lasio - Log ASCII Standard (LAS) files in Python¶
This is a Python 2/3 package to read and write Log ASCII Standard (LAS) files, used for borehole data such as geophysical, geological, or petrophysical logs. It’s compatible with versions 1.2 and 2.0 of the LAS file specification, published by the Canadian Well Logging Society. In principle it is designed to read as many types of LAS files as possible, including ones containing common errors or non-compliant formatting.
Depending on your particular application you may also want to check out striplog for stratigraphic/lithological data, or (still in alpha dev) welly for dealing with data at the well level. lasio is primarily for reading & writing LAS files.
Note this is not a package for reading LiDAR data (also called “LAS files”).
Installation¶
lasio is written to be compatible with Python 2.6+, and 3.2+. The best
way to install is using pip.
(test) C:\Users\kent>pip install lasio
This will download and install lasio’s dependencies (numpy and ordereddict).
There are some other packages which lasio will use to provide extra functionality if they are installed (pandas, cChardet and/or chardet, openpyxl, and argparse). I recommend installing these too with:
(test) C:\Users\kent\Code\lasio>pip install -r optional-packages.txt
lasio is now installed. See the following pages for examples of how to use the package.
To upgrade to the latest PyPI version, use:
(test2) C:\Users\kent\Code\testing\lasio>pip install --upgrade lasio
Development version¶
Installing via pip gets the latest release which has been published on PyPI.
The source code for lasio is kept at:
https://github.com/kinverarity1/lasio
Updates are made much more frequently to the master branch here. If you have
Git installed, you can keep up to date with these changes:
(test2) C:\Users\kent\Code\testing>git clone https://github.com/kinverarity1/lasio
(test2) C:\Users\kent\Code\testing>cd lasio
(test2) C:\Users\kent\Code\testing\lasio>pip install -r requirements.txt
(test2) C:\Users\kent\Code\testing\lasio>python setup.py develop
To update your version with the latest changes on GitHub:
(test2) C:\Users\kent\Code\testing\lasio>git pull origin master
Testing¶
Every time lasio is updated, automated tests are run:
Travis CI: Linux, Python versions 2.7, 3.3, 3.4, 3.5, and 3.6.Appveyor CI: Windows, Python versions 2.7, 3.4, 3.5, and 3.6.
lasio should also work on Python 2.6 and 3.2, but these are tested only
occassionally.
To run tests yourself, first install the testing framework and all the optional packages:
(test2) C:\Users\kent\Code\testing\lasio>pip install pytest
(test2) C:\Users\kent\Code\testing\lasio>pip install -r optional-packages.txt
And then run tests:
(test2) C:\Users\kent\Code\testing\lasio>py.test
Basic example¶
In the example below you can see how to:
- read a LAS file in
- look at the information in the header
- see basic curve information
- make a graph
In [29]: import lasio
In [30]: las = lasio.read(r"C:\Users\kent\Code\las\examples\2.0\49-005-30258.las")
In [31]: las.header
Out[31]:
{'Curves': [CurveItem(mnemonic=DEPT, unit=F, value=, descr=1 DEPTH, original_mnemonic=DEPT, data.shape=(235,)),
CurveItem(mnemonic=DT, unit=US/F, value=, descr=2 SONIC DELTA-T, original_mnemonic=DT, data.shape=(235,)),
CurveItem(mnemonic=RESD, unit=OHMM, value=, descr=3 DEEP RESISTIVITY, original_mnemonic=RESD, data.shape=(235,)),
CurveItem(mnemonic=SP, unit=MV, value=, descr=4 SP CURVE, original_mnemonic=SP, data.shape=(235,)),
CurveItem(mnemonic=GR, unit=GAPI, value=, descr=5 GAMMA RAY, original_mnemonic=GR, data.shape=(235,))],
'Other': '',
'Parameter': [HeaderItem(mnemonic=BHT, unit=DEGF, value=194.0, descr=BOTTOM HOLE TEMPERATURE, original_mnemonic=BHT),
HeaderItem(mnemonic=RMF, unit=OHMM, value=0.441, descr=MUD FILTRATE RESISTIVITY, original_mnemonic=RMF),
HeaderItem(mnemonic=RMFT, unit=DEGF, value=68.0, descr=MEASURE TEMPERATURE OF RMF, original_mnemonic=RMFT),
HeaderItem(mnemonic=EKB, unit=F, value=4642.0, descr=ELEVATION KELLY BUSHING, original_mnemonic=EKB),
HeaderItem(mnemonic=SECT, unit=, value=36, descr=SECTION, original_mnemonic=SECT),
HeaderItem(mnemonic=TOWN, unit=, value=47N, descr=TOWNSHIP, original_mnemonic=TOWN),
HeaderItem(mnemonic=RANG, unit=, value=71W, descr=RANGE, original_mnemonic=RANG)],
'Version': [HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS log ASCII Standard - Version 2.0, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=One Line per Depth Step, original_mnemonic=WRAP),
HeaderItem(mnemonic=CREA, unit=, value=02-08-2006, descr=LAS File Creation Date (MM-DD-YYYY), original_mnemonic=CREA)],
'Well': [HeaderItem(mnemonic=STRT, unit=F, value=10180.0, descr=START DEPTH, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=F, value=10414.0, descr=STOP DEPTH, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=F, value=1.0, descr=STEP, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-999.25, descr=NULL VALUE, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=Cramer Oil, descr=COMPANY, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=#36-16 State, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=LOC, unit=, value=SE SE 36-47N-71W, descr=LOCATION, original_mnemonic=LOC),
HeaderItem(mnemonic=CNTY, unit=, value=Campbell, descr=COUNTY, original_mnemonic=CNTY),
HeaderItem(mnemonic=FLD, unit=, value=, descr=FIELD, original_mnemonic=FLD),
HeaderItem(mnemonic=STAT, unit=, value=Wyoming, descr=STATE, original_mnemonic=STAT),
HeaderItem(mnemonic=CTRY, unit=, value=U.S.A., descr=COUNTRY, original_mnemonic=CTRY),
HeaderItem(mnemonic=DATE, unit=, value=11/91, descr=COMPLETION DATE (MM/YY), original_mnemonic=DATE),
HeaderItem(mnemonic=API, unit=, value=49-005-30258-0000, descr=API NUMBER, original_mnemonic=API),
HeaderItem(mnemonic=SRVC, unit=, value=, descr=SERVICE COMPANY, original_mnemonic=SRVC)]}
In [33]: type(las.data)
Out[33]: numpy.ndarray
In [34]: las.data.shape
Out[34]: (235, 5)
In [35]: for curve in las.curves:
...: print(curve.mnemonic)
...: print(curve.unit)
...: print(curve.data)
...: print("\n")
...:
DEPT
F
[ 10180. 10181. 10182. 10183. 10184. 10185. 10186. 10187. 10188.
10189. 10190. 10191. 10192. 10193. 10194. 10195. 10196. 10197.
10198. 10199. 10200. 10201. 10202. 10203. 10204. 10205. 10206.
10207. 10208. 10209. 10210. 10211. 10212. 10213. 10214. 10215.
10216. 10217. 10218. 10219. 10220. 10221. 10222. 10223. 10224.
10225. 10226. 10227. 10228. 10229. 10230. 10231. 10232. 10233.
10234. 10235. 10236. 10237. 10238. 10239. 10240. 10241. 10242.
10243. 10244. 10245. 10246. 10247. 10248. 10249. 10250. 10251.
10252. 10253. 10254. 10255. 10256. 10257. 10258. 10259. 10260.
10261. 10262. 10263. 10264. 10265. 10266. 10267. 10268. 10269.
10270. 10271. 10272. 10273. 10274. 10275. 10276. 10277. 10278.
10279. 10280. 10281. 10282. 10283. 10284. 10285. 10286. 10287.
10288. 10289. 10290. 10291. 10292. 10293. 10294. 10295. 10296.
10297. 10298. 10299. 10300. 10301. 10302. 10303. 10304. 10305.
10306. 10307. 10308. 10309. 10310. 10311. 10312. 10313. 10314.
10315. 10316. 10317. 10318. 10319. 10320. 10321. 10322. 10323.
10324. 10325. 10326. 10327. 10328. 10329. 10330. 10331. 10332.
10333. 10334. 10335. 10336. 10337. 10338. 10339. 10340. 10341.
10342. 10343. 10344. 10345. 10346. 10347. 10348. 10349. 10350.
10351. 10352. 10353. 10354. 10355. 10356. 10357. 10358. 10359.
10360. 10361. 10362. 10363. 10364. 10365. 10366. 10367. 10368.
10369. 10370. 10371. 10372. 10373. 10374. 10375. 10376. 10377.
10378. 10379. 10380. 10381. 10382. 10383. 10384. 10385. 10386.
10387. 10388. 10389. 10390. 10391. 10392. 10393. 10394. 10395.
10396. 10397. 10398. 10399. 10400. 10401. 10402. 10403. 10404.
10405. 10406. 10407. 10408. 10409. 10410. 10411. 10412. 10413.
10414.]
DT
US/F
[ 59.9 59.9 60.5 63.5 64.5 64.6 61.5 59.2 55.9 52.1 49.1 47.8
47.2 47.2 48.5 49.6 48.3 46.9 46.6 46.8 46.7 47.8 51.2 51.6
51.1 51.4 52.3 52.3 51.5 51.2 53.3 57.6 60.6 60.8 59.5 59.7
61.1 61.6 61.8 62. 62.2 62.2 62.2 60.9 60.8 61.5 61.4 61.9
63.2 64.4 62.6 61.4 61. 61.1 62.8 65.4 66.3 66.2 68.3 69.8
70.6 72.4 74.2 74.3 71.5 63.5 60.1 65.2 68.2 66.4 63.2 63.4
65.3 65.1 64.1 63.9 63.9 63.9 63.9 63.5 62.7 63.1 63.6 61.1
58.4 58.1 58.1 57.7 57.1 56.6 56.8 59.5 61.3 61.9 61.9 62.1
62.5 62.5 62.5 62.4 62. 60.7 57.5 56. 56. 57.8 60. 60.3
60.2 59.9 60.4 60.9 61.4 61.4 56.1 51.2 48.4 48.5 49.8 49.8
50. 50.9 50.5 47.9 46.3 46.1 46.4 46.4 45.8 45.9 46.5 46.7
47.3 51.9 55.7 61.2 66.5 68.9 69.6 69.6 69.1 68. 66.9 66.7
66.6 66. 65. 64.4 64. 64.6 64.7 64.4 64.4 65.5 67.4 69.3
70.9 72.4 73.3 73.7 73.8 73.4 73.4 74.4 75.4 75.2 72.6 71.6
72. 74.3 74.6 74.7 72.3 71.9 75.5 77.6 78.3 75.8 73.8 71.6
69.3 67.1 65. 64. 63.8 63.9 65.1 65.5 64.3 64.4 66. 66.
64.6 64.9 65. 62.6 60.4 59.3 59.3 62.6 63.6 61.5 61.7 62.3
61.9 62.3 63.2 63.5 63.5 62.7 60. 57. 54. 49.1 47.2 46.7
47.1 47.6 48.8 49.8 50.8 51.1 50.2 49. 48.4 50.6 50.7 50.4
49.9 49.7 49.6 51.5 52.5 53.2 54.1]
RESD
OHMM
[ 2.20000000e+01 2.10000000e+01 1.97000000e+01 1.89000000e+01
1.82000000e+01 1.80000000e+01 1.80000000e+01 2.10000000e+01
2.90000000e+01 5.30000000e+01 3.90000000e+02 1.50100000e+03
2.09300000e+03 1.67700000e+03 1.07700000e+03 7.65000000e+02
5.64000000e+02 5.54000000e+02 4.87000000e+02 1.59000000e+02
7.40000000e+01 5.70000000e+01 5.00000000e+01 4.80000000e+01
4.80000000e+01 4.90000000e+01 5.60000000e+01 5.90000000e+01
6.10000000e+01 5.20000000e+01 2.40000000e+01 1.75000000e+01
1.54000000e+01 1.52000000e+01 1.52000000e+01 1.52000000e+01
1.47000000e+01 1.29000000e+01 1.20000000e+01 1.10000000e+01
1.06000000e+01 1.05000000e+01 1.05000000e+01 1.08000000e+01
1.11000000e+01 1.12000000e+01 1.07000000e+01 9.90000000e+00
9.30000000e+00 9.00000000e+00 9.40000000e+00 1.01000000e+01
1.02000000e+01 1.00000000e+01 8.00000000e+00 7.10000000e+00
6.50000000e+00 5.80000000e+00 5.00000000e+00 4.20000000e+00
3.60000000e+00 3.30000000e+00 3.20000000e+00 3.30000000e+00
4.00000000e+00 4.90000000e+00 5.40000000e+00 5.80000000e+00
6.20000000e+00 6.60000000e+00 7.60000000e+00 8.90000000e+00
1.01000000e+01 1.12000000e+01 1.24000000e+01 1.51000000e+01
1.66000000e+01 1.75000000e+01 1.80000000e+01 1.80000000e+01
1.80000000e+01 1.80000000e+01 1.90000000e+01 2.10000000e+01
2.30000000e+01 2.70000000e+01 3.00000000e+01 3.30000000e+01
3.50000000e+01 3.50000000e+01 3.00000000e+01 2.70000000e+01
2.30000000e+01 1.99000000e+01 1.89000000e+01 1.85000000e+01
1.94000000e+01 2.00000000e+01 2.00000000e+01 2.20000000e+01
2.40000000e+01 2.60000000e+01 3.00000000e+01 3.30000000e+01
3.40000000e+01 3.00000000e+01 2.80000000e+01 2.60000000e+01
2.60000000e+01 2.90000000e+01 3.40000000e+01 3.50000000e+01
3.90000000e+01 4.40000000e+01 6.60000000e+01 1.22000000e+02
2.48000000e+02 1.72400000e+03 2.03600000e+03 2.03600000e+03
2.05500000e+03 2.09300000e+03 2.11300000e+03 2.11300000e+03
2.11300000e+03 2.09300000e+03 1.63100000e+03 7.51000000e+02
2.50000000e+02 2.16000000e+02 1.99000000e+02 1.76000000e+02
1.30000000e+02 9.50000000e+01 6.90000000e+01 4.70000000e+01
3.10000000e+01 2.10000000e+01 1.75000000e+01 1.61000000e+01
1.61000000e+01 1.61000000e+01 1.75000000e+01 1.80000000e+01
1.83000000e+01 1.83000000e+01 1.83000000e+01 1.83000000e+01
1.82000000e+01 1.74000000e+01 1.63000000e+01 1.54000000e+01
1.40000000e+01 1.27000000e+01 1.10000000e+01 9.00000000e+00
7.50000000e+00 6.70000000e+00 6.10000000e+00 5.70000000e+00
5.60000000e+00 5.30000000e+00 5.00000000e+00 4.50000000e+00
4.00000000e+00 3.50000000e+00 3.20000000e+00 2.80000000e+00
2.50000000e+00 2.20000000e+00 1.94000000e+00 1.72000000e+00
1.59000000e+00 1.50000000e+00 1.43000000e+00 1.37000000e+00
1.34000000e+00 1.34000000e+00 1.38000000e+00 1.59000000e+00
2.00000000e+00 2.90000000e+00 3.30000000e+00 3.80000000e+00
4.50000000e+00 5.00000000e+00 5.30000000e+00 5.50000000e+00
5.60000000e+00 5.60000000e+00 5.70000000e+00 5.70000000e+00
5.70000000e+00 5.70000000e+00 5.80000000e+00 6.30000000e+00
7.20000000e+00 8.10000000e+00 8.30000000e+00 8.30000000e+00
8.10000000e+00 8.00000000e+00 8.80000000e+00 1.00000000e+01
1.01000000e+01 9.20000000e+00 8.60000000e+00 8.50000000e+00
9.40000000e+00 1.14000000e+01 1.48000000e+01 1.90000000e+01
4.00000000e+01 8.90000000e+01 1.34000000e+02 2.20000000e+02
1.93000000e+02 1.22000000e+02 9.60000000e+01 8.10000000e+01
7.50000000e+01 7.50000000e+01 9.70000000e+01 1.67000000e+02
3.15000000e+02 1.69300000e+03 1.87400000e+03 1.87400000e+03
1.87400000e+03 5.91000000e+02 2.08000000e+02 1.34000000e+02
1.16000000e+02 1.13000000e+02 1.56000000e+02]
SP
MV
[ 45.6 49. 53. 55.6 58.4 62.5 64.7 66.9 69.3 71.3
73.7 75.7 76.7 77.1 77.5 77.5 77.5 77.5 77.1 76.5
75.9 74.7 73.7 71.1 67.3 63.7 60.6 57.8 53.2 48.2
42.9 37.9 34.5 31.7 30.1 28.5 27.2 26. 24.4 23.4
22.2 21.2 20.8 20.2 19.8 19.2 19. 19. 18.8 18.6
18.4 18.2 18.2 18.2 18.2 18.2 18.8 19.4 20.4 21.6
22.4 23.8 25. 26.4 28. 29.3 30.5 31.3 32.7 33.5
34.3 34.9 35.7 36.1 36.1 36.3 36.3 36.3 36.3 36.3
36.3 36.3 36.3 36.3 36.9 37.9 38.5 39.7 40.1 40.5
40.5 40.7 40.9 40.9 41.1 41.1 41.1 41.3 41.7 42.1
42.9 43.9 44.7 45.6 46.6 47.2 48. 48.6 50.2 51.2
52.8 54.2 55.2 56. 57.2 58. 59. 59.6 60.8 62.3
63.5 64.7 66.3 67.7 68.9 70.7 72.1 73.1 74.5 75.9
77.3 78.8 80.8 83.4 85.4 87.6 89.8 91.4 93.4 94.2
95.1 95.5 95.5 95.5 95.5 95.7 96.5 96.7 96.9 97.1
97.1 96.7 95.9 95.1 94.8 94.8 94.8 94.8 95.5 96.3
97.1 98.1 99.1 99.5 99.9 100.1 100.1 100.1 99.9 99.9
100.1 101.9 102.1 103.9 104.9 105.7 106.1 106.3 106.5 106.5
106.5 106.5 106.5 105.9 105.3 104.7 104.7 104.5 104.5 104.5
104.5 104.7 105.9 106.9 109.1 109.7 108.7 107.9 107.3 106.9
106.7 106.7 106.5 105.9 105.1 102.7 87.6 78.8 76.5 76.5
76.5 76.5 76.5 76.5 75.5 74.1 72.1 70.5 68.9 67.5
66.7 65.7 65.1 64.1 63.3 63.1 62.7 62.5 62.5 62.5
62.5 62.5 62.9 64.3 65.9]
GR
GAPI
[ 116. 114. 127. 150. 155. 140. 121. 106. 62. 25. 9. 11.
11. 10. 9. 8. 8. 8. 10. 15. 16. 14. 14. 18.
23. 27. 28. 20. 17. 72. 109. 131. 135. 122. 119. 128.
135. 138. 139. 139. 135. 135. 129. 119. 123. 116. 118. 135.
149. 153. 120. 108. 105. 116. 134. 138. 132. 131. 135. 140.
144. 129. 118. 119. 114. 87. 80. 99. 99. 89. 100. 118.
124. 117. 120. 123. 124. 123. 120. 120. 125. 130. 126. 113.
100. 90. 78. 66. 69. 96. 103. 113. 122. 122. 117. 118.
120. 119. 122. 130. 124. 87. 53. 46. 58. 83. 100. 101.
92. 85. 99. 105. 107. 97. 62. 46. 45. 33. 23. 20.
15. 11. 13. 16. 21. 30. 38. 39. 35. 30. 25. 24.
27. 21. 17. 16. 16. 18. 19. 19. 16. 18. 19. 17.
17. 17. 17. 17. 18. 22. 22. 21. 17. 14. 13. 18.
19. 20. 20. 18. 17. 17. 16. 14. 15. 19. 24. 24.
19. 22. 24. 22. 20. 17. 16. 16. 16. 16. 17. 17.
18. 30. 36. 34. 33. 36. 37. 30. 24. 25. 29. 32.
33. 32. 32. 27. 22. 19. 21. 27. 27. 27. 27. 30.
34. 38. 38. 40. 38. 33. 27. 27. 27. 23. 22. 25.
31. 33. 35. 5. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.]
In [36]: import matplotlib.pyplot as plt
In [37]: %matplotlib inline
In [38]: plt.plot(las.index, las["GR"])
Out[38]: [<matplotlib.lines.Line2D at 0xb9dc1d0>]
Integration with pandas.DataFrame¶
The lasio.LASFile.df() method converts the LAS data to a
pandas.DataFrame.
Any changes that you make to the DataFrame can be brought back into the
LASFile object with lasio.LASFile.set_data().
In [168]: las = lasio.read('tests/examples/6038187_v1.2.las')
In [169]: df = las.df()
There are some summary methods handy for data exploration:
In [170]: df.head(10)
Out[170]:
CALI DFAR DNEAR GAMN NEUT PR SP COND
DEPT
0.05 49.765 4.587 3.382 NaN NaN NaN NaN NaN
0.10 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.15 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.20 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.25 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.30 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.35 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.40 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.45 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.50 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
In [171]: df.tail(40)
Out[171]:
CALI DFAR DNEAR GAMN NEUT PR SP COND
DEPT
134.65 100.983 1.563 1.357 -2324.28 158.0 115.508 -3.049 578.643
134.70 100.833 1.570 1.357 NaN NaN NaN NaN 571.233
134.75 93.760 1.582 1.378 NaN NaN NaN NaN 565.552
134.80 88.086 1.561 1.361 NaN NaN NaN NaN 570.490
134.85 86.443 1.516 1.338 NaN NaN NaN NaN 574.937
134.90 79.617 5.989 1.356 NaN NaN NaN NaN 579.137
134.95 65.236 4.587 1.397 NaN NaN NaN NaN NaN
135.00 55.833 4.587 1.351 NaN NaN NaN NaN NaN
135.05 49.061 4.587 1.329 NaN NaN NaN NaN NaN
135.10 49.036 NaN NaN NaN NaN NaN NaN NaN
135.15 49.024 NaN NaN NaN NaN NaN NaN NaN
135.20 49.005 NaN NaN NaN NaN NaN NaN NaN
135.25 48.999 NaN NaN NaN NaN NaN NaN NaN
135.30 48.987 NaN NaN NaN NaN NaN NaN NaN
135.35 48.980 NaN NaN NaN NaN NaN NaN NaN
135.40 48.962 NaN NaN NaN NaN NaN NaN NaN
135.45 48.962 NaN NaN NaN NaN NaN NaN NaN
135.50 48.925 NaN NaN NaN NaN NaN NaN NaN
135.55 48.931 NaN NaN NaN NaN NaN NaN NaN
135.60 48.919 NaN NaN NaN NaN NaN NaN NaN
135.65 48.900 NaN NaN NaN NaN NaN NaN NaN
135.70 48.882 NaN NaN NaN NaN NaN NaN NaN
135.75 48.863 NaN NaN NaN NaN NaN NaN NaN
135.80 48.857 NaN NaN NaN NaN NaN NaN NaN
135.85 48.839 NaN NaN NaN NaN NaN NaN NaN
135.90 48.808 NaN NaN NaN NaN NaN NaN NaN
135.95 48.802 NaN NaN NaN NaN NaN NaN NaN
136.00 48.789 NaN NaN NaN NaN NaN NaN NaN
136.05 48.771 NaN NaN NaN NaN NaN NaN NaN
136.10 48.765 NaN NaN NaN NaN NaN NaN NaN
136.15 48.752 NaN NaN NaN NaN NaN NaN NaN
136.20 48.734 NaN NaN NaN NaN NaN NaN NaN
136.25 48.684 NaN NaN NaN NaN NaN NaN NaN
136.30 48.666 NaN NaN NaN NaN NaN NaN NaN
136.35 48.647 NaN NaN NaN NaN NaN NaN NaN
136.40 48.604 NaN NaN NaN NaN NaN NaN NaN
136.45 48.555 NaN NaN NaN NaN NaN NaN NaN
136.50 48.555 NaN NaN NaN NaN NaN NaN NaN
136.55 48.438 NaN NaN NaN NaN NaN NaN NaN
136.60 -56.275 NaN NaN NaN NaN NaN NaN NaN
In [172]: df.describe()
Out[172]:
CALI DFAR DNEAR GAMN NEUT \
count 2732.000000 2701.000000 2701.000000 2691.000000 2492.000000
mean 97.432002 1.767922 1.729209 -102.330033 441.600013
std 13.939547 0.480333 0.372412 630.106420 370.138208
min -56.275000 0.725000 0.657001 -2324.280000 81.001800
25% 101.077500 1.526000 1.535000 55.783000 158.002000
50% 101.426000 1.758000 1.785000 74.376900 256.501500
75% 101.582000 1.993000 1.948000 88.326900 680.500250
max 103.380000 5.989000 3.382000 169.672000 1665.990000
PR SP COND
count 2692.000000 2692.000000 2697.000000
mean 17940.522307 90.393464 478.670791
std 22089.297212 26.725547 753.869866
min 115.508000 -3.049000 -116.998000
25% 2652.470000 93.495500 200.981000
50% 2709.345000 99.994000 266.435000
75% 50499.900000 100.623000 505.530000
max 50499.900000 102.902000 4978.160000
There’s obviously a problem with the GAMN log: -2324.28 is not a valid value. Let’s fix that.
In [44]: import numpy as np
In [173]: df['GAMN'][df['GAMN'] == -2324.28] = np.nan
In [174]: df.describe()['GAMN']
Out[174]:
count 2491.000000
mean 76.068198
std 23.120160
min 13.946000
25% 60.434100
50% 76.700700
75% 90.647500
max 169.672000
Name: GAMN, dtype: float64
Let’s create a new log with the moving average of the GAMN log, over
1 m. This is easy enough to do with the pandas pandas.Series.rolling()
method and the LAS file’s STEP value:
In [175]: df['GAMN_avg'] = df['GAMN'].rolling(int(1 / las.well.STEP.value), center=True).mean()
Now we want to apply this DataFrame df back to the las LASFile object,
and check that it’s all there:
In [176]: las.set_data(df)
In [177]: las.curves
Out[177]:
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=DEPTH, original_mnemonic=DEPT, data.shape=(2732,)),
CurveItem(mnemonic=CALI, unit=MM, value=, descr=CALI, original_mnemonic=CALI, data.shape=(2732,)),
CurveItem(mnemonic=DFAR, unit=G/CM3, value=, descr=DFAR, original_mnemonic=DFAR, data.shape=(2732,)),
CurveItem(mnemonic=DNEAR, unit=G/CM3, value=, descr=DNEAR, original_mnemonic=DNEAR, data.shape=(2732,)),
CurveItem(mnemonic=GAMN, unit=GAPI, value=, descr=GAMN, original_mnemonic=GAMN, data.shape=(2732,)),
CurveItem(mnemonic=NEUT, unit=CPS, value=, descr=NEUT, original_mnemonic=NEUT, data.shape=(2732,)),
CurveItem(mnemonic=PR, unit=OHM/M, value=, descr=PR, original_mnemonic=PR, data.shape=(2732,)),
CurveItem(mnemonic=SP, unit=MV, value=, descr=SP, original_mnemonic=SP, data.shape=(2732,)),
CurveItem(mnemonic=COND, unit=MS/M, value=, descr=COND, original_mnemonic=COND, data.shape=(2732,)),
CurveItem(mnemonic=GAMN_avg, unit=, value=, descr=, original_mnemonic=GAMN_avg, data.shape=(2732,))]
In [178]: las.df().describe()
Out[178]:
CALI DFAR DNEAR GAMN NEUT \
count 2732.000000 2701.000000 2701.000000 2491.000000 2492.000000
mean 97.432002 1.767922 1.729209 76.068198 441.600013
std 13.939547 0.480333 0.372412 23.120160 370.138208
min -56.275000 0.725000 0.657001 13.946000 81.001800
25% 101.077500 1.526000 1.535000 60.434100 158.002000
50% 101.426000 1.758000 1.785000 76.700700 256.501500
75% 101.582000 1.993000 1.948000 90.647500 680.500250
max 103.380000 5.989000 3.382000 169.672000 1665.990000
PR SP COND GAMN_avg
count 2692.000000 2692.000000 2697.000000 2472.000000
mean 17940.522307 90.393464 478.670791 76.326075
std 22089.297212 26.725547 753.869866 18.208038
min 115.508000 -3.049000 -116.998000 24.753655
25% 2652.470000 93.495500 200.981000 64.848379
50% 2709.345000 99.994000 266.435000 77.747517
75% 50499.900000 100.623000 505.530000 88.323376
max 50499.900000 102.902000 4978.160000 120.049300
All good, the new curve is in there.
See the pandas documentation for more information!
Header section metadata¶
Tutorial¶
One of the primary motivations in writing lasio was to be able to reliably parse LAS header sections. This is working fairly well for LAS 1.2 and 2.0 files, and lasio does not require LAS files to be strictly compliant with either standard.
In [179]: import lasio
In [180]: las = lasio.read('tests/examples/6038187_v1.2_short.las')
The header sections are stored in the dictionary las.sections:
In [206]: type(las.sections)
Out[206]: dict
In [207]: las.sections.keys()
Out[207]: dict_keys(['Version', 'Well', 'Curves', 'Parameter', 'Other'])
These are special names reserved for LAS 1.2 and 2.0 files, as defined by the standard. Non-standard header sections are also allowed but not fully parsed.
| LAS file | Read in as | References in LASFile |
|---|---|---|
~v or ~V |
lasio.las_items.SectionItems |
LASFile.version and LASFile.sections['Version'] |
~w or ~W |
lasio.las_items.SectionItems |
LASFile.well and LASFile.sections['Well'] |
~c or ~C |
lasio.las_items.SectionItems |
LASFile.curves and LASFile.sections['Curves'] |
~p or ~P |
lasio.las_items.SectionItems |
LASFile.params and LASFile.sections['Parameter'] |
~o or ~O |
str |
LASFile.other and LASFile.sections['Other'] |
~extra section |
str |
LASFile.sections['extra section'] |
~a or ~A |
numpy.ndarray |
LASFile.data or each column is in LASFile.curves[...].data |
For example:
In [208]: las.sections['Version']
Out[208]:
[HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS LOG ASCII STANDARD - VERSION 2.0, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=ONE LINE PER DEPTH STEP, original_mnemonic=WRAP)]
In [182]: las.version
Out[182]:
[HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS LOG ASCII STANDARD - VERSION 2.0, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=ONE LINE PER DEPTH STEP, original_mnemonic=WRAP)]
Sections themselves are represented by lasio.las_items.SectionItems objects.
This is a list which has been extended to allow you to access the items within
by their mnemonic:
In [209]: las.version.VERS
Out[209]: HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS LOG ASCII STANDARD - VERSION 2.0, original_mnemonic=VERS)
In [210]: las.version['VERS']
Out[210]: HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS LOG ASCII STANDARD - VERSION 2.0, original_mnemonic=VERS)
In [211]: las.version[0]
Out[211]: HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS LOG ASCII STANDARD - VERSION 2.0, original_mnemonic=VERS)
In [212]: id(Out[209]), id(Out[210]), id(Out[211])
Out[212]: (250964032, 250964032, 250964032)
As you can see, either attribute-style or item-style access is fine - with one exception, see below.
Let’s take a look at the next special section, ~W:
In [188]: las.well
Out[188]:
[HeaderItem(mnemonic=STRT, unit=M, value=0.05, descr=FIRST INDEX VALUE, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=M, value=136.6, descr=LAST INDEX VALUE, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=M, value=0.05, descr=STEP, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-99999, descr=NULL VALUE, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=, descr=COMP, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=Scorpio E1, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=FLD, unit=, value=, descr=, original_mnemonic=FLD),
HeaderItem(mnemonic=LOC, unit=, value=Mt Eba, descr=LOC, original_mnemonic=LOC),
HeaderItem(mnemonic=SRVC, unit=, value=, descr=, original_mnemonic=SRVC),
HeaderItem(mnemonic=CTRY, unit=, value=, descr=, original_mnemonic=CTRY),
HeaderItem(mnemonic=STAT, unit=, value=SA, descr=STAT, original_mnemonic=STAT),
HeaderItem(mnemonic=CNTY, unit=, value=, descr=, original_mnemonic=CNTY),
HeaderItem(mnemonic=DATE, unit=, value=15/03/2015, descr=DATE, original_mnemonic=DATE),
HeaderItem(mnemonic=UWI, unit=, value=6038-187, descr=WUNT, original_mnemonic=UWI)]
The CTRY item is blank. We will set it:
In [190]: las.well.CTRY = 'Australia'
In [191]: las.well.CTRY
Out[191]: HeaderItem(mnemonic=CTRY, unit=, value=Australia, descr=, original_mnemonic=CTRY)
Notice that SectionItems plays a little trick here. It actually sets the header_item.value
attribute, instead of replacing the entire HeaderItem object.
You can set any of the attributes directly. Let’s take an example from the ~C section:
In [192]: las.curves
Out[192]:
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=DEPTH, original_mnemonic=DEPT, data.shape=(121,)),
CurveItem(mnemonic=CALI, unit=MM, value=, descr=CALI, original_mnemonic=CALI, data.shape=(121,)),
CurveItem(mnemonic=DFAR, unit=G/CM3, value=, descr=DFAR, original_mnemonic=DFAR, data.shape=(121,)),
CurveItem(mnemonic=DNEAR, unit=G/CM3, value=, descr=DNEAR, original_mnemonic=DNEAR, data.shape=(121,)),
CurveItem(mnemonic=GAMN, unit=GAPI, value=, descr=GAMN, original_mnemonic=GAMN, data.shape=(121,)),
CurveItem(mnemonic=NEUT, unit=CPS, value=, descr=NEUT, original_mnemonic=NEUT, data.shape=(121,)),
CurveItem(mnemonic=PR, unit=OHM/M, value=, descr=PR, original_mnemonic=PR, data.shape=(121,)),
CurveItem(mnemonic=SP, unit=MV, value=, descr=SP, original_mnemonic=SP, data.shape=(121,)),
CurveItem(mnemonic=COND, unit=MS/M, value=, descr=COND, original_mnemonic=COND, data.shape=(121,))]
In [193]: las.curves.PR.unit = 'ohmm'
In [194]: las.curves.PR
Out[194]: CurveItem(mnemonic=PR, unit=ohmm, value=, descr=PR, original_mnemonic=PR, data.shape=(121,))
Now let’s look more closely at how to manipulate and add or remove items from a section.
In [195]: las.params
Out[195]:
[HeaderItem(mnemonic=BS, unit=, value=216 mm, descr=BS, original_mnemonic=BS),
HeaderItem(mnemonic=JOBN, unit=, value=, descr=JOBN, original_mnemonic=JOBN),
HeaderItem(mnemonic=WPMT, unit=, value=, descr=WPMT, original_mnemonic=WPMT),
HeaderItem(mnemonic=AGL, unit=, value=, descr=AGL, original_mnemonic=AGL),
HeaderItem(mnemonic=PURP, unit=, value=Cased hole stratigraphy, descr=PURP, original_mnemonic=PURP),
HeaderItem(mnemonic=X, unit=, value=560160, descr=X, original_mnemonic=X),
HeaderItem(mnemonic=CSGL, unit=, value=0 m - 135 m, descr=CSGL, original_mnemonic=CSGL),
HeaderItem(mnemonic=UNIT, unit=, value=, descr=UNIT, original_mnemonic=UNIT),
HeaderItem(mnemonic=Y, unit=, value=6686430, descr=Y, original_mnemonic=Y),
HeaderItem(mnemonic=TDL, unit=, value=135.2 m, descr=TDL, original_mnemonic=TDL),
HeaderItem(mnemonic=PROD, unit=, value=, descr=PROD, original_mnemonic=PROD),
HeaderItem(mnemonic=MUD, unit=, value=Water, descr=MUD, original_mnemonic=MUD),
HeaderItem(mnemonic=CSGS, unit=, value=100 mm, descr=CSGS, original_mnemonic=CSGS),
HeaderItem(mnemonic=ENG, unit=, value=, descr=ENG, original_mnemonic=ENG),
HeaderItem(mnemonic=STEP, unit=, value=5 cm, descr=STEP, original_mnemonic=STEP),
HeaderItem(mnemonic=FluidLevel, unit=, value=54 m, descr=FluidLevel, original_mnemonic=FluidLevel),
HeaderItem(mnemonic=CSGT, unit=, value=PVC, descr=CSGT, original_mnemonic=CSGT),
HeaderItem(mnemonic=WIT, unit=, value=, descr=WIT, original_mnemonic=WIT),
HeaderItem(mnemonic=EREF, unit=, value=, descr=EREF, original_mnemonic=EREF),
HeaderItem(mnemonic=PROJ, unit=, value=, descr=PROJ, original_mnemonic=PROJ),
HeaderItem(mnemonic=ZONE, unit=, value=53J, descr=ZONE, original_mnemonic=ZONE),
HeaderItem(mnemonic=DREF, unit=, value=GL, descr=DREF, original_mnemonic=DREF),
HeaderItem(mnemonic=TDD, unit=, value=136 m, descr=TDD, original_mnemonic=TDD)]
We want to rename the DREF mnemonic as LMF. We can do so by changing the
header_item.mnemonic attribute.
In [197]: las.params.DREF.mnemonic = 'LMF'
In [198]: las.params
Out[198]:
[HeaderItem(mnemonic=BS, unit=, value=216 mm, descr=BS, original_mnemonic=BS),
HeaderItem(mnemonic=JOBN, unit=, value=, descr=JOBN, original_mnemonic=JOBN),
HeaderItem(mnemonic=WPMT, unit=, value=, descr=WPMT, original_mnemonic=WPMT),
HeaderItem(mnemonic=AGL, unit=, value=, descr=AGL, original_mnemonic=AGL),
HeaderItem(mnemonic=PURP, unit=, value=Cased hole stratigraphy, descr=PURP, original_mnemonic=PURP),
HeaderItem(mnemonic=X, unit=, value=560160, descr=X, original_mnemonic=X),
HeaderItem(mnemonic=CSGL, unit=, value=0 m - 135 m, descr=CSGL, original_mnemonic=CSGL),
HeaderItem(mnemonic=UNIT, unit=, value=, descr=UNIT, original_mnemonic=UNIT),
HeaderItem(mnemonic=Y, unit=, value=6686430, descr=Y, original_mnemonic=Y),
HeaderItem(mnemonic=TDL, unit=, value=135.2 m, descr=TDL, original_mnemonic=TDL),
HeaderItem(mnemonic=PROD, unit=, value=, descr=PROD, original_mnemonic=PROD),
HeaderItem(mnemonic=MUD, unit=, value=Water, descr=MUD, original_mnemonic=MUD),
HeaderItem(mnemonic=CSGS, unit=, value=100 mm, descr=CSGS, original_mnemonic=CSGS),
HeaderItem(mnemonic=ENG, unit=, value=, descr=ENG, original_mnemonic=ENG),
HeaderItem(mnemonic=STEP, unit=, value=5 cm, descr=STEP, original_mnemonic=STEP),
HeaderItem(mnemonic=FluidLevel, unit=, value=54 m, descr=FluidLevel, original_mnemonic=FluidLevel),
HeaderItem(mnemonic=CSGT, unit=, value=PVC, descr=CSGT, original_mnemonic=CSGT),
HeaderItem(mnemonic=WIT, unit=, value=, descr=WIT, original_mnemonic=WIT),
HeaderItem(mnemonic=EREF, unit=, value=, descr=EREF, original_mnemonic=EREF),
HeaderItem(mnemonic=PROJ, unit=, value=, descr=PROJ, original_mnemonic=PROJ),
HeaderItem(mnemonic=ZONE, unit=, value=53J, descr=ZONE, original_mnemonic=ZONE),
HeaderItem(mnemonic=LMF, unit=, value=GL, descr=DREF, original_mnemonic=LMF),
HeaderItem(mnemonic=TDD, unit=, value=136 m, descr=TDD, original_mnemonic=TDD)]
And now we need to add a new mnemonic. Adding via an attribute will not work. You need to use the item-style access.
In [201]: las.params['DRILL'] = lasio.HeaderItem(mnemonic='DRILL', value='John Smith', descr='Driller on site')
In [202]: las.params
Out[202]:
[HeaderItem(mnemonic=BS, unit=, value=216 mm, descr=BS, original_mnemonic=BS),
HeaderItem(mnemonic=JOBN, unit=, value=, descr=JOBN, original_mnemonic=JOBN),
HeaderItem(mnemonic=WPMT, unit=, value=, descr=WPMT, original_mnemonic=WPMT),
HeaderItem(mnemonic=AGL, unit=, value=, descr=AGL, original_mnemonic=AGL),
HeaderItem(mnemonic=PURP, unit=, value=Cased hole stratigraphy, descr=PURP, original_mnemonic=PURP),
HeaderItem(mnemonic=X, unit=, value=560160, descr=X, original_mnemonic=X),
HeaderItem(mnemonic=CSGL, unit=, value=0 m - 135 m, descr=CSGL, original_mnemonic=CSGL),
HeaderItem(mnemonic=UNIT, unit=, value=, descr=UNIT, original_mnemonic=UNIT),
HeaderItem(mnemonic=Y, unit=, value=6686430, descr=Y, original_mnemonic=Y),
HeaderItem(mnemonic=TDL, unit=, value=135.2 m, descr=TDL, original_mnemonic=TDL),
HeaderItem(mnemonic=PROD, unit=, value=, descr=PROD, original_mnemonic=PROD),
HeaderItem(mnemonic=MUD, unit=, value=Water, descr=MUD, original_mnemonic=MUD),
HeaderItem(mnemonic=CSGS, unit=, value=100 mm, descr=CSGS, original_mnemonic=CSGS),
HeaderItem(mnemonic=ENG, unit=, value=, descr=ENG, original_mnemonic=ENG),
HeaderItem(mnemonic=STEP, unit=, value=5 cm, descr=STEP, original_mnemonic=STEP),
HeaderItem(mnemonic=FluidLevel, unit=, value=54 m, descr=FluidLevel, original_mnemonic=FluidLevel),
HeaderItem(mnemonic=CSGT, unit=, value=PVC, descr=CSGT, original_mnemonic=CSGT),
HeaderItem(mnemonic=WIT, unit=, value=, descr=WIT, original_mnemonic=WIT),
HeaderItem(mnemonic=EREF, unit=, value=, descr=EREF, original_mnemonic=EREF),
HeaderItem(mnemonic=PROJ, unit=, value=, descr=PROJ, original_mnemonic=PROJ),
HeaderItem(mnemonic=ZONE, unit=, value=53J, descr=ZONE, original_mnemonic=ZONE),
HeaderItem(mnemonic=LMF, unit=, value=GL, descr=DREF, original_mnemonic=LMF),
HeaderItem(mnemonic=TDD, unit=, value=136 m, descr=TDD, original_mnemonic=TDD),
HeaderItem(mnemonic=DRILL, unit=, value=John Smith, descr=Driller on site, original_mnemonic=DRILL)]
Bingo.
Handling errors¶
lasio will do its best to read every line from the header section. If it can make sense of it, it will parse it into a mnemonic, unit, value, and description.
However often there are problems in LAS files. For example, a header section might contain something like:
COUNTY: RUSSELL
(missing period, should be COUNTY. : RUSSELL). Or:
API . : API Number (required if CTRY = US)
"# Surface Coords: 1,000' FNL & 2,000' FWL"
LATI .DEG : Latitude - see Surface Coords comment above
LONG .DEG : Longitude - see Surface Coords comment above
Obviously the line with ” causes an error.
All these (and any other kind of error in the header section) can be turned from LASHeaderError exceptions into logger.warning() calls instead by using lasio.read(..., ignore_header_errors=True).
Here is an example. First we try reading a file without this argument:
In [2]: las = lasio.read('tests/examples/dodgy_param_sect.las', ignore_header_errors=False)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\Code\lasio\lasio\reader.py in parse_header_section(sectdict, version, ignore_header_errors, mnemonic_case)
458 try:
--> 459 values = read_line(line)
460 except:
~\Code\lasio\lasio\reader.py in read_line(*args, **kwargs)
625 '''
--> 626 return read_header_line(*args, **kwargs)
627
~\Code\lasio\lasio\reader.py in read_header_line(line, pattern)
656 m = re.match(pattern, line)
--> 657 mdict = m.groupdict()
658 for key, value in mdict.items():
AttributeError: 'NoneType' object has no attribute 'groupdict'
During handling of the above exception, another exception occurred:
LASHeaderError Traceback (most recent call last)
<ipython-input-2-3c0606fe7dc1> in <module>()
----> 1 las = lasio.read('tests/examples/dodgy_param_sect.las', ignore_header_errors=False)
~\Code\lasio\lasio\__init__.py in read(file_ref, **kwargs)
41
42 '''
---> 43 return LASFile(file_ref, **kwargs)
~\Code\lasio\lasio\las.py in __init__(self, file_ref, **read_kwargs)
76
77 if not (file_ref is None):
---> 78 self.read(file_ref, **read_kwargs)
79
80 def read(self, file_ref,
~\Code\lasio\lasio\las.py in read(self, file_ref, ignore_data, read_policy, null_policy, ignore_header_errors, mnemonic_case, **kwargs)
185 add_section("~P", "Parameter", version=version,
186 ignore_header_errors=ignore_header_errors,
--> 187 mnemonic_case=mnemonic_case)
188 s = self.match_raw_section("~O")
189
~\Code\lasio\lasio\las.py in add_section(pattern, name, **sect_kws)
122 if raw_section:
123 self.sections[name] = reader.parse_header_section(raw_section,
--> 124 **sect_kws)
125 drop.append(raw_section["title"])
126 else:
~\Code\lasio\lasio\reader.py in parse_header_section(sectdict, version, ignore_header_errors, mnemonic_case)
465 logger.warning(message)
466 else:
--> 467 raise exceptions.LASHeaderError(message)
468 else:
469 if mnemonic_case == 'upper':
LASHeaderError: line 31 (section ~PARAMETER INFORMATION): "DEPTH DT RHOB NPHI SFLU SFLA ILM ILD"
Now if we use ignore_header_errors=True:
In [3]: las = lasio.read('tests/examples/dodgy_param_sect.las', ignore_header_errors=True)
line 31 (section ~PARAMETER INFORMATION): "DEPTH DT RHOB NPHI SFLU SFLA ILM ILD"
In [4]: las.params
[]
In [5]: las.curves
Out[5]:
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1 DEPTH, original_mnemonic=DEPT, data.shape=(3,)),
CurveItem(mnemonic=DT, unit=US/M, value=, descr=2 SONIC TRANSIT TIME, original_mnemonic=DT, data.shape=(3,)),
CurveItem(mnemonic=RHOB, unit=K/M3, value=, descr=3 BULK DENSITY, original_mnemonic=RHOB, data.shape=(3,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4 NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(3,)),
CurveItem(mnemonic=SFLU, unit=OHMM, value=, descr=5 RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=SFLA, unit=OHMM, value=, descr=6 SHALLOW RESISTIVITY, original_mnemonic=SFLA, data.shape=(3,)),
CurveItem(mnemonic=ILM, unit=OHMM, value=, descr=7 MEDIUM RESISTIVITY, original_mnemonic=ILM, data.shape=(3,)),
CurveItem(mnemonic=ILD, unit=OHMM, value=, descr=8 DEEP RESISTIVITY, original_mnemonic=ILD, data.shape=(3,))]
Only a warning is issued, and the rest of the LAS file loads OK.
Handling duplicate mnemonics¶
Take this LAS file as an example, containing this ~C section:
~CURVE INFORMATION
DEPT.M : 1 DEPTH
DT .US/M : 2 SONIC TRANSIT TIME
RHOB.K/M3 : 3 BULK DENSITY
NPHI.V/V : 4 NEUTRON POROSITY
RXO.OHMM : 5 RXO RESISTIVITY
RES.OHMM : 6 SHALLOW RESISTIVITY
RES.OHMM : 7 MEDIUM RESISTIVITY
RES.OHMM : 8 DEEP RESISTIVITY
Notice there are three curves with the mnemonic RES.
When we load the file in, lasio distinguishes between these duplicates:
In [2]: las = lasio.read('tests/examples/mnemonic_duplicate2.las')
In [3]: las.curves
Out[3]:
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1 DEPTH, original_mnemonic=DEPT, data.shape=(3,)),
CurveItem(mnemonic=DT, unit=US/M, value=, descr=2 SONIC TRANSIT TIME, original_mnemonic=DT, data.shape=(3,)),
CurveItem(mnemonic=RHOB, unit=K/M3, value=, descr=3 BULK DENSITY, original_mnemonic=RHOB, data.shape=(3,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4 NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(3,)),
CurveItem(mnemonic=RXO, unit=OHMM, value=, descr=5 RXO RESISTIVITY, original_mnemonic=RXO, data.shape=(3,)),
CurveItem(mnemonic=RES:1, unit=OHMM, value=, descr=6 SHALLOW RESISTIVITY, original_mnemonic=RES, data.shape=(3,)),
CurveItem(mnemonic=RES:2, unit=OHMM, value=, descr=7 MEDIUM RESISTIVITY, original_mnemonic=RES, data.shape=(3,)),
CurveItem(mnemonic=RES:3, unit=OHMM, value=, descr=8 DEEP RESISTIVITY, original_mnemonic=RES, data.shape=(3,))]
In [4]: las.curves['RES:2']
Out[4]: CurveItem(mnemonic=RES:2, unit=OHMM, value=, descr=7 MEDIUM RESISTIVITY, original_mnemonic=RES, data.shape=(3,))
It remembers the original mnemonic, so when you write the file back out, they come back:
In [6]: import sys
In [7]: las.write(sys.stdout)
~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : ONE LINE PER DEPTH STEP
~Well ------------------------------------------------------
STRT.M 1670.0 :
STOP.M 1669.75 :
STEP.M -0.125 :
NULL. -999.25 :
COMP. COMPANY : # ANY OIL COMPANY LTD.
WELL. WELL : ANY ET AL OIL WELL #12
FLD . FIELD : EDAM
LOC . LOCATION : A9-16-49-20W3M
PROV. PROVINCE : SASKATCHEWAN
SRVC. SERVICE COMPANY : ANY LOGGING COMPANY LTD.
DATE. LOG DATE : 25-DEC-1988
UWI . UNIQUE WELL ID : 100091604920W300
~Curves ----------------------------------------------------
DEPT.M : 1 DEPTH
DT .US/M : 2 SONIC TRANSIT TIME
RHOB.K/M3 : 3 BULK DENSITY
NPHI.V/V : 4 NEUTRON POROSITY
RXO .OHMM : 5 RXO RESISTIVITY
RES .OHMM : 6 SHALLOW RESISTIVITY
RES .OHMM : 7 MEDIUM RESISTIVITY
RES .OHMM : 8 DEEP RESISTIVITY
~Params ----------------------------------------------------
BHT .DEGC 35.5 : BOTTOM HOLE TEMPERATURE
BS .MM 200.0 : BIT SIZE
FD .K/M3 1000.0 : FLUID DENSITY
MATR. 0.0 : NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO)
MDEN. 2710.0 : LOGGING MATRIX DENSITY
RMF .OHMM 0.216 : MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0 : DRILL FLUID DENSITY
~Other -----------------------------------------------------
Note: The logging tools became stuck at 625 meters causing the data
between 625 meters and 615 meters to be invalid.
~ASCII -----------------------------------------------------
1670 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.9 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.8 123.45 2550 0.45 123.45 123.45 110.2 105.6
Normalising mnemonic case¶
If there is a mix of upper and lower case characters in the mnemonics, by default lasio will convert all mnemonics to uppercase to avoid problems with producing the :1, :2, :3, and so on. There is a keyword argument which will preserve the original formatting if that is what you prefer.
In [8]: las = lasio.read('tests/examples/mnemonic_case.las')
In [9]: las.curves
Out[9]:
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1 DEPTH, original_mnemonic=DEPT, data.shape=(3,)),
CurveItem(mnemonic=SFLU:1, unit=K/M3, value=, descr=3 BULK DENSITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4 NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(3,)),
CurveItem(mnemonic=SFLU:2, unit=OHMM, value=, descr=5 RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=SFLU:3, unit=OHMM, value=, descr=6 SHALLOW RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=SFLU:4, unit=OHMM, value=, descr=7 MEDIUM RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=SFLU:5, unit=OHMM, value=, descr=8 DEEP RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,))]
In [10]: las = lasio.read('tests/examples/mnemonic_case.las', mnemonic_case='preserve')
In [11]: las.curves
Out[11]:
[CurveItem(mnemonic=Dept, unit=M, value=, descr=1 DEPTH, original_mnemonic=Dept, data.shape=(3,)),
CurveItem(mnemonic=Sflu, unit=K/M3, value=, descr=3 BULK DENSITY, original_mnemonic=Sflu, data.shape=(3,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4 NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(3,)),
CurveItem(mnemonic=SFLU:1, unit=OHMM, value=, descr=5 RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=SFLU:2, unit=OHMM, value=, descr=6 SHALLOW RESISTIVITY, original_mnemonic=SFLU, data.shape=(3,)),
CurveItem(mnemonic=sflu, unit=OHMM, value=, descr=7 MEDIUM RESISTIVITY, original_mnemonic=sflu, data.shape=(3,)),
CurveItem(mnemonic=SfLu, unit=OHMM, value=, descr=8 DEEP RESISTIVITY, original_mnemonic=SfLu, data.shape=(3,))]
Data section¶
Handling errors¶
lasio has a flexible way of handling “errors” in the ~ASCII data section to
accommodate how strict or flexible you want to be.
Example errors¶
Here are some examples of errors.
- Files could contain a variety of indicators for an invalid data point other than that defined by
the NULL line in the LAS header (usually -999.25).
- Fixed-width columns could run into each other:
7686.500 64.932 0.123 0.395 12.403 156.271 10.649 -0.005 193.223 327.902 -0.023 4.491 2.074 29.652
7686.000 67.354 0.140 0.415 9.207 4648.011 10.609 -0.004 3778.709 1893.751 -0.048 4.513 2.041 291.910
7685.500 69.004 0.151 0.412 7.020101130.188 10.560 -0.004 60000.000 2901.317 -0.047 4.492 2.046 310.119
7685.000 68.809 0.150 0.411 7.330109508.961 10.424 -0.005 60000.000 2846.619 -0.042 4.538 2.049 376.968
7684.500 68.633 0.149 0.402 7.345116238.453 10.515 -0.005 60000.000 2290.275 -0.051 4.543 2.063 404.972
7684.000 68.008 0.144 0.386 7.682 4182.679 10.515 -0.004 3085.681 1545.842 -0.046 4.484 2.089 438.195
- Odd text such as
(null):
8090.00 -999.25 -999.25 -999.25 0 0 0 0 0 0 0 0
8091.000 0.70 337.70 (null) 0 0 0 0 0 0 0 0
8092.000 -999.25 -999.25 -999.25 0 0 0 0 0 0 0 0
Handling run-on errors¶
lasio detects and handles these problems by default using lasio.read(f, read_policy='default').
For example a file with this data section:
~A
7686.000 67.354 0.140 0.415 9.207 4648.011 10.609
7685.500 69.004 0.151 0.412 7.020101130.188 10.560
7685.000 68.809 0.150 0.411 7.330-19508.961 10.424
7684.500 68.633 0.149 0.402 7.345116238.453 10.515
7684.000 68.008 0.144 0.386 7.682 4182.679 10.515
is loaded by default as the following:
In [9]: las = lasio.read('tests/examples/null_policy_runon.las')
In [12]: las.data
Out[12]:
array([[7686.0, 67.354, 0.14, 0.415, 9.207, 4648.011, 10.609],
[7685.5, 69.004, 0.151, 0.412, nan, nan, 10.56],
[7685.0, 68.809, 0.15, 0.411, 7.33, -19508.961, 10.424],
[7684.5, 68.633, 0.149, 0.402, nan, nan, 10.515],
[7684.0, 68.008, 0.144, 0.386, 7.682, 4182.679, 10.515]])
Handling invalid data indicators automatically¶
These are detected by lasio to a degree which you can control with the null_policy keyword argument.
You can specify a policy of ‘none’, ‘strict’, ‘common’, ‘aggressive’,
or ‘all’. These policies all include a subset of pre-defined substitutions.
Or you can give your own list of substitutions. Here is the list of
predefined policies and substitutions from lasio.defaults.
Policies that you can pick with e.g. null_policy='common':
NULL_POLICIES = {
'none': [],
'strict': ['NULL', ],
'common': ['NULL', '(null)', '-',
'9999.25', '999.25', 'NA', 'INF', 'IO', 'IND'],
'aggressive': ['NULL', '(null)', '--',
'9999.25', '999.25', 'NA', 'INF', 'IO', 'IND',
'999', '999.99', '9999', '9999.99' '2147483647', '32767',
'-0.0', ],
'all': ['NULL', '(null)', '-',
'9999.25', '999.25', 'NA', 'INF', 'IO', 'IND',
'999', '999.99', '9999', '9999.99' '2147483647', '32767', '-0.0',
'numbers-only', ],
'numbers-only': ['numbers-only', ]
}
Or substitutions you could specify with e.g. null_policy=['NULL', '999.25', 'INF']:
NULL_SUBS = {
'NULL': [None, ], # special case to be handled
'999.25': [-999.25, 999.25],
'9999.25': [-9999.25, 9999.25],
'999.99': [-999.99, 999.99],
'9999.99': [-9999.99, 9999.99],
'999': [-999, 999],
'9999': [-9999, 9999],
'2147483647': [-2147483647, 2147483647],
'32767': [-32767, 32767],
'NA': [(re.compile(r'(#N/A)[ ]'), ' NaN '),
(re.compile(r'[ ](#N/A)'), ' NaN '), ],
'INF': [(re.compile(r'(-?1\.#INF)[ ]'), ' NaN '),
(re.compile(r'[ ](-?1\.#INF)'), ' NaN '), ],
'IO': [(re.compile(r'(-?1\.#IO)[ ]'), ' NaN '),
(re.compile(r'[ ](-?1\.#IO)'), ' NaN '), ],
'IND': [(re.compile(r'(-?1\.#IND)[ ]'), ' NaN '),
(re.compile(r'[ ](-?1\.#IND)'), ' NaN '), ],
'-0.0': [(re.compile(r'(-?0\.0+)[ ]'), ' NaN '),
(re.compile(r'[ ](-?0\.0+)'), ' NaN '), ],
'numbers-only': [(re.compile(r'([^ 0-9.\-+]+)[ ]'), ' NaN '),
(re.compile(r'[ ]([^ 0-9.\-+]+)'), ' NaN '), ],
}
You can also specify substitutions directly. E.g. for a file with this data section:
~A DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
1670.000 9998 2550.000 0.450 123.450 123.450 110.200 105.600
1669.875 9999 2550.000 0.450 123.450 123.450 110.200 105.600
1669.750 10000 ERR 0.450 123.450 -999.25 110.200 105.600
Ordinarily it would raise an exception:
In [13]: las = lasio.read('tests/examples/null_policy_ERR.las')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\Code\lasio\lasio\reader.py in read_file_contents(file_obj, regexp_subs, value_null_subs, ignore_data)
271 try:
--> 272 data = read_data_section_iterative(file_obj, regexp_subs, value_null_subs)
273 except:
~\Code\lasio\lasio\reader.py in read_data_section_iterative(file_obj, regexp_subs, value_null_subs)
348
--> 349 array = np.fromiter(items(file_obj), np.float64, -1)
350 for value in value_null_subs:
ValueError: could not convert string to float: 'ERR'
During handling of the above exception, another exception occurred:
LASDataError Traceback (most recent call last)
<ipython-input-13-0cb27623119d> in <module>()
----> 1 las = lasio.read('tests/examples/null_policy_ERR.las')
~\Code\lasio\lasio\__init__.py in read(file_ref, **kwargs)
41
42 '''
---> 43 return LASFile(file_ref, **kwargs)
~\Code\lasio\lasio\las.py in __init__(self, file_ref, **read_kwargs)
76
77 if not (file_ref is None):
---> 78 self.read(file_ref, **read_kwargs)
79
80 def read(self, file_ref,
~\Code\lasio\lasio\las.py in read(self, file_ref, ignore_data, read_policy, null_policy, ignore_header_errors, **kwargs)
106
107 self.raw_sections = reader.read_file_contents(
--> 108 file_obj, regexp_subs, value_null_subs, ignore_data=ignore_data, )
109
110 if hasattr(file_obj, "close"):
~\Code\lasio\lasio\reader.py in read_file_contents(file_obj, regexp_subs, value_null_subs, ignore_data)
274 raise exceptions.LASDataError(
275 traceback.format_exc()[:-1] +
--> 276 ' in data section beginning line {}'.format(i + 1))
277 sections[line] = {
278 "section_type": "data",
LASDataError: Traceback (most recent call last):
File "C:\Users\kent\Code\lasio\lasio\reader.py", line 272, in read_file_contents
data = read_data_section_iterative(file_obj, regexp_subs, value_null_subs)
File "C:\Users\kent\Code\lasio\lasio\reader.py", line 349, in read_data_section_iterative
array = np.fromiter(items(file_obj), np.float64, -1)
ValueError: could not convert string to float: 'ERR' in data section beginning line 43
But if we specify the regular expression to use with re.sub(), we can
easily load it:
In [14]: las = lasio.read('tests/examples/null_policy_ERR.las', null_policy=[('ERR', ' NaN '), ])
In [16]: las.data
Out[16]:
array([[1670.0, 9998.0, 2550.0, 0.45, 123.45, 123.45, 110.2, 105.6],
[1669.875, 9999.0, 2550.0, 0.45, 123.45, 123.45, 110.2, 105.6],
[1669.75, 10000.0, nan, 0.45, 123.45, -999.25, 110.2, 105.6]])
In [17]:
See tests/test_null_policy.py (link) for some examples.
Writing LAS files¶
Any LASFile object can be written to a new LAS file using the
lasio.LASFile.write() method.
Converting between v1.2 and v2.0¶
Take this sample LAS 2.0 file:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ~VERSION INFORMATION
VERS. 2.0 : CWLS LOG ASCII STANDARD -VERSION 2.0
WRAP. NO : ONE LINE PER DEPTH STEP
~WELL INFORMATION
#MNEM.UNIT DATA DESCRIPTION
#----- ----- ---------- -------------------------
STRT .M 1670.0000 :START DEPTH
STOP .M 1660.0000 :STOP DEPTH
STEP .M -0.1250 :STEP
NULL . -999.25 :NULL VALUE
COMP . ANY OIL COMPANY INC. :COMPANY
WELL . AAAAA_2 :WELL
FLD . WILDCAT :FIELD
LOC . 12-34-12-34W5M :LOCATION
PROV . ALBERTA :PROVINCE
SRVC . ANY LOGGING COMPANY INC. :SERVICE COMPANY
DATE . 13-DEC-86 :LOG DATE
UWI . 100123401234W500 :UNIQUE WELL ID
~CURVE INFORMATION
#MNEM.UNIT API CODES CURVE DESCRIPTION
#------------------ ------------ -------------------------
DEPT .M : 1 DEPTH
DT .US/M 60 520 32 00 : 2 SONIC TRANSIT TIME
RHOB .K/M3 45 350 01 00 : 3 BULK DENSITY
NPHI .V/V 42 890 00 00 : 4 NEUTRON POROSITY
SFLU .OHMM 07 220 04 00 : 5 SHALLOW RESISTIVITY
SFLA .OHMM 07 222 01 00 : 6 SHALLOW RESISTIVITY
ILM .OHMM 07 120 44 00 : 7 MEDIUM RESISTIVITY
ILD .OHMM 07 120 46 00 : 8 DEEP RESISTIVITY
~PARAMETER INFORMATION
#MNEM.UNIT VALUE DESCRIPTION
#-------------- ---------------- -----------------------------------------------
MUD . GEL CHEM : MUD TYPE
BHT .DEGC 35.5000 : BOTTOM HOLE TEMPERATURE
BS .MM 200.0000 : BIT SIZE
FD .K/M3 1000.0000 : FLUID DENSITY
MATR . SAND : NEUTRON MATRIX
MDEN . 2710.0000 : LOGGING MATRIX DENSITY
RMF .OHMM 0.2160 : MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0000 : DRILL FLUID DENSITY
~OTHER
Note: The logging tools became stuck at 625 metres causing the data
between 625 metres and 615 metres to be invalid.
~A DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
1670.000 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
1669.875 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
1669.750 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
|
And we can use lasio to convert it to LAS 1.2:
In [31]: las = lasio.read("tests/examples/2.0/sample_2.0.las")
In [33]: las.write('example-as-v1.2.las', version=1.2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : ONE LINE PER DEPTH STEP
~Well ------------------------------------------------------
STRT.M 1670.0 : START DEPTH
STOP.M 1669.75 : STOP DEPTH
STEP.M -0.125 : STEP
NULL. -999.25 : NULL VALUE
COMP. COMPANY : ANY OIL COMPANY INC.
WELL. WELL : AAAAA_2
FLD . FIELD : WILDCAT
LOC . LOCATION : 12-34-12-34W5M
PROV. PROVINCE : ALBERTA
SRVC. SERVICE COMPANY : ANY LOGGING COMPANY INC.
DATE. LOG DATE : 13-DEC-86
UWI . UNIQUE WELL ID : 100123401234W500
~Curves ----------------------------------------------------
DEPT.M : 1 DEPTH
DT .US/M 60 520 32 00 : 2 SONIC TRANSIT TIME
RHOB.K/M3 45 350 01 00 : 3 BULK DENSITY
NPHI.V/V 42 890 00 00 : 4 NEUTRON POROSITY
SFLU.OHMM 07 220 04 00 : 5 SHALLOW RESISTIVITY
SFLA.OHMM 07 222 01 00 : 6 SHALLOW RESISTIVITY
ILM .OHMM 07 120 44 00 : 7 MEDIUM RESISTIVITY
ILD .OHMM 07 120 46 00 : 8 DEEP RESISTIVITY
~Params ----------------------------------------------------
MUD . GEL CHEM : MUD TYPE
BHT .DEGC 35.5 : BOTTOM HOLE TEMPERATURE
BS .MM 200.0 : BIT SIZE
FD .K/M3 1000.0 : FLUID DENSITY
MATR. SAND : NEUTRON MATRIX
MDEN. 2710.0 : LOGGING MATRIX DENSITY
RMF .OHMM 0.216 : MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0 : DRILL FLUID DENSITY
~Other -----------------------------------------------------
Note: The logging tools became stuck at 625 metres causing the data
between 625 metres and 615 metres to be invalid.
~ASCII -----------------------------------------------------
1670 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.9 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.8 123.45 2550 0.45 123.45 123.45 110.2 105.6
|
Converting between wrapped/unwrapped¶
Here is an example using this file to convert a wrapped data section to unwrapped.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ~Version Information
VERS. 1.20: CWLS log ASCII Standard -VERSION 1.20
WRAP. YES: Multiple lines per depth step
~Well Information
#MNEM.UNIT Data Type Information
#--------- ------------- ------------------------------
STRT.M 910.000:
STOP.M 901.000:
STEP.M -0.1250:
NULL. -999.2500: Null value
COMP. COMPANY: ANY OIL COMPANY INC.
WELL. WELL: ANY ET AL XX-XX-XX-XX
FLD . FIELD: WILDCAT
LOC . LOCATION: XX-XX-XX-XXW3M
PROV. PROVINCE: SASKATCHEWAN
SRVC. SERVICE COMPANY: ANY LOGGING COMPANY INC.
SON . SERVICE ORDER : 142085
DATE. LOG DATE: 13-DEC-86
UWI . UNIQUE WELL ID:
~Curve Information
#MNEM.UNIT API CODE Curve Description
#--------- ------------- ------------------------------
DEPT.M : Depth
DT .US/M : 1 Sonic Travel Time
RHOB.K/M : 2 Density-Bulk Density
NPHI.V/V : 3 Porosity -Neutron
RX0 .OHMM : 4 Resistivity -Rxo
RESS.OHMM : 5 Resistivity -Shallow
RESM.OHMM : 6 Resistivity -Medium
RESD.OHMM : 7 Resistivity -Deep
SP .MV : 8 Spon. Potential
GR .GAPI : 9 Gamma Ray
CALI.MM : 10 Caliper
DRHO.K/M3 : 11 Delta-Rho
EATT.DBM : 12 EPT Attenuation
TPL .NS/M : 13 TP -EPT
PEF . : 14 PhotoElectric Factor
FFI .V/V : 15 Porosity -NML FFI
DCAL.MM : 16 Caliper-Differential
RHGF.K/M3 : 17 Density-Formation
RHGA.K/M3 : 18 Density-Apparent
SPBL.MV : 19 Baselined SP
GRC .GAPI : 20 Gamma Ray BHC
PHIA.V/V : 21 Porosity -Apparent
PHID.V/V : 22 Porosity -Density
PHIE.V/V : 23 Porosity -Effective
PHIN.V/V : 24 Porosity -Neut BHC
PHIC.V/V : 25 Porosity -Total HCC
R0 .OHMM : 26 Ro
RWA .OHMM : 27 Rfa
SW . : 28 Sw -Effective
MSI . : 29 Sh Idx -Min
BVW . : 30 BVW
FGAS. : 31 Flag -Gas Index
PIDX. : 32 Prod Idx
FBH . : 33 Flag -Bad Hole
FHCC. : 34 Flag -HC Correction
LSWB. : 35 Flag -Limit SWB
~A Log data section
910.000000
-999.2500 2692.7075 0.3140 19.4086 19.4086 13.1709 12.2681
-1.5010 96.5306 204.7177 30.5822 -999.2500 -999.2500 3.2515
-999.2500 4.7177 3025.0264 3025.0264 -1.5010 93.1378 0.1641
0.0101 0.1641 0.3140 0.1641 11.1397 0.3304 0.9529
0.0000 0.1564 0.0000 11.1397 0.0000 0.0000 0.0000
909.875000
-999.2500 2712.6460 0.2886 23.3987 23.3987 13.6129 12.4744
-1.4720 90.2803 203.1093 18.7566 -999.2500 -999.2500 3.7058
-999.2500 3.1093 3004.6050 3004.6050 -1.4720 86.9078 0.1456
-0.0015 0.1456 0.2886 0.1456 14.1428 0.2646 1.0000
0.0000 0.1456 0.0000 14.1428 0.0000 0.0000 0.0000
909.750000
-999.2500 2692.8137 0.2730 22.5909 22.5909 13.6821 12.6146
-1.4804 89.8492 201.9287 3.1551 -999.2500 -999.2500 4.3124
-999.2500 1.9287 2976.4451 2976.4451 -1.4804 86.3465 0.1435
0.0101 0.1435 0.2730 0.1435 14.5674 0.2598 1.0000
0.0000 0.1435 0.0000 14.5674 0.0000 0.0000 0.0000
909.625000
-999.2500 2644.3650 0.2765 18.4831 18.4831 13.4159 12.6900
-1.5010 93.3999 201.5826 -6.5861 -999.2500 -999.2500 4.3822
-999.2500 1.5826 2955.3528 2955.3528 -1.5010 89.7142 0.1590
0.0384 0.1590 0.2765 0.1590 11.8600 0.3210 0.9667
0.0000 0.1538 0.0000 11.8600 0.0000 0.0000 0.0000
909.500000
-999.2500 2586.2822 0.2996 13.9187 13.9187 12.9195 12.7016
-1.4916 98.1214 201.7126 -4.5574 -999.2500 -999.2500 3.5967
-999.2500 1.7126 2953.5940 2953.5940 -1.4916 94.2670 0.1880
0.0723 0.1880 0.2996 0.1880 8.4863 0.4490 0.8174
0.0000 0.1537 0.0000 8.4863 0.0000 0.0000 0.0000
|
We will change the wrap by adjusting the relevant header section in the LASFile header:
In [26]: las.version
Out[26]:
[HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=CWLS log ASCII Standard -VERSION 1.20, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=YES, descr=Multiple lines per depth step, original_mnemonic=WRAP)]
In [27]: las.version.WRAP = 'NO'
In [28]: las.version.WRAP
Out[28]: HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=Multiple lines per depth step, original_mnemonic=WRAP)
In [29]: las.write('example-unwrapped.las')
WARNING:lasio.writer:[v1.2] line #58 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #59 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #60 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #61 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #62 has 396 chars (>256)
We get warnings because the LAS 1.2 standard doesn’t allow writing lines longer
than 256 characters. lasio provides the warning but still produces the long
lines:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : Multiple lines per depth step
~Well ------------------------------------------------------
STRT.M 910.0 :
STOP.M 909.5 :
STEP.M -0.125 :
NULL. -999.25 : Null value
COMP. COMPANY : ANY OIL COMPANY INC.
WELL. WELL : ANY ET AL XX-XX-XX-XX
FLD . FIELD : WILDCAT
LOC . LOCATION : XX-XX-XX-XXW3M
PROV. PROVINCE : SASKATCHEWAN
SRVC. SERVICE COMPANY : ANY LOGGING COMPANY INC.
SON . SERVICE ORDER : 142085
DATE. LOG DATE : 13-DEC-86
UWI . UNIQUE WELL ID :
~Curves ----------------------------------------------------
DEPT.M : Depth
DT .US/M : 1 Sonic Travel Time
RHOB.K/M : 2 Density-Bulk Density
NPHI.V/V : 3 Porosity -Neutron
RX0 .OHMM : 4 Resistivity -Rxo
RESS.OHMM : 5 Resistivity -Shallow
RESM.OHMM : 6 Resistivity -Medium
RESD.OHMM : 7 Resistivity -Deep
SP .MV : 8 Spon. Potential
GR .GAPI : 9 Gamma Ray
CALI.MM : 10 Caliper
DRHO.K/M3 : 11 Delta-Rho
EATT.DBM : 12 EPT Attenuation
TPL .NS/M : 13 TP -EPT
PEF . : 14 PhotoElectric Factor
FFI .V/V : 15 Porosity -NML FFI
DCAL.MM : 16 Caliper-Differential
RHGF.K/M3 : 17 Density-Formation
RHGA.K/M3 : 18 Density-Apparent
SPBL.MV : 19 Baselined SP
GRC .GAPI : 20 Gamma Ray BHC
PHIA.V/V : 21 Porosity -Apparent
PHID.V/V : 22 Porosity -Density
PHIE.V/V : 23 Porosity -Effective
PHIN.V/V : 24 Porosity -Neut BHC
PHIC.V/V : 25 Porosity -Total HCC
R0 .OHMM : 26 Ro
RWA .OHMM : 27 Rfa
SW . : 28 Sw -Effective
MSI . : 29 Sh Idx -Min
BVW . : 30 BVW
FGAS. : 31 Flag -Gas Index
PIDX. : 32 Prod Idx
FBH . : 33 Flag -Bad Hole
FHCC. : 34 Flag -HC Correction
LSWB. : 35 Flag -Limit SWB
~Params ----------------------------------------------------
~Other -----------------------------------------------------
~ASCII -----------------------------------------------------
910 -999.25 2692.7 0.314 19.409 19.409 13.171 12.268 -1.501 96.531 204.72 30.582 -999.25 -999.25 3.2515 -999.25 4.7177 3025 3025 -1.501 93.138 0.1641 0.0101 0.1641 0.314 0.1641 11.14 0.3304 0.9529 0 0.1564 0 11.14 0 0 0
909.88 -999.25 2712.6 0.2886 23.399 23.399 13.613 12.474 -1.472 90.28 203.11 18.757 -999.25 -999.25 3.7058 -999.25 3.1093 3004.6 3004.6 -1.472 86.908 0.1456 -0.0015 0.1456 0.2886 0.1456 14.143 0.2646 1 0 0.1456 0 14.143 0 0 0
909.75 -999.25 2692.8 0.273 22.591 22.591 13.682 12.615 -1.4804 89.849 201.93 3.1551 -999.25 -999.25 4.3124 -999.25 1.9287 2976.4 2976.4 -1.4804 86.347 0.1435 0.0101 0.1435 0.273 0.1435 14.567 0.2598 1 0 0.1435 0 14.567 0 0 0
909.62 -999.25 2644.4 0.2765 18.483 18.483 13.416 12.69 -1.501 93.4 201.58 -6.5861 -999.25 -999.25 4.3822 -999.25 1.5826 2955.4 2955.4 -1.501 89.714 0.159 0.0384 0.159 0.2765 0.159 11.86 0.321 0.9667 0 0.1538 0 11.86 0 0 0
909.5 -999.25 2586.3 0.2996 13.919 13.919 12.919 12.702 -1.4916 98.121 201.71 -4.5574 -999.25 -999.25 3.5967 -999.25 1.7126 2953.6 2953.6 -1.4916 94.267 0.188 0.0723 0.188 0.2996 0.188 8.4863 0.449 0.8174 0 0.1537 0 8.4863 0 0 0
|
If we decide to write the file in LAS 2.0 format, the warnings will go away:
In [23]: las.write('example-version-2.0.las', version=2.0)
In [24]:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ~Version ---------------------------------------------------
VERS. 2.0 : CWLS log ASCII Standard -VERSION 2.0
WRAP. NO : Multiple lines per depth step
~Well ------------------------------------------------------
STRT.M 910.0 :
STOP.M 909.5 :
STEP.M -0.125 :
NULL. -999.25 : Null value
COMP. ANY OIL COMPANY INC. : COMPANY
WELL. ANY ET AL XX-XX-XX-XX : WELL
FLD . WILDCAT : FIELD
LOC . XX-XX-XX-XXW3M : LOCATION
PROV. SASKATCHEWAN : PROVINCE
SRVC. ANY LOGGING COMPANY INC. : SERVICE COMPANY
SON . 142085 : SERVICE ORDER
DATE. 13-DEC-86 : LOG DATE
UWI . : UNIQUE WELL ID
~Curves ----------------------------------------------------
DEPT.M : Depth
DT .US/M : 1 Sonic Travel Time
RHOB.K/M : 2 Density-Bulk Density
NPHI.V/V : 3 Porosity -Neutron
RX0 .OHMM : 4 Resistivity -Rxo
RESS.OHMM : 5 Resistivity -Shallow
RESM.OHMM : 6 Resistivity -Medium
RESD.OHMM : 7 Resistivity -Deep
SP .MV : 8 Spon. Potential
GR .GAPI : 9 Gamma Ray
CALI.MM : 10 Caliper
DRHO.K/M3 : 11 Delta-Rho
EATT.DBM : 12 EPT Attenuation
TPL .NS/M : 13 TP -EPT
PEF . : 14 PhotoElectric Factor
FFI .V/V : 15 Porosity -NML FFI
DCAL.MM : 16 Caliper-Differential
RHGF.K/M3 : 17 Density-Formation
RHGA.K/M3 : 18 Density-Apparent
SPBL.MV : 19 Baselined SP
GRC .GAPI : 20 Gamma Ray BHC
PHIA.V/V : 21 Porosity -Apparent
PHID.V/V : 22 Porosity -Density
PHIE.V/V : 23 Porosity -Effective
PHIN.V/V : 24 Porosity -Neut BHC
PHIC.V/V : 25 Porosity -Total HCC
R0 .OHMM : 26 Ro
RWA .OHMM : 27 Rfa
SW . : 28 Sw -Effective
MSI . : 29 Sh Idx -Min
BVW . : 30 BVW
FGAS. : 31 Flag -Gas Index
PIDX. : 32 Prod Idx
FBH . : 33 Flag -Bad Hole
FHCC. : 34 Flag -HC Correction
LSWB. : 35 Flag -Limit SWB
~Params ----------------------------------------------------
~Other -----------------------------------------------------
~ASCII -----------------------------------------------------
910 -999.25 2692.7 0.314 19.409 19.409 13.171 12.268 -1.501 96.531 204.72 30.582 -999.25 -999.25 3.2515 -999.25 4.7177 3025 3025 -1.501 93.138 0.1641 0.0101 0.1641 0.314 0.1641 11.14 0.3304 0.9529 0 0.1564 0 11.14 0 0 0
909.88 -999.25 2712.6 0.2886 23.399 23.399 13.613 12.474 -1.472 90.28 203.11 18.757 -999.25 -999.25 3.7058 -999.25 3.1093 3004.6 3004.6 -1.472 86.908 0.1456 -0.0015 0.1456 0.2886 0.1456 14.143 0.2646 1 0 0.1456 0 14.143 0 0 0
909.75 -999.25 2692.8 0.273 22.591 22.591 13.682 12.615 -1.4804 89.849 201.93 3.1551 -999.25 -999.25 4.3124 -999.25 1.9287 2976.4 2976.4 -1.4804 86.347 0.1435 0.0101 0.1435 0.273 0.1435 14.567 0.2598 1 0 0.1435 0 14.567 0 0 0
909.62 -999.25 2644.4 0.2765 18.483 18.483 13.416 12.69 -1.501 93.4 201.58 -6.5861 -999.25 -999.25 4.3822 -999.25 1.5826 2955.4 2955.4 -1.501 89.714 0.159 0.0384 0.159 0.2765 0.159 11.86 0.321 0.9667 0 0.1538 0 11.86 0 0 0
909.5 -999.25 2586.3 0.2996 13.919 13.919 12.919 12.702 -1.4916 98.121 201.71 -4.5574 -999.25 -999.25 3.5967 -999.25 1.7126 2953.6 2953.6 -1.4916 94.267 0.188 0.0723 0.188 0.2996 0.188 8.4863 0.449 0.8174 0 0.1537 0 8.4863 0 0 0
|
Exporting to other formats¶
The following examples all use sample.las:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ~VERSION INFORMATION
VERS. 1.2: CWLS LOG ASCII STANDARD -VERSION 1.2
WRAP. NO: ONE LINE PER DEPTH STEP
~WELL INFORMATION BLOCK
#MNEM.UNIT DATA TYPE INFORMATION
#--------- ------------- ------------------------------
STRT.M 1670.000000:
STOP.M 1660.000000:
STEP.M -0.1250:
NULL. -999.2500:
COMP. COMPANY: # ANY OIL COMPANY LTD.
WELL. WELL: ANY ET AL OIL WELL #12
FLD . FIELD: EDAM
LOC . LOCATION: A9-16-49-20W3M
PROV. PROVINCE: SASKATCHEWAN
SRVC. SERVICE COMPANY: ANY LOGGING COMPANY LTD.
DATE. LOG DATE: 25-DEC-1988
UWI . UNIQUE WELL ID: 100091604920W300
~CURVE INFORMATION
#MNEM.UNIT API CODE CURVE DESCRIPTION
#--------- ------------- ------------------------------
DEPT.M : 1 DEPTH
DT .US/M : 2 SONIC TRANSIT TIME
RHOB.K/M3 : 3 BULK DENSITY
NPHI.V/V : 4 NEUTRON POROSITY
SFLU.OHMM : 5 RXO RESISTIVITY
SFLA.OHMM : 6 SHALLOW RESISTIVITY
ILM .OHMM : 7 MEDIUM RESISTIVITY
ILD .OHMM : 8 DEEP RESISTIVITY
~PARAMETER INFORMATION
#MNEM.UNIT VALUE DESCRIPTION
#--------- ------------- ------------------------------
BHT .DEGC 35.5000: BOTTOM HOLE TEMPERATURE
BS .MM 200.0000: BIT SIZE
FD .K/M3 1000.0000: FLUID DENSITY
MATR. 0.0000: NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO)
MDEN. 2710.0000: LOGGING MATRIX DENSITY
RMF .OHMM 0.2160: MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0000: DRILL FLUID DENSITY
~Other
Note: The logging tools became stuck at 625 meters causing the data
between 625 meters and 615 meters to be invalid.
~A DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
1670.000 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
1669.875 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
1669.750 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
|
Comma-separated values (CSV)¶
LASFile objects can be converted to CSV files with a few options for how
mnemonics and units are included (or not). It uses the
lasio.las.LASFile.to_csv() method.
In [3]: import lasio
In [4]: las = lasio.read('tests/examples/sample.las')
In [6]: las.to_csv('sample.csv')
1 2 3 4 5 | DEPT,DT,RHOB,NPHI,SFLU,SFLA,ILM,ILD
M,US/M,K/M3,V/V,OHMM,OHMM,OHMM,OHMM
1670.0,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.875,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.75,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
|
There are options for putting the units together with mnemonics:
In [7]: las.to_csv('sample.csv', units_loc='[]')
1 2 3 4 | DEPT [M],DT [US/M],RHOB [K/M3],NPHI [V/V],SFLU [OHMM],SFLA [OHMM],ILM [OHMM],ILD [OHMM]
1670.0,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.875,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.75,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
|
Or leaving things out altogether:
In [11]: las.to_csv('sample.csv', mnemonics=False, units=False)
1 2 3 | 1670.0,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.875,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.75,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
|
Excel spreadsheet (XLSX)¶
You can easily convert LAS files into Excel, retaining the header information.
If we are working in Python, you export like this:
In [58]: las = lasio.read('tests/examples/sample.las')
In [59]: las.to_excel('sample.xlsx')
You will need to have openpyxl
installed ($ pip install openpyxl).
Format of exported Excel file¶
The exported spreadsheet has two sheets named “Header” and “Curves”. The “Header” sheet has five columns named “Section”, “Mnemonic”, “Unit”, “Value”, and “Description”, containing the information from all the sections in the header.
The “Curves” sheet contains the data as a table, with the curve mnemonics as a header row.
Script interfaces¶
Single file¶
(py36) C:\Program Files (x86)\Misc\kentcode\lasio>las2excel --help
usage: Convert LAS file to XLSX [-h] LAS_filename XLSX_filename
positional arguments:
LAS_filename
XLSX_filename
optional arguments:
-h, --help show this help message and exit
(py36) C:\Program Files (x86)\Misc\kentcode\lasio>las2excel tests\examples\sample.las c:\users\kinverarity\Desktop\sample.xlsx
Multiple files (las2excelbulk)¶
The better script to use is las2excelbulk:
(py36) C:\Windows\System32>las2excelbulk --help
usage: Convert LAS files to XLSX [-h] [-g GLOB] [-r] [-i] path
positional arguments:
path
optional arguments:
-h, --help show this help message and exit
-g GLOB, --glob GLOB Match LAS files with this pattern (default: *.las)
-r, --recursive Recurse through subfolders. (default: False)
-i, --ignore-header-errors
Ignore header section errors. (default: False)
Here is the command to create Excel versions of all the LAS files contained within the folder
test_folder, and any sub-folders:
(py36) C:\Users\kinverarity\Documents\scratch2017\November>las2excelbulk --recursive test_folder
Converting test_folder\-2793 & -2746\5086\PN41497.LAS -> test_folder\-2793 & -2746\5086\pn41497.xlsx
Converting test_folder\-2793 & -2746\5149\PN41497.LAS -> test_folder\-2793 & -2746\5149\pn41497.xlsx
Converting test_folder\-2794\6356\66302794.las -> test_folder\-2794\6356\66302794.xlsx
Converting test_folder\-2794\6808\66302794.las -> test_folder\-2794\6808\66302794.xlsx
Converting test_folder\-2794\7608\2794HYD.LAS -> test_folder\-2794\7608\2794hyd.xlsx
Converting test_folder\-2794\7608\66302794.LAS -> test_folder\-2794\7608\66302794.xlsx
Failed to convert file. Error message:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\excel.py", line 133, in main_bulk
l = las.LASFile(lasfn)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 77, in __init__
self.read(file_ref, **read_kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 204, in read
data = np.reshape(arr, (-1, n_arr_cols))
File "C:\Program Files (x86)\Miniconda3\envs\py36\lib\site-packages\numpy\core\fromnumeric.py", line 232, in reshape
return _wrapfunc(a, 'reshape', newshape, order=order)
File "C:\Program Files (x86)\Miniconda3\envs\py36\lib\site-packages\numpy\core\fromnumeric.py", line 57, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 25708 into shape (11)
Converting test_folder\-2794\7627\clr105.las -> test_folder\-2794\7627\clr105.xlsx
Converting test_folder\-2839 &c\4830\PN36385.LAS -> test_folder\-2839 &c\4830\pn36385.xlsx
Converting test_folder\-2874\6375\66302874.las -> test_folder\-2874\6375\66302874.xlsx
Converting test_folder\-2874\7607\2874HYD.LAS -> test_folder\-2874\7607\2874hyd.xlsx
Converting test_folder\-2874\7607\66302874.LAS -> test_folder\-2874\7607\66302874.xlsx
Failed to convert file. Error message:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\excel.py", line 133, in main_bulk
l = las.LASFile(lasfn)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 77, in __init__
self.read(file_ref, **read_kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 204, in read
data = np.reshape(arr, (-1, n_arr_cols))
File "C:\Program Files (x86)\Miniconda3\envs\py36\lib\site-packages\numpy\core\fromnumeric.py", line 232, in reshape
return _wrapfunc(a, 'reshape', newshape, order=order)
File "C:\Program Files (x86)\Miniconda3\envs\py36\lib\site-packages\numpy\core\fromnumeric.py", line 57, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 31666 into shape (16)
Converting test_folder\-2874\7626\clr121.las -> test_folder\-2874\7626\clr121.xlsx
Converting test_folder\-2875\5220\1cm\PN44456.LAS -> test_folder\-2875\5220\1cm\pn44456.xlsx
Converting test_folder\-2875\5220\5cm\PN44456.LAS -> test_folder\-2875\5220\5cm\pn44456.xlsx
Converting test_folder\-2875\5220\980402\PN44456.LAS -> test_folder\-2875\5220\980402\pn44456.xlsx
Converting test_folder\-2875\5220\980403_0\PN44456.LAS -> test_folder\-2875\5220\980403_0\pn44456.xlsx
Converting test_folder\-2875\5220\980403_1\PN44456.LAS -> test_folder\-2875\5220\980403_1\pn44456.xlsx
Converting test_folder\-2875\5220\cal1cm\PN44456.LAS -> test_folder\-2875\5220\cal1cm\pn44456.xlsx
Converting test_folder\-2875\5220\cal5cm\PN44456.LAS -> test_folder\-2875\5220\cal5cm\pn44456.xlsx
Converting test_folder\-2875\5220\tm2\PN44456.LAS -> test_folder\-2875\5220\tm2\pn44456.xlsx
Converting test_folder\-2875\6813\2875HYD.LAS -> test_folder\-2875\6813\2875hyd.xlsx
Header section Parameter regexp=~P was not found.
Converting test_folder\-2875\6813\66302875.LAS -> test_folder\-2875\6813\66302875.xlsx
Converting test_folder\-2876\5219\PN44457.LAS -> test_folder\-2876\5219\pn44457.xlsx
Converting test_folder\-2876\5219\PN44457H.LAS -> test_folder\-2876\5219\pn44457h.xlsx
Converting test_folder\-2876\5219\PN44457I.LAS -> test_folder\-2876\5219\pn44457i.xlsx
Converting test_folder\-2876\7609\2876H.LAS -> test_folder\-2876\7609\2876h.xlsx
Converting test_folder\-2876\7609\66302876.LAS -> test_folder\-2876\7609\66302876.xlsx
Failed to convert file. Error message:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\excel.py", line 133, in main_bulk
l = las.LASFile(lasfn)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 77, in __init__
self.read(file_ref, **read_kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 204, in read
data = np.reshape(arr, (-1, n_arr_cols))
File "C:\Program Files (x86)\Miniconda3\envs\py36\lib\site-packages\numpy\core\fromnumeric.py", line 232, in reshape
return _wrapfunc(a, 'reshape', newshape, order=order)
File "C:\Program Files (x86)\Miniconda3\envs\py36\lib\site-packages\numpy\core\fromnumeric.py", line 57, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 19791 into shape (11)
Converting test_folder\-2876\7629\clr120.las -> test_folder\-2876\7629\clr120.xlsx
Converting test_folder\-2877\7597\CLR118.LAS -> test_folder\-2877\7597\clr118.xlsx
Converting test_folder\-2877\7628\clr118.las -> test_folder\-2877\7628\clr118.xlsx
Converting test_folder\-3066\6372\66303066.las -> test_folder\-3066\6372\66303066.xlsx
Converting test_folder\-3066\6810\3066HYD.LAS -> test_folder\-3066\6810\3066hyd.xlsx
Converting test_folder\-3066\6810\66303066.LAS -> test_folder\-3066\6810\66303066.xlsx
Converting test_folder\-3067\6373\66303067.las -> test_folder\-3067\6373\66303067.xlsx
Converting test_folder\-3067\6811\3067HYD.LAS -> test_folder\-3067\6811\3067hyd.xlsx
Converting test_folder\-3067\6811\66303067.LAS -> test_folder\-3067\6811\66303067.xlsx
Header section Parameter regexp=~P was not found.
Converting test_folder\-3068\6374\66303068.las -> test_folder\-3068\6374\66303068.xlsx
Converting test_folder\-3068\6812\3068HYD.LAS -> test_folder\-3068\6812\3068hyd.xlsx
Converting test_folder\-3068\6812\66303068.LAS -> test_folder\-3068\6812\66303068.xlsx
Notice that some LAS files raised exceptions (in this case, ValueError) and were not converted. In some cases these will relate to errors in the header sections:
(py36) Q:\>las2excelbulk.exe -r .
Converting .\4424\PN31769.LAS -> .\4424\pn31769.xlsx
Converting .\4424\PN31769L.LAS -> .\4424\pn31769l.xlsx
Converting .\4424\PN31769R.LAS -> .\4424\pn31769r.xlsx
Converting .\4428\pn31769.las -> .\4428\pn31769.xlsx
Failed to convert file. Error message:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 366, in parse_header_section
values = read_line(line)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 522, in read_line
return read_header_line(*args, **kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 548, in read_header_line
mdict = m.groupdict()
AttributeError: 'NoneType' object has no attribute 'groupdict'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\excel.py", line 133, in main_bulk
l = las.LASFile(lasfn, ignore_header_errors=args.ignore_header_errors)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 77, in __init__
self.read(file_ref, **read_kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 156, in read
ignore_header_errors=ignore_header_errors)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 110, in add_section
**sect_kws)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 375, in parse_header_section
raise exceptions.LASHeaderError(message)
lasio.exceptions.LASHeaderError: Line #21 - failed in ~Well Information section on line:
PN PERMIT NUMBER: 31769AttributeError: 'NoneType' object has no attribute 'groupdict'
Converting .\4526\PENRICE.LAS -> .\4526\penrice.xlsx
But in this case I’m happy to lose that single corrupted line in the header in the conversion. In order to force lasio to ignore the error and continue to convert the file, use the --ignore-header-errors flag (-i for short):
(py36) Q:\>las2excelbulk.exe -r -i .
Converting .\4424\PN31769.LAS -> .\4424\pn31769.xlsx
Converting .\4424\PN31769L.LAS -> .\4424\pn31769l.xlsx
Converting .\4424\PN31769R.LAS -> .\4424\pn31769r.xlsx
Converting .\4428\pn31769.las -> .\4428\pn31769.xlsx
Line #21 - failed in ~Well Information section on line:
PN PERMIT NUMBER: 31769AttributeError: 'NoneType' object has no attribute 'groupdict'
Converting .\4526\PENRICE.LAS -> .\4526\penrice.xlsx
lasio still reports the problem, but ignores it and continues the conversion of the file.
Building a LAS file from scratch¶
When you create a LASFile from scratch, it comes with some default metadata:
In [5]: import lasio
In [6]: las = lasio.LASFile()
In [7]: las.header
Out[7]:
{'Curves': [],
'Other': '',
'Parameter': [],
'Version': [HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS log ASCII Standard -VERSION 2.0, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=One line per depth step, original_mnemonic=WRAP),
HeaderItem(mnemonic=DLM, unit=, value=SPACE, descr=Column Data Section Delimiter, original_mnemonic=DLM)],
'Well': [HeaderItem(mnemonic=STRT, unit=m, value=nan, descr=START DEPTH, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=m, value=nan, descr=STOP DEPTH, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=m, value=nan, descr=STEP, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-9999.25, descr=NULL VALUE, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=, descr=COMPANY, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=FLD, unit=, value=, descr=FIELD, original_mnemonic=FLD),
HeaderItem(mnemonic=LOC, unit=, value=, descr=LOCATION, original_mnemonic=LOC),
HeaderItem(mnemonic=PROV, unit=, value=, descr=PROVINCE, original_mnemonic=PROV),
HeaderItem(mnemonic=CNTY, unit=, value=, descr=COUNTY, original_mnemonic=CNTY),
HeaderItem(mnemonic=STAT, unit=, value=, descr=STATE, original_mnemonic=STAT),
HeaderItem(mnemonic=CTRY, unit=, value=, descr=COUNTRY, original_mnemonic=CTRY),
HeaderItem(mnemonic=SRVC, unit=, value=, descr=SERVICE COMPANY, original_mnemonic=SRVC),
HeaderItem(mnemonic=DATE, unit=, value=, descr=DATE, original_mnemonic=DATE),
HeaderItem(mnemonic=UWI, unit=, value=, descr=UNIQUE WELL ID, original_mnemonic=UWI),
HeaderItem(mnemonic=API, unit=, value=, descr=API NUMBER, original_mnemonic=API)]}
In our case, let’s set the correct date:
In [8]: from datetime import datetime
In [9]: las.well.DATE = datetime.today().strftime('%Y-%m-%d %H:%M:%S')
And add some new header fields:
In [10]: las.params['ENG'] = lasio.HeaderItem('ENG', value='Kent Inverarity')
In [11]: las.params['LMF'] = lasio.HeaderItem('LMF', value='GL')
In [12]: las.other = 'Example of how to create a LAS file from scratch using lasio'
We will invent some data for a curve:
In [1]: import numpy as np
In [2]: depths = np.arange(10, 50, 0.5)
In [3]: synth = np.log10(depths)*5+np.random.random(len(depths))
In [4]: synth[:8] = np.nan
…add these to the LASFile object:
In [13]: las.add_curve('DEPT', depths, unit='m')
In [14]: las.add_curve('SYNTH', synth, descr='fake data')
And write the result to files:
In [16]: las.write('scratch_v1.2.las', version=1.2)
In [15]: las.write('scratch_v2.las', version=2)
Here is the resulting scratch_v1.2.las:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : One line per depth step
DLM . SPACE : Column Data Section Delimiter
~Well ------------------------------------------------------
STRT.m 10.0 : START DEPTH
STOP.m 49.5 : STOP DEPTH
STEP.m 0.5 : STEP
NULL. -9999.25 : NULL VALUE
COMP. COMPANY :
WELL. WELL :
FLD . FIELD :
LOC . LOCATION :
PROV. PROVINCE :
CNTY. COUNTY :
STAT. STATE :
CTRY. COUNTRY :
SRVC. SERVICE COMPANY :
DATE. DATE : 2017-11-04 15:33:20.963287
UWI . UNIQUE WELL ID :
API . API NUMBER :
~Curves ----------------------------------------------------
DEPT .m :
SYNTH. : fake data
~Params ----------------------------------------------------
ENG. Kent Inverarity :
LMF. GL :
~Other -----------------------------------------------------
Example of how to create a LAS file from scratch using lasio
~ASCII -----------------------------------------------------
10 -9999.25
10.5 -9999.25
11 -9999.25
11.5 -9999.25
12 -9999.25
12.5 -9999.25
13 -9999.25
13.5 -9999.25
14 5.799
14.5 6.3938
15 6.4122
15.5 6.4605
16 6.9518
16.5 6.567
17 6.3816
17.5 6.2872
18 6.4336
18.5 7.0252
19 6.7988
19.5 6.7172
20 6.6929
20.5 7.0971
21 7.145
21.5 6.7192
22 7.6034
22.5 7.3078
23 7.2213
23.5 7.668
24 7.853
24.5 7.4073
25 7.4238
25.5 7.9173
26 7.1282
26.5 7.4131
27 7.8014
27.5 7.348
28 7.9
28.5 7.6294
29 8.1244
29.5 7.9835
30 7.4759
30.5 8.3766
31 7.4717
31.5 7.6432
32 8.2327
32.5 7.6541
33 8.4481
33.5 7.8811
34 8.2332
34.5 8.4302
35 7.7218
35.5 8.71
36 8.3965
36.5 8.4355
37 8.6836
37.5 8.2236
38 8.4997
38.5 8.6656
39 8.8295
39.5 8.1707
40 8.9034
40.5 8.681
41 8.1698
41.5 8.3001
42 9.0266
42.5 8.4398
43 8.7562
43.5 8.2673
44 8.4682
44.5 8.5801
45 8.9065
45.5 8.8392
46 8.661
46.5 9.2355
47 9.0468
47.5 8.8249
48 9.0298
48.5 8.6864
49 8.5745
49.5 8.6143
|
and scratch_v2.las:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ~Version ---------------------------------------------------
VERS. 2.0 : CWLS log ASCII Standard -VERSION 2.0
WRAP. NO : One line per depth step
DLM . SPACE : Column Data Section Delimiter
~Well ------------------------------------------------------
STRT.m 10.0 : START DEPTH
STOP.m 49.5 : STOP DEPTH
STEP.m 0.5 : STEP
NULL. -9999.25 : NULL VALUE
COMP. : COMPANY
WELL. : WELL
FLD . : FIELD
LOC . : LOCATION
PROV. : PROVINCE
CNTY. : COUNTY
STAT. : STATE
CTRY. : COUNTRY
SRVC. : SERVICE COMPANY
DATE. 2017-11-04 15:33:20.963287 : DATE
UWI . : UNIQUE WELL ID
API . : API NUMBER
~Curves ----------------------------------------------------
DEPT .m :
SYNTH. : fake data
~Params ----------------------------------------------------
ENG. Kent Inverarity :
LMF. GL :
~Other -----------------------------------------------------
Example of how to create a LAS file from scratch using lasio
~ASCII -----------------------------------------------------
10 -9999.25
10.5 -9999.25
11 -9999.25
11.5 -9999.25
12 -9999.25
12.5 -9999.25
13 -9999.25
13.5 -9999.25
14 5.799
14.5 6.3938
15 6.4122
15.5 6.4605
16 6.9518
16.5 6.567
17 6.3816
17.5 6.2872
18 6.4336
18.5 7.0252
19 6.7988
19.5 6.7172
20 6.6929
20.5 7.0971
21 7.145
21.5 6.7192
22 7.6034
22.5 7.3078
23 7.2213
23.5 7.668
24 7.853
24.5 7.4073
25 7.4238
25.5 7.9173
26 7.1282
26.5 7.4131
27 7.8014
27.5 7.348
28 7.9
28.5 7.6294
29 8.1244
29.5 7.9835
30 7.4759
30.5 8.3766
31 7.4717
31.5 7.6432
32 8.2327
32.5 7.6541
33 8.4481
33.5 7.8811
34 8.2332
34.5 8.4302
35 7.7218
35.5 8.71
36 8.3965
36.5 8.4355
37 8.6836
37.5 8.2236
38 8.4997
38.5 8.6656
39 8.8295
39.5 8.1707
40 8.9034
40.5 8.681
41 8.1698
41.5 8.3001
42 9.0266
42.5 8.4398
43 8.7562
43.5 8.2673
44 8.4682
44.5 8.5801
45 8.9065
45.5 8.8392
46 8.661
46.5 9.2355
47 9.0468
47.5 8.8249
48 9.0298
48.5 8.6864
49 8.5745
49.5 8.6143
|
Character encodings¶
There are four options:
1. Specify the encoding (internally lasio uses the open function from codecs which is part of the standard library):
>>> las = lasio.read('example.las', encoding='windows-1252')
2. Do nothing. By default lasio.read() uses the keyword argument
autodetect_encoding=True. This will try to open the file with a few
different encodings, like ‘ascii’, ‘windows-1252’, and ‘latin-1’. The
first one to raise no UnicodeDecodeError exceptions will be used.
This may still result in an error, or incorrectly decoded characters.
3. Install a package like cChardet (faster) or chardet (slower) to automatically detect the character encoding. If these packages are installed then lasio will use them by default:
>>> import logging
>>> logging.basicConfig()
>>> logging.getLogger().setLevel(logging.DEBUG)
>>> las = lasio.read('encodings_utf8.las')
DEBUG:lasio.reader:get_encoding Using cchardet
DEBUG:lasio.reader:cchardet method detected encoding of UTF-8 at confidence 0.9900000095367432
INFO:lasio.reader:Opening encodings_utf8.las as UTF-8 and treating errors with "replace"
DEBUG:lasio.las:n_curves=8 ncols=8
DEBUG:lasio.las:set_data data.shape = (3, 8)
DEBUG:lasio.las:set_data self.data.shape = (3, 8)
This may still result in an error, or incorrectly decoded characters.
If you are certain that you have no “extended characters” (or that you don’t care), you can easily speed up lasio’s performance by using:
>>> try:
... las = lasio.read('example.las', autodetect_encoding=False)
... except UnicodeDecodeError:
... continue
Docstrings for the lasio package¶
Module contents¶
-
lasio.read(file_ref, **kwargs)[source]¶ Read a LAS file.
Note that only versions 1.2 and 2.0 of the LAS file specification are currently supported.
Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. Returns: A LASFile object representing the file – see above There are a number of optional keyword arguments that can be passed to this function that control how the LAS file is opened and parsed. Any of the keyword arguments from the below functions can be used here:
lasio.reader.open_with_codecs()- manage issues relate to character encodingslasio.las.LASFile.read()- control how NULL values and errors are handled during parsing
Submodules¶
lasio.las module¶
-
class
lasio.las.LASFile(file_ref=None, **read_kwargs)[source]¶ Bases:
objectLAS file object.
Keyword Arguments: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. See these routines for additional keyword arguments you can use when reading in a LAS file:
lasio.reader.open_with_codecs()- manage issues relate to character encodingslasio.las.LASFile.read()- control how NULL values and errors are handled during parsing
-
read(file_ref, ignore_data=False, read_policy='default', null_policy='strict', ignore_header_errors=False, mnemonic_case='upper', index_unit=None, **kwargs)[source]¶ Read a LAS file.
Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file.
Keyword Arguments: - null_policy (str or list) – see http://lasio.readthedocs.io/en/latest/data-section.html#handling-invalid-data-indicators-automatically
- ignore_data (bool) – if True, do not read in any of the actual data, just the header metadata. False by default.
- ignore_header_errors (bool) – ignore LASHeaderErrors (False by default)
- mnemonic_case (str) – ‘preserve’: keep the case of HeaderItem mnemonics ‘upper’: convert all HeaderItem mnemonics to uppercase ‘lower’: convert all HeaderItem mnemonics to lowercase
- index_unit (str) – Optionally force-set the index curve’s unit to “m” or “ft”
See
lasio.reader.open_with_codecs()for additional keyword arguments which help to manage issues relate to character encodings.
-
write(file_ref, **kwargs)[source]¶ Write LAS file to disk.
Parameters: file_ref (open file-like object or str) – a file-like object opening for writing, or a filename. All
**kwargsare passed tolasio.writer.write()– please check the docstring of that function for more keyword arguments you can use here!Examples
>>> import lasio >>> las = lasio.read("tests/examples/sample.las") >>> with open('test_output.las', mode='w') as f: ... las.write(f, version=2.0) # <-- this method
-
to_excel(filename)[source]¶ Export LAS file to a Microsoft Excel workbook.
This function will raise an
ImportErrorifopenpyxlis not installed.Parameters: filename (str) –
-
to_csv(file_ref, mnemonics=True, units=True, units_loc='line', **kwargs)[source]¶ Export to a CSV file.
Parameters: file_ref (open file-like object or str) – a file-like object opening for writing, or a filename.
Keyword Arguments: - mnemonics (list, True, False) – write mnemonics as a header line at the start. If list, use the supplied items as mnemonics. If True, use the curve mnemonics.
- units (list, True, False) – as for mnemonics.
- units_loc (str or None) – either ‘line’, ‘[]’ or ‘()’. ‘line’ will put units on the line following the mnemonics (good for WellCAD). ‘[]’ and ‘()’ will put the units in either brackets or parentheses following the mnemonics, on the single header line (better for Excel)
- **kwargs – passed to
csv.writer. Note that iflineterminatoris not specified here, then it will be sent tocsv.writeraslineterminator='\n'.
-
match_raw_section(pattern, re_func='match', flags=<RegexFlag.IGNORECASE: 2>)[source]¶ Find raw section with a regular expression.
Parameters: pattern (str) – regular expression (you need to include the tilde)
Keyword Arguments: - re_func (str) – either “match” or “search”, see python
remodule. - flags (int) – flags for
re.compile()
Returns: dict
Intended for internal use only.
- re_func (str) – either “match” or “search”, see python
-
get_curve(mnemonic)[source]¶ Return CurveItem object.
Parameters: mnemonic (str) – the name of the curve Returns: lasio.las_items.CurveItem(not just the data array)
-
version¶ Header information from the Version (~V) section.
Returns: lasio.las_items.SectionItemsobject.
-
well¶ Header information from the Well (~W) section.
Returns: lasio.las_items.SectionItemsobject.
-
curves¶ Curve information and data from the Curves (~C) and data section..
Returns: lasio.las_items.SectionItemsobject.
-
curvesdict¶ Curve information and data from the Curves (~C) and data section..
Returns: dict
-
params¶ Header information from the Parameter (~P) section.
Returns: lasio.las_items.SectionItemsobject.
-
other¶ Header information from the Other (~O) section.
Returns: str
-
metadata¶ All header information joined together.
Returns: lasio.las_items.SectionItemsobject.
-
header¶ All header information
Returns: dict
-
df()[source]¶ Return data as a
pandas.DataFramestructure.The first Curve of the LASFile object is used as the pandas DataFrame’s index.
-
data¶
-
set_data(array_like, names=None, truncate=False)[source]¶ Set the data for the LAS; actually sets data on individual curves.
Parameters: array_like (array_like or
pandas.DataFrame) – 2-D data arrayKeyword Arguments: - names (list, optional) – used to replace the names of the existing
lasio.las_items.CurveItemobjects. - truncate (bool) – remove any columns which are not included in the Curves (~C) section.
Note: you can pass a
pandas.DataFrameto this method.- names (list, optional) – used to replace the names of the existing
-
set_data_from_df(df, **kwargs)[source]¶ Set the LAS file data from a
pandas.DataFrame.Parameters: df (pandas.DataFrame) – curve mnemonics are the column names. The depth column for the curves must be the index of the DataFrame. Keyword arguments are passed to
lasio.las.LASFile.set_data().
-
stack_curves(mnemonic, sort_curves=True)[source]¶ Stack multi-channel curve data to a numpy 2D ndarray. Provide a stub name (prefix shared by all curves that will be stacked) or a list of curve mnemonic strings.
Keyword Arguments: Returns: 2-D numpy array
-
index¶ Return data from the first column of the LAS file data (depth/time).
-
depth_m¶ Return the index as metres.
-
depth_ft¶ Return the index as feet.
-
add_curve_raw(mnemonic, data, unit='', descr='', value='')[source]¶ Deprecated. Use append_curve_item() or insert_curve_item() instead.
-
insert_curve_item(ix, curve_item)[source]¶ Insert a CurveItem.
Parameters: - ix (int) – position to insert CurveItem i.e. 0 for start
- curve_item (lasio.CurveItem) –
-
append_curve(mnemonic, data, unit='', descr='', value='')[source]¶ Add a curve.
Parameters: - mnemonic (str) – the curve mnemonic
- data (1D ndarray) – the curve data
Keyword Arguments:
-
insert_curve(ix, mnemonic, data, unit='', descr='', value='')[source]¶ Insert a curve.
Parameters: Keyword Arguments:
-
delete_curve(mnemonic=None, ix=None)[source]¶ Delete a curve.
Keyword Arguments: The index takes precedence over the mnemonic.
-
json¶ Return object contents as a JSON string.
-
class
lasio.las.Las(file_ref=None, **read_kwargs)[source]¶ Bases:
lasio.las.LASFileLAS file object.
Retained for backwards compatibility.
-
class
lasio.las.JSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]¶ Bases:
json.encoder.JSONEncoder-
default(obj)[source]¶ Implement this method in a subclass such that it returns a serializable object for
o, or calls the base implementation (to raise aTypeError).For example, to support arbitrary iterators, you could implement default like this:
def default(self, o): try: iterable = iter(o) except TypeError: pass else: return list(iterable) # Let the base class default method raise the TypeError return JSONEncoder.default(self, o)
-
lasio.las_items module¶
-
class
lasio.las_items.HeaderItem(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Bases:
collections.OrderedDictDictionary/namedtuple-style object for a LAS header line.
Parameters: These arguments are available for use as either items or attributes of the object.
-
__init__(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
set_session_mnemonic_only(value)[source]¶ Set the mnemonic for session use.
See source comments for
lasio.las_items.HeaderItem.__init__for a more in-depth explanation.
-
-
class
lasio.las_items.CurveItem(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Bases:
lasio.las_items.HeaderItemDictionary/namedtuple-style object for a LAS curve.
See
lasio.las_items.HeaderItem`for the (keyword) arguments.Keyword Arguments: data (array-like, 1-D) – the curve’s data. -
__init__(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
API_code¶ Equivalent to the
valueattribute.
-
-
class
lasio.las_items.SectionItems(*args, **kwargs)[source]¶ Bases:
listVariant of a
listwhich is used to represent a LAS section.-
__contains__(testitem)[source]¶ Check whether a header item or mnemonic is in the section.
Parameters: testitem (HeaderItem, CurveItem, str) – either an item or a mnemonic Returns: bool
-
__getitem__(key)[source]¶ Item-style access by either mnemonic or index.
Parameters: key (str, int, slice) – either a mnemonic or the index to the list. Returns: item from the list (either HeaderItem or CurveItem)
-
__delitem__(key)[source]¶ Delete item by either mnemonic or index.
Parameters: key (str, int) – either a mnemonic or the index to the list.
-
__setitem__(key, newitem)[source]¶ Either replace the item or its value.
Parameters: - key (int, str) – either the mnemonic or the index.
- newitem (HeaderItem or str/float/int) – the thing to be set.
If
newitemis alasio.las_items.HeaderItemthen the existing item will be replaced. Otherwise the existing item’svalueattribute will be replaced.i.e. this allows us to do
>>> from lasio import SectionItems, HeaderItem >>> section = SectionItems( ... [HeaderItem(mnemonic="OPERATOR", value="John")] ... ) >>> section.OPERATOR HeaderItem(mnemonic=OPERATOR, unit=, value=John, descr=) >>> section.OPERATOR = 'Kent' >>> section.OPERATOR HeaderItem(mnemonic=OPERATOR, unit=, value=Kent, descr=)
See
lasio.las_items.SectionItems.set_item()andlasio.las_items.SectionItems.set_item_value().
-
__getattr__(key)[source]¶ Provide attribute access via __contains__ e.g.
>>> from lasio import SectionItems, HeaderItem >>> section = SectionItems( ... [HeaderItem(mnemonic="VERS", value=1.2)] ... ) >>> section['VERS'] HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=) >>> 'VERS' in section True >>> section.VERS HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=)
-
__setattr__(key, value)[source]¶ Allow access to
lasio.las_items.SectionItems.__setitem__()via attribute access.
-
set_item(key, newitem)[source]¶ Replace an item by comparison of session mnemonics.
Parameters: - key (str) – the item mnemonic (or HeaderItem with mnemonic) you want to replace.
- newitem (HeaderItem) – the new item
If key is not present, it appends newitem.
-
__weakref__¶ list of weak references to the object (if defined)
-
lasio.reader module¶
-
lasio.reader.check_for_path_obj(file_ref)[source]¶ Check if file_ref is a pathlib.Path object.
If file_ref is a pathlib.Path object, then return its absolute file path as a string so it will get processed as other string filenames.
If pathlib is not available, do nothing and return file_ref.
-
lasio.reader.open_file(file_ref, **encoding_kwargs)[source]¶ Open a file if necessary.
If
autodetect_encoding=Truethen eithercchardetorchardetneeds to be installed, or else anImportErrorwill be raised.Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. See
lasio.reader.open_with_codecs()for keyword arguments that can be used here.Returns: tuple of an open file-like object, and the encoding that was used to decode it (if it were read from disk).
-
lasio.reader.open_with_codecs(filename, encoding=None, encoding_errors='replace', autodetect_encoding=True, autodetect_encoding_chars=4000)[source]¶ Read Unicode data from file.
Parameters: filename (str) – path to file
Keyword Arguments: - encoding (str) – character encoding to open file_ref with, using
codecs.open(). - encoding_errors (str) – ‘strict’, ‘replace’ (default), ‘ignore’ - how to
handle errors with encodings (see
this section
of the standard library’s
codecsmodule for more information) - autodetect_encoding (str or bool) – default True to use chardet/cchardet to detect encoding. Note if set to False several common encodings will be tried but chardet won’t be used.
- autodetect_encoding_chars (int/None) – number of chars to read from LAS file for auto-detection of encoding.
Returns: a unicode or string object
This function is called by
lasio.reader.open_file().- encoding (str) – character encoding to open file_ref with, using
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lasio.reader.get_encoding(auto, raw)[source]¶ Automatically detect character encoding.
Parameters: Returns: A string specifying the character encoding.
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lasio.reader.read_file_contents(file_obj, regexp_subs, value_null_subs, ignore_data=False)[source]¶ Read file contents into memory.
Parameters: file_obj (open file-like object) –
Keyword Arguments: Returns: OrderedDict
I think of the returned dictionary as a “raw section”. The keys are the first line of the LAS section, including the tilde. Each value is a dict with either:
{"section_type": "header", "title": str, # title of section (including the ~) "lines": [str, ], # a list of the lines from the lAS file "line_nos": [int, ] # line nos from the original file }
or:
{"section_type": "data", "title": str, # title of section (including the ~) "start_line": int, # location of data section (the title line) "ncols": int, # no. of columns on first line of data, "array": ndarray # 1-D numpy.ndarray, }
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lasio.reader.read_data_section_iterative(file_obj, regexp_subs, value_null_subs)[source]¶ Read data section into memory.
Parameters: - file_obj (open file-like object) – should be positioned in line-by-line reading mode, with the last line read being the title of the ~ASCII data section.
- regexp_subs (list) – each item should be a tuple of the pattern and substitution string for a call to re.sub() on each line of the data section. See defaults.py READ_SUBS and NULL_SUBS for examples.
- value_null_subs (list) – list of numerical values to be replaced by numpy.nan values.
Returns: A 1-D numpy ndarray.
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lasio.reader.get_substitutions(read_policy, null_policy)[source]¶ Parse read and null policy definitions into a list of regexp and value substitutions.
Parameters: - read_policy (str, list, or substitution) – either (1) a string defined in defaults.READ_POLICIES; (2) a list of substitutions as defined by the keys of defaults.READ_SUBS; or (3) a list of actual substitutions similar to the values of defaults.READ_SUBS. You can mix (2) and (3) together if you want.
- null_policy (str, list, or sub) – as for read_policy but for defaults.NULL_POLICIES and defaults.NULL_SUBS
Returns: regexp_subs, value_null_subs, version_NULL - two lists and a bool. The first list is pairs of regexp patterns and substrs, and the second list is just a list of floats or integers. The bool is whether or not ‘NULL’ was located as a substitution.
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lasio.reader.parse_header_section(sectdict, version, ignore_header_errors=False, mnemonic_case='preserve')[source]¶ Parse a header section dict into a SectionItems containing HeaderItems.
Parameters: - sectdict (dict) – object returned from
lasio.reader.read_file_contents() - version (float) – either 1.2 or 2.0
Keyword Arguments: - ignore_header_errors (bool) – if True, issue HeaderItem parse errors
as
logging.warning()calls instead of alasio.exceptions.LASHeaderErrorexception. - mnemonic_case (str) – ‘preserve’: keep the case of HeaderItem mnemonics ‘upper’: convert all HeaderItem mnemonics to uppercase ‘lower’: convert all HeaderItem mnemonics to lowercase
Returns: - sectdict (dict) – object returned from
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class
lasio.reader.SectionParser(title, version=1.2)[source]¶ Bases:
objectParse lines from header sections.
Parameters: title (str) – title line of section. Used to understand different order formatting across the special sections ~C, ~P, ~W, and ~V, depending on version 1.2 or 2.0. Keyword Arguments: version (float) – version to parse according to. Default is 1.2. -
num(x, default=None)[source]¶ Attempt to parse a number.
Parameters: Returns: int, float, or default - from most to least preferred types.
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metadata(**keys)[source]¶ Return HeaderItem correctly formatted according to the order prescribed for LAS v 1.2 or 2.0 for the ~W section.
Keyword arguments should be the key:value pairs returned by
lasio.reader.read_header_line().
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curves(**keys)[source]¶ Return CurveItem.
Keyword arguments should be the key:value pairs returned by
lasio.reader.read_header_line().
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params(**keys)[source]¶ Return HeaderItem for ~P section (the same between 1.2 and 2.0 specs)
Keyword arguments should be the key:value pairs returned by
lasio.reader.read_header_line().
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lasio.reader.read_header_line(line, pattern=None, section_name=None)[source]¶ Read a line from a LAS header section.
The line is parsed with a regular expression – see LAS file specs for more details, but it should basically be in the format:
name.unit value : descr
Parameters: line (str) – line from a LAS header section Returns: A dictionary with keys ‘name’, ‘unit’, ‘value’, and ‘descr’, each containing a string as value.
lasio.writer module¶
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lasio.writer.write(las, file_object, version=None, wrap=None, STRT=None, STOP=None, STEP=None, fmt='%.5f', column_fmt=None, len_numeric_field=None, data_width=79, header_width=60)[source]¶ Write a LAS files.
Parameters: - las (
lasio.las.LASFile) – - file_object (file-like object open for writing) – output
- version (float or None) – version of written file, either 1.2 or 2.
If this is None,
las.version.VERS.valuewill be used. - wrap (bool or None) – whether to wrap the output data section.
If this is None,
las.version.WRAP.valuewill be used. - STRT (float or None) – value to use as STRT (note the data will not be clipped). If this is None, the data value in the first column, first row will be used.
- STOP (float or None) – value to use as STOP (note the data will not be clipped). If this is None, the data value in the first column, last row will be used.
- STEP (float or None) – value to use as STEP (note the data will not be resampled and/or interpolated). If this is None, the STEP will be estimated from the first two rows of the first column.
- fmt (str) – Python string formatting operator for numeric data to be used.
- column_fmt (dict or None) – use this to set a different format string
for specific columns from the data ndarray. E.g. to use
'%.3f'for the depth column and'%.2f'for all the other columns, you would usefmt='%.2f', column_fmt={0: '%.3f'}. - len_numeric_field (int) – width of each numeric field column (must be greater than than all the formatted numeric values in the file).
- data_width (79) – width of data field in characters
Creating an output file is not the only side-effect of this function. It will also modify the STRT, STOP and STEP HeaderItems so that they correctly reflect the ~Data section’s units and the actual first, last, and interval values.
You should avoid calling this function directly - instead use the
lasio.las.LASFile.write()method.- las (
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lasio.writer.get_formatter_function(order, left_width=None, middle_width=None)[source]¶ Create function to format a LAS header item for output.
Parameters: order – format of item, either ‘descr:value’ or ‘value:descr’
Keyword Arguments: Returns: A function which takes a header item (e.g.
lasio.las_items.HeaderItem) as its single argument and which in turn returns a string which is the correctly formatted LAS header line.
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lasio.writer.get_section_order_function(section, version, order_definitions={1.2: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['descr:value', ('value:descr', ['STRT', 'STOP', 'STEP', 'NULL'])]}, 2.0: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['value:descr']}})[source]¶ Get a function that returns the order per the mnemonic and section.
Parameters: Keyword Arguments: order_definitions (dict) – see source of defaults.py for more information
Returns: A function which takes a mnemonic (str) as its only argument, and in turn returns the order ‘value:descr’ or ‘descr:value’.
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lasio.writer.get_section_widths(section_name, items, version, order_func)[source]¶ Find minimum section widths fitting the content in items.
Parameters: - section_name (str) – either ‘version’, ‘well’, ‘curves’, or ‘params’
- items (SectionItems) – section items
- version (float) – either 1.2 or 2.0
- order_func (func) – see
lasio.writer.get_section_order_function()