)
1 0 0 60
2 600 600 40
3 9000 9275 210
Date Comments THP THT WC LIQRT GDepth GPres ResPr GOR GOR Free
1 03/16/2011 Test 1 230 143.8 0 9784.1 6250 1322.6 4042.19 800 0
2 05/21/2011 Test 2 521 134.2 0.5 7915.3 6250 1623.8 3910.16 800 0
3 10/07/2011 Test 3 765 118 1.9 5636.9 6250 1962.6 3778.13 800 0 MD TVD CumDisp(ft) Angle(deg)
1 0 0 0 0
2 600 600 0 0
3 1005 1000 63.4429 9.01245
4 4075 4000 715.286 12.2587
5 7700 7500 1659.02 15.0902
6 9275 9000 2139.25 17.7528# in Prosper the angle is measured againt the vertical
library(rNodal)
md_tvd_01 <- "
MD TVD
0 0
600 600
1005 1000
4075 4000
7700 7500
9275 9000
"
md_tvd <- set_deviation_survey(md_tvd_01)
md_tvd## MD TVD
## 1 0 0
## 2 600 600
## 3 1005 1000
## 4 4075 4000
## 5 7700 7500
## 6 9275 9000deviation_survey <- compute_angle_deviation_survey(md_tvd, reference = "vertical")
dataFrame <- deviation_survey
dataFrame## MD TVD delta.md delta.tvd radians disp cum_disp degrees
## 1 0 0 0 0 0.0000000 0.000 0.000 0.000000
## 2 600 600 600 600 0.0000000 0.000 0.000 0.000000
## 3 1005 1000 405 400 0.1572970 63.443 63.443 9.012451
## 4 4075 4000 3070 3000 0.2139555 651.844 715.287 12.258749
## 5 7700 7500 3625 3500 0.2633734 943.729 1659.016 15.090185
## 6 9275 9000 1575 1500 0.3098446 480.234 2139.250 17.752790# split deviated well in two ways: by and length.out
library(rNodal)
md <- deviation_survey[["MD"]] # get MD vector
add_md_by <- rNodal:::split_well_in_deltas(md, by = 50)
add_md_by## [1] 0 50 100 150 200 250 300 350 400 450 500 550 600 650
## [15] 700 750 800 850 900 950 1000 1005 1050 1100 1150 1200 1250 1300
## [29] 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
## [43] 2050 2100 2150 2200 2250 2300 2350 2400 2450 2500 2550 2600 2650 2700
## [57] 2750 2800 2850 2900 2950 3000 3050 3100 3150 3200 3250 3300 3350 3400
## [71] 3450 3500 3550 3600 3650 3700 3750 3800 3850 3900 3950 4000 4050 4075
## [85] 4100 4150 4200 4250 4300 4350 4400 4450 4500 4550 4600 4650 4700 4750
## [99] 4800 4850 4900 4950 5000 5050 5100 5150 5200 5250 5300 5350 5400 5450
## [113] 5500 5550 5600 5650 5700 5750 5800 5850 5900 5950 6000 6050 6100 6150
## [127] 6200 6250 6300 6350 6400 6450 6500 6550 6600 6650 6700 6750 6800 6850
## [141] 6900 6950 7000 7050 7100 7150 7200 7250 7300 7350 7400 7450 7500 7550
## [155] 7600 7650 7700 7750 7800 7850 7900 7950 8000 8050 8100 8150 8200 8250
## [169] 8300 8350 8400 8450 8500 8550 8600 8650 8700 8750 8800 8850 8900 8950
## [183] 9000 9050 9100 9150 9200 9250 9275# split deviated well in two ways: by and length.out
library(rNodal)
md <- deviation_survey[["MD"]] # get MD vector
add_md_lo <- rNodal:::split_well_in_deltas(md, length.out = 40)
add_md_lo## [1] 0.0000 237.8205 475.6410 600.0000 713.4615 951.2821 1005.0000
## [8] 1189.1026 1426.9231 1664.7436 1902.5641 2140.3846 2378.2051 2616.0256
## [15] 2853.8462 3091.6667 3329.4872 3567.3077 3805.1282 4042.9487 4075.0000
## [22] 4280.7692 4518.5897 4756.4103 4994.2308 5232.0513 5469.8718 5707.6923
## [29] 5945.5128 6183.3333 6421.1538 6658.9744 6896.7949 7134.6154 7372.4359
## [36] 7610.2564 7700.0000 7848.0769 8085.8974 8323.7179 8561.5385 8799.3590
## [43] 9037.1795 9275.0000rNodal:::build_survey_with_deltas(deviation_survey, add_md_by)## md seg delta.md radians degrees delta.tvd tvd
## 1 0 1 0 0.0000000 0.000000 0.000000 0.0000
## 2 50 2 50 0.0000000 0.000000 50.000000 50.0000
## 3 100 2 50 0.0000000 0.000000 50.000000 100.0000
## 4 150 2 50 0.0000000 0.000000 50.000000 150.0000
## 5 200 2 50 0.0000000 0.000000 50.000000 200.0000
## 6 250 2 50 0.0000000 0.000000 50.000000 250.0000
## 7 300 2 50 0.0000000 0.000000 50.000000 300.0000
## 8 350 2 50 0.0000000 0.000000 50.000000 350.0000
## 9 400 2 50 0.0000000 0.000000 50.000000 400.0000
## 10 450 2 50 0.0000000 0.000000 50.000000 450.0000
## 11 500 2 50 0.0000000 0.000000 50.000000 500.0000
## 12 550 2 50 0.0000000 0.000000 50.000000 550.0000
## 13 600 2 50 0.0000000 0.000000 50.000000 600.0000
## 14 650 3 50 0.1572970 9.012451 49.382716 649.3827
## 15 700 3 50 0.1572970 9.012451 49.382716 698.7654
## 16 750 3 50 0.1572970 9.012451 49.382716 748.1481
## 17 800 3 50 0.1572970 9.012451 49.382716 797.5309
## 18 850 3 50 0.1572970 9.012451 49.382716 846.9136
## 19 900 3 50 0.1572970 9.012451 49.382716 896.2963
## 20 950 3 50 0.1572970 9.012451 49.382716 945.6790
## 21 1000 3 50 0.1572970 9.012451 49.382716 995.0617
## 22 1005 3 5 0.1572970 9.012451 4.938272 1000.0000
## 23 1050 4 45 0.2139555 12.258749 43.973941 1043.9739
## 24 1100 4 50 0.2139555 12.258749 48.859935 1092.8339
## 25 1150 4 50 0.2139555 12.258749 48.859935 1141.6938
## 26 1200 4 50 0.2139555 12.258749 48.859935 1190.5537
## 27 1250 4 50 0.2139555 12.258749 48.859935 1239.4137
## 28 1300 4 50 0.2139555 12.258749 48.859935 1288.2736
## 29 1350 4 50 0.2139555 12.258749 48.859935 1337.1336
## 30 1400 4 50 0.2139555 12.258749 48.859935 1385.9935
## 31 1450 4 50 0.2139555 12.258749 48.859935 1434.8534
## 32 1500 4 50 0.2139555 12.258749 48.859935 1483.7134
## 33 1550 4 50 0.2139555 12.258749 48.859935 1532.5733
## 34 1600 4 50 0.2139555 12.258749 48.859935 1581.4332
## 35 1650 4 50 0.2139555 12.258749 48.859935 1630.2932
## 36 1700 4 50 0.2139555 12.258749 48.859935 1679.1531
## 37 1750 4 50 0.2139555 12.258749 48.859935 1728.0130
## 38 1800 4 50 0.2139555 12.258749 48.859935 1776.8730
## 39 1850 4 50 0.2139555 12.258749 48.859935 1825.7329
## 40 1900 4 50 0.2139555 12.258749 48.859935 1874.5928
## 41 1950 4 50 0.2139555 12.258749 48.859935 1923.4528
## 42 2000 4 50 0.2139555 12.258749 48.859935 1972.3127
## 43 2050 4 50 0.2139555 12.258749 48.859935 2021.1726
## 44 2100 4 50 0.2139555 12.258749 48.859935 2070.0326
## 45 2150 4 50 0.2139555 12.258749 48.859935 2118.8925
## 46 2200 4 50 0.2139555 12.258749 48.859935 2167.7524
## 47 2250 4 50 0.2139555 12.258749 48.859935 2216.6124
## 48 2300 4 50 0.2139555 12.258749 48.859935 2265.4723
## 49 2350 4 50 0.2139555 12.258749 48.859935 2314.3322
## 50 2400 4 50 0.2139555 12.258749 48.859935 2363.1922
## 51 2450 4 50 0.2139555 12.258749 48.859935 2412.0521
## 52 2500 4 50 0.2139555 12.258749 48.859935 2460.9121
## 53 2550 4 50 0.2139555 12.258749 48.859935 2509.7720
## 54 2600 4 50 0.2139555 12.258749 48.859935 2558.6319
## 55 2650 4 50 0.2139555 12.258749 48.859935 2607.4919
## 56 2700 4 50 0.2139555 12.258749 48.859935 2656.3518
## 57 2750 4 50 0.2139555 12.258749 48.859935 2705.2117
## 58 2800 4 50 0.2139555 12.258749 48.859935 2754.0717
## 59 2850 4 50 0.2139555 12.258749 48.859935 2802.9316
## 60 2900 4 50 0.2139555 12.258749 48.859935 2851.7915
## 61 2950 4 50 0.2139555 12.258749 48.859935 2900.6515
## 62 3000 4 50 0.2139555 12.258749 48.859935 2949.5114
## 63 3050 4 50 0.2139555 12.258749 48.859935 2998.3713
## 64 3100 4 50 0.2139555 12.258749 48.859935 3047.2313
## 65 3150 4 50 0.2139555 12.258749 48.859935 3096.0912
## 66 3200 4 50 0.2139555 12.258749 48.859935 3144.9511
## 67 3250 4 50 0.2139555 12.258749 48.859935 3193.8111
## 68 3300 4 50 0.2139555 12.258749 48.859935 3242.6710
## 69 3350 4 50 0.2139555 12.258749 48.859935 3291.5309
## 70 3400 4 50 0.2139555 12.258749 48.859935 3340.3909
## 71 3450 4 50 0.2139555 12.258749 48.859935 3389.2508
## 72 3500 4 50 0.2139555 12.258749 48.859935 3438.1107
## 73 3550 4 50 0.2139555 12.258749 48.859935 3486.9707
## 74 3600 4 50 0.2139555 12.258749 48.859935 3535.8306
## 75 3650 4 50 0.2139555 12.258749 48.859935 3584.6906
## 76 3700 4 50 0.2139555 12.258749 48.859935 3633.5505
## 77 3750 4 50 0.2139555 12.258749 48.859935 3682.4104
## 78 3800 4 50 0.2139555 12.258749 48.859935 3731.2704
## 79 3850 4 50 0.2139555 12.258749 48.859935 3780.1303
## 80 3900 4 50 0.2139555 12.258749 48.859935 3828.9902
## 81 3950 4 50 0.2139555 12.258749 48.859935 3877.8502
## 82 4000 4 50 0.2139555 12.258749 48.859935 3926.7101
## 83 4050 4 50 0.2139555 12.258749 48.859935 3975.5700
## 84 4075 4 25 0.2139555 12.258749 24.429967 4000.0000
## 85 4100 5 25 0.2633734 15.090185 24.137931 4024.1379
## 86 4150 5 50 0.2633734 15.090185 48.275862 4072.4138
## 87 4200 5 50 0.2633734 15.090185 48.275862 4120.6897
## 88 4250 5 50 0.2633734 15.090185 48.275862 4168.9655
## 89 4300 5 50 0.2633734 15.090185 48.275862 4217.2414
## 90 4350 5 50 0.2633734 15.090185 48.275862 4265.5172
## 91 4400 5 50 0.2633734 15.090185 48.275862 4313.7931
## 92 4450 5 50 0.2633734 15.090185 48.275862 4362.0690
## 93 4500 5 50 0.2633734 15.090185 48.275862 4410.3448
## 94 4550 5 50 0.2633734 15.090185 48.275862 4458.6207
## 95 4600 5 50 0.2633734 15.090185 48.275862 4506.8966
## 96 4650 5 50 0.2633734 15.090185 48.275862 4555.1724
## 97 4700 5 50 0.2633734 15.090185 48.275862 4603.4483
## 98 4750 5 50 0.2633734 15.090185 48.275862 4651.7241
## 99 4800 5 50 0.2633734 15.090185 48.275862 4700.0000
## 100 4850 5 50 0.2633734 15.090185 48.275862 4748.2759
## 101 4900 5 50 0.2633734 15.090185 48.275862 4796.5517
## 102 4950 5 50 0.2633734 15.090185 48.275862 4844.8276
## 103 5000 5 50 0.2633734 15.090185 48.275862 4893.1034
## 104 5050 5 50 0.2633734 15.090185 48.275862 4941.3793
## 105 5100 5 50 0.2633734 15.090185 48.275862 4989.6552
## 106 5150 5 50 0.2633734 15.090185 48.275862 5037.9310
## 107 5200 5 50 0.2633734 15.090185 48.275862 5086.2069
## 108 5250 5 50 0.2633734 15.090185 48.275862 5134.4828
## 109 5300 5 50 0.2633734 15.090185 48.275862 5182.7586
## 110 5350 5 50 0.2633734 15.090185 48.275862 5231.0345
## 111 5400 5 50 0.2633734 15.090185 48.275862 5279.3103
## 112 5450 5 50 0.2633734 15.090185 48.275862 5327.5862
## 113 5500 5 50 0.2633734 15.090185 48.275862 5375.8621
## 114 5550 5 50 0.2633734 15.090185 48.275862 5424.1379
## 115 5600 5 50 0.2633734 15.090185 48.275862 5472.4138
## 116 5650 5 50 0.2633734 15.090185 48.275862 5520.6897
## 117 5700 5 50 0.2633734 15.090185 48.275862 5568.9655
## 118 5750 5 50 0.2633734 15.090185 48.275862 5617.2414
## 119 5800 5 50 0.2633734 15.090185 48.275862 5665.5172
## 120 5850 5 50 0.2633734 15.090185 48.275862 5713.7931
## 121 5900 5 50 0.2633734 15.090185 48.275862 5762.0690
## 122 5950 5 50 0.2633734 15.090185 48.275862 5810.3448
## 123 6000 5 50 0.2633734 15.090185 48.275862 5858.6207
## 124 6050 5 50 0.2633734 15.090185 48.275862 5906.8966
## 125 6100 5 50 0.2633734 15.090185 48.275862 5955.1724
## 126 6150 5 50 0.2633734 15.090185 48.275862 6003.4483
## 127 6200 5 50 0.2633734 15.090185 48.275862 6051.7241
## 128 6250 5 50 0.2633734 15.090185 48.275862 6100.0000
## 129 6300 5 50 0.2633734 15.090185 48.275862 6148.2759
## 130 6350 5 50 0.2633734 15.090185 48.275862 6196.5517
## 131 6400 5 50 0.2633734 15.090185 48.275862 6244.8276
## 132 6450 5 50 0.2633734 15.090185 48.275862 6293.1034
## 133 6500 5 50 0.2633734 15.090185 48.275862 6341.3793
## 134 6550 5 50 0.2633734 15.090185 48.275862 6389.6552
## 135 6600 5 50 0.2633734 15.090185 48.275862 6437.9310
## 136 6650 5 50 0.2633734 15.090185 48.275862 6486.2069
## 137 6700 5 50 0.2633734 15.090185 48.275862 6534.4828
## 138 6750 5 50 0.2633734 15.090185 48.275862 6582.7586
## 139 6800 5 50 0.2633734 15.090185 48.275862 6631.0345
## 140 6850 5 50 0.2633734 15.090185 48.275862 6679.3103
## 141 6900 5 50 0.2633734 15.090185 48.275862 6727.5862
## 142 6950 5 50 0.2633734 15.090185 48.275862 6775.8621
## 143 7000 5 50 0.2633734 15.090185 48.275862 6824.1379
## 144 7050 5 50 0.2633734 15.090185 48.275862 6872.4138
## 145 7100 5 50 0.2633734 15.090185 48.275862 6920.6897
## 146 7150 5 50 0.2633734 15.090185 48.275862 6968.9655
## 147 7200 5 50 0.2633734 15.090185 48.275862 7017.2414
## 148 7250 5 50 0.2633734 15.090185 48.275862 7065.5172
## 149 7300 5 50 0.2633734 15.090185 48.275862 7113.7931
## 150 7350 5 50 0.2633734 15.090185 48.275862 7162.0690
## 151 7400 5 50 0.2633734 15.090185 48.275862 7210.3448
## 152 7450 5 50 0.2633734 15.090185 48.275862 7258.6207
## 153 7500 5 50 0.2633734 15.090185 48.275862 7306.8966
## 154 7550 5 50 0.2633734 15.090185 48.275862 7355.1724
## 155 7600 5 50 0.2633734 15.090185 48.275862 7403.4483
## 156 7650 5 50 0.2633734 15.090185 48.275862 7451.7241
## 157 7700 5 50 0.2633734 15.090185 48.275862 7500.0000
## 158 7750 6 50 0.3098446 17.752790 47.619048 7547.6190
## 159 7800 6 50 0.3098446 17.752790 47.619048 7595.2381
## 160 7850 6 50 0.3098446 17.752790 47.619048 7642.8571
## 161 7900 6 50 0.3098446 17.752790 47.619048 7690.4762
## 162 7950 6 50 0.3098446 17.752790 47.619048 7738.0952
## 163 8000 6 50 0.3098446 17.752790 47.619048 7785.7143
## 164 8050 6 50 0.3098446 17.752790 47.619048 7833.3333
## 165 8100 6 50 0.3098446 17.752790 47.619048 7880.9524
## 166 8150 6 50 0.3098446 17.752790 47.619048 7928.5714
## 167 8200 6 50 0.3098446 17.752790 47.619048 7976.1905
## 168 8250 6 50 0.3098446 17.752790 47.619048 8023.8095
## 169 8300 6 50 0.3098446 17.752790 47.619048 8071.4286
## 170 8350 6 50 0.3098446 17.752790 47.619048 8119.0476
## 171 8400 6 50 0.3098446 17.752790 47.619048 8166.6667
## 172 8450 6 50 0.3098446 17.752790 47.619048 8214.2857
## 173 8500 6 50 0.3098446 17.752790 47.619048 8261.9048
## 174 8550 6 50 0.3098446 17.752790 47.619048 8309.5238
## 175 8600 6 50 0.3098446 17.752790 47.619048 8357.1429
## 176 8650 6 50 0.3098446 17.752790 47.619048 8404.7619
## 177 8700 6 50 0.3098446 17.752790 47.619048 8452.3810
## 178 8750 6 50 0.3098446 17.752790 47.619048 8500.0000
## 179 8800 6 50 0.3098446 17.752790 47.619048 8547.6190
## 180 8850 6 50 0.3098446 17.752790 47.619048 8595.2381
## 181 8900 6 50 0.3098446 17.752790 47.619048 8642.8571
## 182 8950 6 50 0.3098446 17.752790 47.619048 8690.4762
## 183 9000 6 50 0.3098446 17.752790 47.619048 8738.0952
## 184 9050 6 50 0.3098446 17.752790 47.619048 8785.7143
## 185 9100 6 50 0.3098446 17.752790 47.619048 8833.3333
## 186 9150 6 50 0.3098446 17.752790 47.619048 8880.9524
## 187 9200 6 50 0.3098446 17.752790 47.619048 8928.5714
## 188 9250 6 50 0.3098446 17.752790 47.619048 8976.1905
## 189 9275 6 25 0.3098446 17.752790 23.809524 9000.0000rNodal:::build_survey_with_deltas(deviation_survey, add_md_lo)## md seg delta.md radians degrees delta.tvd tvd
## 1 0.0000 1 0.00000 0.0000000 0.000000 0.00000 0.0000
## 2 237.8205 2 237.82051 0.0000000 0.000000 237.82051 237.8205
## 3 475.6410 2 237.82051 0.0000000 0.000000 237.82051 475.6410
## 4 600.0000 2 124.35897 0.0000000 0.000000 124.35897 600.0000
## 5 713.4615 3 113.46154 0.1572970 9.012451 112.06078 712.0608
## 6 951.2821 3 237.82051 0.1572970 9.012451 234.88446 946.9452
## 7 1005.0000 3 53.71795 0.1572970 9.012451 53.05476 1000.0000
## 8 1189.1026 4 184.10256 0.2139555 12.258749 179.90479 1179.9048
## 9 1426.9231 4 237.82051 0.2139555 12.258749 232.39790 1412.3027
## 10 1664.7436 4 237.82051 0.2139555 12.258749 232.39790 1644.7006
## 11 1902.5641 4 237.82051 0.2139555 12.258749 232.39790 1877.0985
## 12 2140.3846 4 237.82051 0.2139555 12.258749 232.39790 2109.4964
## 13 2378.2051 4 237.82051 0.2139555 12.258749 232.39790 2341.8943
## 14 2616.0256 4 237.82051 0.2139555 12.258749 232.39790 2574.2922
## 15 2853.8462 4 237.82051 0.2139555 12.258749 232.39790 2806.6901
## 16 3091.6667 4 237.82051 0.2139555 12.258749 232.39790 3039.0879
## 17 3329.4872 4 237.82051 0.2139555 12.258749 232.39790 3271.4858
## 18 3567.3077 4 237.82051 0.2139555 12.258749 232.39790 3503.8837
## 19 3805.1282 4 237.82051 0.2139555 12.258749 232.39790 3736.2816
## 20 4042.9487 4 237.82051 0.2139555 12.258749 232.39790 3968.6795
## 21 4075.0000 4 32.05128 0.2139555 12.258749 31.32047 4000.0000
## 22 4280.7692 5 205.76923 0.2633734 15.090185 198.67374 4198.6737
## 23 4518.5897 5 237.82051 0.2633734 15.090185 229.61981 4428.2935
## 24 4756.4103 5 237.82051 0.2633734 15.090185 229.61981 4657.9134
## 25 4994.2308 5 237.82051 0.2633734 15.090185 229.61981 4887.5332
## 26 5232.0513 5 237.82051 0.2633734 15.090185 229.61981 5117.1530
## 27 5469.8718 5 237.82051 0.2633734 15.090185 229.61981 5346.7728
## 28 5707.6923 5 237.82051 0.2633734 15.090185 229.61981 5576.3926
## 29 5945.5128 5 237.82051 0.2633734 15.090185 229.61981 5806.0124
## 30 6183.3333 5 237.82051 0.2633734 15.090185 229.61981 6035.6322
## 31 6421.1538 5 237.82051 0.2633734 15.090185 229.61981 6265.2520
## 32 6658.9744 5 237.82051 0.2633734 15.090185 229.61981 6494.8718
## 33 6896.7949 5 237.82051 0.2633734 15.090185 229.61981 6724.4916
## 34 7134.6154 5 237.82051 0.2633734 15.090185 229.61981 6954.1114
## 35 7372.4359 5 237.82051 0.2633734 15.090185 229.61981 7183.7312
## 36 7610.2564 5 237.82051 0.2633734 15.090185 229.61981 7413.3510
## 37 7700.0000 5 89.74359 0.2633734 15.090185 86.64898 7500.0000
## 38 7848.0769 6 148.07692 0.3098446 17.752790 141.02564 7641.0256
## 39 8085.8974 6 237.82051 0.3098446 17.752790 226.49573 7867.5214
## 40 8323.7179 6 237.82051 0.3098446 17.752790 226.49573 8094.0171
## 41 8561.5385 6 237.82051 0.3098446 17.752790 226.49573 8320.5128
## 42 8799.3590 6 237.82051 0.3098446 17.752790 226.49573 8547.0085
## 43 9037.1795 6 237.82051 0.3098446 17.752790 226.49573 8773.5043
## 44 9275.0000 6 237.82051 0.3098446 17.752790 226.49573 9000.0000# split the MD of the well in equal parts but a total of "n" segments
split <- seq.int(deviation_survey[1, "MD"], deviation_survey[nrow(deviation_survey), "MD"],
length.out = 100)
# add the known MD values to the sequence. Now the length is little bit longer
md <- deviation_survey[["MD"]]
add_md <- sort(unique(c(md, split)))
add_md## [1] 0.00000 93.68687 187.37374 281.06061 374.74747 468.43434
## [7] 562.12121 600.00000 655.80808 749.49495 843.18182 936.86869
## [13] 1005.00000 1030.55556 1124.24242 1217.92929 1311.61616 1405.30303
## [19] 1498.98990 1592.67677 1686.36364 1780.05051 1873.73737 1967.42424
## [25] 2061.11111 2154.79798 2248.48485 2342.17172 2435.85859 2529.54545
## [31] 2623.23232 2716.91919 2810.60606 2904.29293 2997.97980 3091.66667
## [37] 3185.35354 3279.04040 3372.72727 3466.41414 3560.10101 3653.78788
## [43] 3747.47475 3841.16162 3934.84848 4028.53535 4075.00000 4122.22222
## [49] 4215.90909 4309.59596 4403.28283 4496.96970 4590.65657 4684.34343
## [55] 4778.03030 4871.71717 4965.40404 5059.09091 5152.77778 5246.46465
## [61] 5340.15152 5433.83838 5527.52525 5621.21212 5714.89899 5808.58586
## [67] 5902.27273 5995.95960 6089.64646 6183.33333 6277.02020 6370.70707
## [73] 6464.39394 6558.08081 6651.76768 6745.45455 6839.14141 6932.82828
## [79] 7026.51515 7120.20202 7213.88889 7307.57576 7401.26263 7494.94949
## [85] 7588.63636 7682.32323 7700.00000 7776.01010 7869.69697 7963.38384
## [91] 8057.07071 8150.75758 8244.44444 8338.13131 8431.81818 8525.50505
## [97] 8619.19192 8712.87879 8806.56566 8900.25253 8993.93939 9087.62626
## [103] 9181.31313 9275.00000# reconstruct MD v TVD but for the partitioned well in delta-x
df <- data.frame() # new long dataframe
index <- 1 # index the small dataframe
tvd <- 0
for (j in 1:length(add_md)) { # iterate through the sequence
row = dataFrame[index, ] # get a row of the deviation survey
# cat(index)
df[j, "md"] <- add_md[j] # assign MD in sequence to md in long dataframe
df[j, "seg"] <- index # assign
if (j == 1) # if it is the first row
df[j, "delta.md"] <- add_md[j]
else
df[j, "delta.md"] <- add_md[j] - df[j-1, "md"]
df[j, "radians"] <- row[["radians"]]
df[j, "degrees"] <- row[["degrees"]]
df[j, "delta.tvd"] <- cos(row[["radians"]]) * df[j, "delta.md"] # calculate delta TVD
tvd <- tvd + df[j, "delta.tvd"] # add delta.tvd
df[j, "tvd"] <- tvd # tvd column
if (add_md[j] >= row[["MD"]]) { # switch to next deviation branch
index <- index + 1
}
}
df## md seg delta.md radians degrees delta.tvd tvd
## 1 0.00000 1 0.00000 0.0000000 0.000000 0.00000 0.00000
## 2 93.68687 2 93.68687 0.0000000 0.000000 93.68687 93.68687
## 3 187.37374 2 93.68687 0.0000000 0.000000 93.68687 187.37374
## 4 281.06061 2 93.68687 0.0000000 0.000000 93.68687 281.06061
## 5 374.74747 2 93.68687 0.0000000 0.000000 93.68687 374.74747
## 6 468.43434 2 93.68687 0.0000000 0.000000 93.68687 468.43434
## 7 562.12121 2 93.68687 0.0000000 0.000000 93.68687 562.12121
## 8 600.00000 2 37.87879 0.0000000 0.000000 37.87879 600.00000
## 9 655.80808 3 55.80808 0.1572970 9.012451 55.11909 655.11909
## 10 749.49495 3 93.68687 0.1572970 9.012451 92.53024 747.64933
## 11 843.18182 3 93.68687 0.1572970 9.012451 92.53024 840.17957
## 12 936.86869 3 93.68687 0.1572970 9.012451 92.53024 932.70981
## 13 1005.00000 3 68.13131 0.1572970 9.012451 67.29019 1000.00000
## 14 1030.55556 4 25.55556 0.2139555 12.258749 24.97286 1024.97286
## 15 1124.24242 4 93.68687 0.2139555 12.258749 91.55069 1116.52354
## 16 1217.92929 4 93.68687 0.2139555 12.258749 91.55069 1208.07423
## 17 1311.61616 4 93.68687 0.2139555 12.258749 91.55069 1299.62491
## 18 1405.30303 4 93.68687 0.2139555 12.258749 91.55069 1391.17560
## 19 1498.98990 4 93.68687 0.2139555 12.258749 91.55069 1482.72629
## 20 1592.67677 4 93.68687 0.2139555 12.258749 91.55069 1574.27697
## 21 1686.36364 4 93.68687 0.2139555 12.258749 91.55069 1665.82766
## 22 1780.05051 4 93.68687 0.2139555 12.258749 91.55069 1757.37834
## 23 1873.73737 4 93.68687 0.2139555 12.258749 91.55069 1848.92903
## 24 1967.42424 4 93.68687 0.2139555 12.258749 91.55069 1940.47972
## 25 2061.11111 4 93.68687 0.2139555 12.258749 91.55069 2032.03040
## 26 2154.79798 4 93.68687 0.2139555 12.258749 91.55069 2123.58109
## 27 2248.48485 4 93.68687 0.2139555 12.258749 91.55069 2215.13177
## 28 2342.17172 4 93.68687 0.2139555 12.258749 91.55069 2306.68246
## 29 2435.85859 4 93.68687 0.2139555 12.258749 91.55069 2398.23315
## 30 2529.54545 4 93.68687 0.2139555 12.258749 91.55069 2489.78383
## 31 2623.23232 4 93.68687 0.2139555 12.258749 91.55069 2581.33452
## 32 2716.91919 4 93.68687 0.2139555 12.258749 91.55069 2672.88520
## 33 2810.60606 4 93.68687 0.2139555 12.258749 91.55069 2764.43589
## 34 2904.29293 4 93.68687 0.2139555 12.258749 91.55069 2855.98658
## 35 2997.97980 4 93.68687 0.2139555 12.258749 91.55069 2947.53726
## 36 3091.66667 4 93.68687 0.2139555 12.258749 91.55069 3039.08795
## 37 3185.35354 4 93.68687 0.2139555 12.258749 91.55069 3130.63863
## 38 3279.04040 4 93.68687 0.2139555 12.258749 91.55069 3222.18932
## 39 3372.72727 4 93.68687 0.2139555 12.258749 91.55069 3313.74001
## 40 3466.41414 4 93.68687 0.2139555 12.258749 91.55069 3405.29069
## 41 3560.10101 4 93.68687 0.2139555 12.258749 91.55069 3496.84138
## 42 3653.78788 4 93.68687 0.2139555 12.258749 91.55069 3588.39206
## 43 3747.47475 4 93.68687 0.2139555 12.258749 91.55069 3679.94275
## 44 3841.16162 4 93.68687 0.2139555 12.258749 91.55069 3771.49344
## 45 3934.84848 4 93.68687 0.2139555 12.258749 91.55069 3863.04412
## 46 4028.53535 4 93.68687 0.2139555 12.258749 91.55069 3954.59481
## 47 4075.00000 4 46.46465 0.2139555 12.258749 45.40519 4000.00000
## 48 4122.22222 5 47.22222 0.2633734 15.090185 45.59387 4045.59387
## 49 4215.90909 5 93.68687 0.2633734 15.090185 90.45629 4136.05016
## 50 4309.59596 5 93.68687 0.2633734 15.090185 90.45629 4226.50644
## 51 4403.28283 5 93.68687 0.2633734 15.090185 90.45629 4316.96273
## 52 4496.96970 5 93.68687 0.2633734 15.090185 90.45629 4407.41902
## 53 4590.65657 5 93.68687 0.2633734 15.090185 90.45629 4497.87530
## 54 4684.34343 5 93.68687 0.2633734 15.090185 90.45629 4588.33159
## 55 4778.03030 5 93.68687 0.2633734 15.090185 90.45629 4678.78788
## 56 4871.71717 5 93.68687 0.2633734 15.090185 90.45629 4769.24417
## 57 4965.40404 5 93.68687 0.2633734 15.090185 90.45629 4859.70045
## 58 5059.09091 5 93.68687 0.2633734 15.090185 90.45629 4950.15674
## 59 5152.77778 5 93.68687 0.2633734 15.090185 90.45629 5040.61303
## 60 5246.46465 5 93.68687 0.2633734 15.090185 90.45629 5131.06931
## 61 5340.15152 5 93.68687 0.2633734 15.090185 90.45629 5221.52560
## 62 5433.83838 5 93.68687 0.2633734 15.090185 90.45629 5311.98189
## 63 5527.52525 5 93.68687 0.2633734 15.090185 90.45629 5402.43817
## 64 5621.21212 5 93.68687 0.2633734 15.090185 90.45629 5492.89446
## 65 5714.89899 5 93.68687 0.2633734 15.090185 90.45629 5583.35075
## 66 5808.58586 5 93.68687 0.2633734 15.090185 90.45629 5673.80704
## 67 5902.27273 5 93.68687 0.2633734 15.090185 90.45629 5764.26332
## 68 5995.95960 5 93.68687 0.2633734 15.090185 90.45629 5854.71961
## 69 6089.64646 5 93.68687 0.2633734 15.090185 90.45629 5945.17590
## 70 6183.33333 5 93.68687 0.2633734 15.090185 90.45629 6035.63218
## 71 6277.02020 5 93.68687 0.2633734 15.090185 90.45629 6126.08847
## 72 6370.70707 5 93.68687 0.2633734 15.090185 90.45629 6216.54476
## 73 6464.39394 5 93.68687 0.2633734 15.090185 90.45629 6307.00104
## 74 6558.08081 5 93.68687 0.2633734 15.090185 90.45629 6397.45733
## 75 6651.76768 5 93.68687 0.2633734 15.090185 90.45629 6487.91362
## 76 6745.45455 5 93.68687 0.2633734 15.090185 90.45629 6578.36991
## 77 6839.14141 5 93.68687 0.2633734 15.090185 90.45629 6668.82619
## 78 6932.82828 5 93.68687 0.2633734 15.090185 90.45629 6759.28248
## 79 7026.51515 5 93.68687 0.2633734 15.090185 90.45629 6849.73877
## 80 7120.20202 5 93.68687 0.2633734 15.090185 90.45629 6940.19505
## 81 7213.88889 5 93.68687 0.2633734 15.090185 90.45629 7030.65134
## 82 7307.57576 5 93.68687 0.2633734 15.090185 90.45629 7121.10763
## 83 7401.26263 5 93.68687 0.2633734 15.090185 90.45629 7211.56392
## 84 7494.94949 5 93.68687 0.2633734 15.090185 90.45629 7302.02020
## 85 7588.63636 5 93.68687 0.2633734 15.090185 90.45629 7392.47649
## 86 7682.32323 5 93.68687 0.2633734 15.090185 90.45629 7482.93278
## 87 7700.00000 5 17.67677 0.2633734 15.090185 17.06722 7500.00000
## 88 7776.01010 6 76.01010 0.3098446 17.752790 72.39057 7572.39057
## 89 7869.69697 6 93.68687 0.3098446 17.752790 89.22559 7661.61616
## 90 7963.38384 6 93.68687 0.3098446 17.752790 89.22559 7750.84175
## 91 8057.07071 6 93.68687 0.3098446 17.752790 89.22559 7840.06734
## 92 8150.75758 6 93.68687 0.3098446 17.752790 89.22559 7929.29293
## 93 8244.44444 6 93.68687 0.3098446 17.752790 89.22559 8018.51852
## 94 8338.13131 6 93.68687 0.3098446 17.752790 89.22559 8107.74411
## 95 8431.81818 6 93.68687 0.3098446 17.752790 89.22559 8196.96970
## 96 8525.50505 6 93.68687 0.3098446 17.752790 89.22559 8286.19529
## 97 8619.19192 6 93.68687 0.3098446 17.752790 89.22559 8375.42088
## 98 8712.87879 6 93.68687 0.3098446 17.752790 89.22559 8464.64646
## 99 8806.56566 6 93.68687 0.3098446 17.752790 89.22559 8553.87205
## 100 8900.25253 6 93.68687 0.3098446 17.752790 89.22559 8643.09764
## 101 8993.93939 6 93.68687 0.3098446 17.752790 89.22559 8732.32323
## 102 9087.62626 6 93.68687 0.3098446 17.752790 89.22559 8821.54882
## 103 9181.31313 6 93.68687 0.3098446 17.752790 89.22559 8910.77441
## 104 9275.00000 6 93.68687 0.3098446 17.752790 89.22559 9000.00000# iterate through dataframe
for (index in 1:nrow(dataFrame)) {
row = dataFrame[index, ]
# do stuff with the row
# print(row[["MD"]])
cat(row[["MD"]], "\n")
} ## 0
## 600
## 1005
## 4075
## 7700
## 9275for (index in 1:nrow(dataFrame)) {
row = dataFrame[index, ]
# cat(row, "\n")
for (j in add_md) {
if (j <= row[["MD"]]) {
cat(sprintf("%12f %12f \n", j, row[["MD"]]))
# print(row[["MD"]][index] * sin(row[["radians"]][index]))
}
}
}## 0.000000 0.000000
## 0.000000 600.000000
## 93.686869 600.000000
## 187.373737 600.000000
## 281.060606 600.000000
## 374.747475 600.000000
## 468.434343 600.000000
## 562.121212 600.000000
## 600.000000 600.000000
## 0.000000 1005.000000
## 93.686869 1005.000000
## 187.373737 1005.000000
## 281.060606 1005.000000
## 374.747475 1005.000000
## 468.434343 1005.000000
## 562.121212 1005.000000
## 600.000000 1005.000000
## 655.808081 1005.000000
## 749.494949 1005.000000
## 843.181818 1005.000000
## 936.868687 1005.000000
## 1005.000000 1005.000000
## 0.000000 4075.000000
## 93.686869 4075.000000
## 187.373737 4075.000000
## 281.060606 4075.000000
## 374.747475 4075.000000
## 468.434343 4075.000000
## 562.121212 4075.000000
## 600.000000 4075.000000
## 655.808081 4075.000000
## 749.494949 4075.000000
## 843.181818 4075.000000
## 936.868687 4075.000000
## 1005.000000 4075.000000
## 1030.555556 4075.000000
## 1124.242424 4075.000000
## 1217.929293 4075.000000
## 1311.616162 4075.000000
## 1405.303030 4075.000000
## 1498.989899 4075.000000
## 1592.676768 4075.000000
## 1686.363636 4075.000000
## 1780.050505 4075.000000
## 1873.737374 4075.000000
## 1967.424242 4075.000000
## 2061.111111 4075.000000
## 2154.797980 4075.000000
## 2248.484848 4075.000000
## 2342.171717 4075.000000
## 2435.858586 4075.000000
## 2529.545455 4075.000000
## 2623.232323 4075.000000
## 2716.919192 4075.000000
## 2810.606061 4075.000000
## 2904.292929 4075.000000
## 2997.979798 4075.000000
## 3091.666667 4075.000000
## 3185.353535 4075.000000
## 3279.040404 4075.000000
## 3372.727273 4075.000000
## 3466.414141 4075.000000
## 3560.101010 4075.000000
## 3653.787879 4075.000000
## 3747.474747 4075.000000
## 3841.161616 4075.000000
## 3934.848485 4075.000000
## 4028.535354 4075.000000
## 4075.000000 4075.000000
## 0.000000 7700.000000
## 93.686869 7700.000000
## 187.373737 7700.000000
## 281.060606 7700.000000
## 374.747475 7700.000000
## 468.434343 7700.000000
## 562.121212 7700.000000
## 600.000000 7700.000000
## 655.808081 7700.000000
## 749.494949 7700.000000
## 843.181818 7700.000000
## 936.868687 7700.000000
## 1005.000000 7700.000000
## 1030.555556 7700.000000
## 1124.242424 7700.000000
## 1217.929293 7700.000000
## 1311.616162 7700.000000
## 1405.303030 7700.000000
## 1498.989899 7700.000000
## 1592.676768 7700.000000
## 1686.363636 7700.000000
## 1780.050505 7700.000000
## 1873.737374 7700.000000
## 1967.424242 7700.000000
## 2061.111111 7700.000000
## 2154.797980 7700.000000
## 2248.484848 7700.000000
## 2342.171717 7700.000000
## 2435.858586 7700.000000
## 2529.545455 7700.000000
## 2623.232323 7700.000000
## 2716.919192 7700.000000
## 2810.606061 7700.000000
## 2904.292929 7700.000000
## 2997.979798 7700.000000
## 3091.666667 7700.000000
## 3185.353535 7700.000000
## 3279.040404 7700.000000
## 3372.727273 7700.000000
## 3466.414141 7700.000000
## 3560.101010 7700.000000
## 3653.787879 7700.000000
## 3747.474747 7700.000000
## 3841.161616 7700.000000
## 3934.848485 7700.000000
## 4028.535354 7700.000000
## 4075.000000 7700.000000
## 4122.222222 7700.000000
## 4215.909091 7700.000000
## 4309.595960 7700.000000
## 4403.282828 7700.000000
## 4496.969697 7700.000000
## 4590.656566 7700.000000
## 4684.343434 7700.000000
## 4778.030303 7700.000000
## 4871.717172 7700.000000
## 4965.404040 7700.000000
## 5059.090909 7700.000000
## 5152.777778 7700.000000
## 5246.464646 7700.000000
## 5340.151515 7700.000000
## 5433.838384 7700.000000
## 5527.525253 7700.000000
## 5621.212121 7700.000000
## 5714.898990 7700.000000
## 5808.585859 7700.000000
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## 8993.939394 9275.000000
## 9087.626263 9275.000000
## 9181.313131 9275.000000
## 9275.000000 9275.000000# split the tubing in dx pieces
apply(deviation_survey, 1, function(x) x["MD"]
)## [1] 0 600 1005 4075 7700 9275