Giter VIP home page Giter VIP logo

recl's Introduction

rEcl

The goal of rEcl is to serve as an R wrapper of the class EclBinaryParser, written in Python by Konstantin Sermyagin, a reservoir engineer. The class converts the reservoir simulation output files generated by Eclipse from binary to dataframes.

Installation

For the moment, rEcl is only available through Github.

Once is completed it will be submitted to CRAN. You can install it in the meantime by two methods:

  • cloning or downloading the package from Github
  • install it using devtools::install_github("f0nzie/rEcl", ref = "the_branch"), where the branch could be master or develop, depending how newer or bleeding edge you like it.

Requirements

  • R 3.5.3
  • Rtools 3.5
  • RStudio 1.2+. I used RStudio preview 1.2.1327 for the development of the package.
  • Python Anaconda3-2018.12-Windows-x86_64
  • Conda environment 3.6 with pandas and numpy installed. I called this environment pyres

Files used for testing

For testing rEcl and EclBinaryParser I used the output binary files from the reservoir simulation of the Volve field.

VOLVE_2016.INIT
VOLVE_2016.RSSPEC
VOLVE_2016.SMSPEC
VOLVE_2016.UNSMRY

You can find a copy of these files in this repository under rEcl/inst/python/volve, but the rEcl package will not install them. You will have to copy these files manually. In the future, I plan to download the files directly from Zenodo or Google drive; mainly, because these files are too big for an R package.

Functions for the class EclBinaryParser

  • get_dimens
  • is_dual
  • get_actnum
  • get_seqnum_dates
  • read_prop_array
  • read_prop_time
  • read_vectors

New functions

  • get_vectors_shape: get the shape or dimensions of the vectors dataframe
  • get_vector_names: get the names of all the vectors
  • get_vector_column: get the values for a vector-column

Examples

Example reservoir model SPE6

We start by reading the file SPE6_FRAC.UNSMRY. This file , because is relatively small, we can include it with the package. We willread it from the package installation folder.

library(reticulate)

reticulate::use_condaenv("pyres", required = TRUE)
reticulate::py_config()
#> python:         C:\Users\msfz751\Anaconda3\envs\pyres\python.exe
#> libpython:      C:/Users/msfz751/Anaconda3/envs/pyres/python36.dll
#> pythonhome:     C:\Users\msfz751\ANACON~1\envs\pyres
#> version:        3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]
#> Architecture:   64bit
#> numpy:          C:\Users\msfz751\ANACON~1\envs\pyres\lib\site-packages\numpy
#> numpy_version:  1.16.2
#> 
#> NOTE: Python version was forced by use_python function
library(rEcl)

ecl_folder <- system.file("rawdata", package = "rEcl")
ecl_folder
#> [1] "C:/Users/msfz751/Documents/R/win-library/3.5/rEcl/rawdata"
unsmry_file <- file.path(ecl_folder, "spe6", "SPE6_FRAC.UNSMRY")
file.exists(unsmry_file)
#> [1] TRUE

We connect to Python and load the class EclBinaryParser which resides in the Python package called restools. You can take a look at restools under the R installation folder in your lcoal disk.

Once we connect and load the Python package, we create an instance of the class EclBinaryParser providing the parse object py and the full name of the Eclipse binary file.

py <- restools_connect()
parser <- EclBinaryParser(py, unsmry_file)

First basic task is finding the dimensions of the reservoir model. We do that with get_dimensions.

get_dimensions(parser)
#> DIMENS(ni=10, nj=1, nk=10)

This is a heavier operation; reading the vectors.

vectors <- read_vectors(parser)

Get the shape or dimensions of the vector dataframe.

get_vectors_shape(parser)
#> [1] 69 32

We get the names of the vectors we specified in our input file.

get_vector_names(parser)
#>  [1] "BGSAT" "BOSAT" "BPR"   "BRS"   "BWSAT" "FGOR"  "FGPR"  "FOPR" 
#>  [9] "FPR"   "TIME"  "WBHP"  "YEARS"

We now want a dataframe corresponding to a specific vector-column with get_vector_column:

get_vector_column(parser, "FOPR")
#>           FOPR
#> 1    0.0000000
#> 2  495.8547363
#> 3  496.4673462
#> 4  497.7044983
#> 5  500.0000000
#> 6  500.0000000
#> 7  500.0000000
#> 8  500.0000000
#> 9  500.0000000
#> 10 500.0000000
#> 11 499.0129700
#> 12 495.0864258
#> 13 491.3752441
#> 14 487.6884460
#> 15 483.7983093
#> 16 479.4172058
#> 17 474.9851074
#> 18 470.4046021
#> 19 465.6196289
#> 20 460.7243347
#> 21 455.7453308
#> 22 450.6983032
#> 23 445.5942688
#> 24 440.4420471
#> 25 435.2494202
#> 26 430.0222778
#> 27 424.0520325
#> 28 416.5676270
#> 29 408.7698059
#> 30 400.7507019
#> 31 392.5427246
#> 32 384.1553650
#> 33 375.7556763
#> 34 367.2411499
#> 35 358.5085144
#> 36 349.5070496
#> 37 339.1749573
#> 38 327.4499817
#> 39 315.0457153
#> 40 301.9628906
#> 41 288.1910400
#> 42 273.9568787
#> 43 259.3403320
#> 44 243.6156769
#> 45 223.3641968
#> 46 201.8397217
#> 47 179.7216034
#> 48 157.3689117
#> 49 135.5422974
#> 50 114.8100815
#> 51  91.3848877
#> 52  66.5469284
#> 53  44.1556320
#> 54  26.0932941
#> 55  13.4400187
#> 56   5.8621721
#> 57   5.2417884
#> 58   4.5437417
#> 59   3.6096404
#> 60   2.4702997
#> 61   1.3012712
#> 62   0.4364305
#> 63   0.0000000
#> 64   0.0000000
#> 65   0.0000000
#> 66   0.0000000
#> 67   0.0000000
#> 68   0.0000000
#> 69   0.0000000

Finally, because the function get_vector_column is vectorized, we can get a dataframe of multiple columns.

# get several vectors at once
df_vars <- get_vector_column(parser, c("FPR", "FGOR", "FOPR"))
df_vars
#>         FPR         FGOR        FOPR
#> 1  6025.137     0.000000   0.0000000
#> 2  6021.500     1.530000 495.8547363
#> 3  6010.570     1.530000 496.4673462
#> 4  5977.642     1.530000 497.7044983
#> 5  5878.311     1.530000 500.0000000
#> 6  5743.715     1.530000 500.0000000
#> 7  5609.000     1.530000 500.0000000
#> 8  5528.037     1.538324 500.0000000
#> 9  5492.169     1.562090 500.0000000
#> 10 5458.588     1.583304 500.0000000
#> 11 5426.020     1.608501 499.0129700
#> 12 5394.494     1.637577 495.0864258
#> 13 5365.298     1.668722 491.3752441
#> 14 5336.807     1.703061 487.6884460
#> 15 5307.525     1.744257 483.7983093
#> 16 5278.622     1.811011 479.4172058
#> 17 5248.898     1.894031 474.9851074
#> 18 5217.372     2.003969 470.4046021
#> 19 5185.498     2.126142 465.6196289
#> 20 5153.688     2.257681 460.7243347
#> 21 5121.876     2.397633 455.7453308
#> 22 5090.011     2.545463 450.6983032
#> 23 5058.050     2.700851 445.5942688
#> 24 5025.961     2.863592 440.4420471
#> 25 4993.717     3.033530 435.2494202
#> 26 4961.294     3.210622 430.0222778
#> 27 4927.479     3.415624 424.0520325
#> 28 4890.290     3.686419 416.5676270
#> 29 4852.142     3.985715 408.7698059
#> 30 4813.382     4.311690 400.7507019
#> 31 4773.960     4.664652 392.5427246
#> 32 4733.835     5.046370 384.1553650
#> 33 4693.025     5.447596 375.7556763
#> 34 4651.508     5.877616 367.2411499
#> 35 4609.229     6.347281 358.5085144
#> 36 4566.122     6.864860 349.5070496
#> 37 4518.661     7.555875 339.1749573
#> 38 4465.955     8.423036 327.4499817
#> 39 4411.505     9.426800 315.0457153
#> 40 4355.158    10.592511 301.9628906
#> 41 4296.767    11.953240 288.1910400
#> 42 4236.273    13.519985 273.9568787
#> 43 4173.643    15.325132 259.3403320
#> 44 4108.362    17.547583 243.6156769
#> 45 4034.673    20.967762 223.3641968
#> 46 3956.873    25.385218 201.8397217
#> 47 3875.409    31.051266 179.7216034
#> 48 3790.427    38.419727 157.3689117
#> 49 3702.283    47.972599 135.5422974
#> 50 3611.458    60.411133 114.8100815
#> 51 3515.281    81.203598  91.3848877
#> 52 3412.050   119.070526  66.5469284
#> 53 3306.926   189.455231  44.1556320
#> 54 3201.213   333.402679  26.0932941
#> 55 3096.375   662.164490  13.4400187
#> 56 2993.682  1529.078735   5.8621721
#> 57 2983.476  1709.756104   5.2417884
#> 58 2970.660  1971.567749   4.5437417
#> 59 2950.695  2479.002441   3.6096404
#> 60 2919.802  3612.904297   2.4702997
#> 61 2872.318  6819.601074   1.3012712
#> 62 2800.109 20102.908203   0.4364305
#> 63 2800.150     0.000000   0.0000000
#> 64 2800.146     0.000000   0.0000000
#> 65 2800.146     0.000000   0.0000000
#> 66 2800.146     0.000000   0.0000000
#> 67 2800.146     0.000000   0.0000000
#> 68 2800.146     0.000000   0.0000000
#> 69 2800.146     0.000000   0.0000000

Example reservoir model PUNQ-S3

PUNQ-S3 is a synthetic reservoir model that is used for testing and calibrating reservoir simulators. These are the files available with the package:

PUNQS3.INIT
PUNQS3.INSPEC
PUNQS3.RSSPEC
PUNQS3.SMSPEC
PUNQS3.UNRST
PUNQS3.UNSMR

You may list the files with:

list.files(system.file("python", "volve", package = "rEcl"))

library(reticulate)

reticulate::use_condaenv("pyres", required = TRUE)
reticulate::py_config()
#> python:         C:\Users\msfz751\Anaconda3\envs\pyres\python.exe
#> libpython:      C:/Users/msfz751/Anaconda3/envs/pyres/python36.dll
#> pythonhome:     C:\Users\msfz751\ANACON~1\envs\pyres
#> version:        3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]
#> Architecture:   64bit
#> numpy:          C:\Users\msfz751\ANACON~1\envs\pyres\lib\site-packages\numpy
#> numpy_version:  1.16.2
#> 
#> NOTE: Python version was forced by use_python function
library(rEcl)

ecl_folder <- system.file("rawdata", package = "rEcl")
unsmry_file <- file.path(ecl_folder, "PUNQS3", "PUNQS3.UNSMRY")
file.exists(unsmry_file)
#> [1] TRUE
# connect to Python and start a class instance
py <- restools_connect()
parser <- EclBinaryParser(py, unsmry_file)

# dimensions of the reservoir model
get_dimensions(parser)
#> DIMENS(ni=19, nj=28, nk=5)
# reading the Eclipse vectors. this may take few seconds id the reservoir is too complex
vectors <- read_vectors(parser)

# Get the shape or dimensions of the vector dataframe.
get_vectors_shape(parser)
#> [1] 179  15
# We get the names of the vectors we specified in our input file.
get_vector_names(parser)
#>  [1] "FGOR"     "FGPT"     "FOPR"     "FOPT"     "FWCT"     "FWPT"    
#>  [7] "TIME"     "TIMESTEP" "WBHP"     "YEARS"
# dataframe corresponding to a specific vector-column
get_vector_column(parser, "FOPR")
#>          FOPR
#> 1      0.0000
#> 2      0.0000
#> 3    600.0000
#> 4    600.0000
#> 5    600.0000
#> 6    600.0000
#> 7    600.0000
#> 8    600.0000
#> 9    600.0000
#> 10   600.0000
#> 11   600.0000
#> 12  1200.0000
#> 13  1200.0000
#> 14  1200.0000
#> 15  1200.0000
#> 16  1200.0000
#> 17  1200.0000
#> 18   600.0000
#> 19   600.0000
#> 20   600.0000
#> 21   600.0000
#> 22   600.0000
#> 23   600.0000
#> 24   300.0000
#> 25   300.0000
#> 26   300.0000
#> 27   300.0000
#> 28   300.0000
#> 29   300.0000
#> 30     0.0000
#> 31     0.0000
#> 32     0.0000
#> 33     0.0000
#> 34     0.0000
#> 35     0.0000
#> 36     0.0000
#> 37     0.0000
#> 38     0.0000
#> 39   900.0000
#> 40   900.0000
#> 41   900.0000
#> 42   900.0000
#> 43   900.0000
#> 44   900.0000
#> 45   900.0000
#> 46   900.0000
#> 47     0.0000
#> 48     0.0000
#> 49     0.0000
#> 50     0.0000
#> 51   900.0000
#> 52   900.0000
#> 53   900.0000
#> 54   900.0000
#> 55   900.0000
#> 56   900.0000
#> 57   900.0000
#> 58   900.0000
#> 59     0.0000
#> 60     0.0000
#> 61     0.0000
#> 62     0.0000
#> 63   900.0000
#> 64   900.0000
#> 65   900.0000
#> 66   900.0000
#> 67   900.0000
#> 68   862.5000
#> 69   834.3750
#> 70   834.3750
#> 71     0.0000
#> 72     0.0000
#> 73     0.0000
#> 74     0.0000
#> 75   900.0000
#> 76   900.0000
#> 77   900.0000
#> 78   900.0000
#> 79   900.0000
#> 80   900.0000
#> 81   892.9762
#> 82     0.0000
#> 83     0.0000
#> 84     0.0000
#> 85     0.0000
#> 86   900.0000
#> 87   900.0000
#> 88   898.3598
#> 89   894.4315
#> 90   890.6831
#> 91   888.4182
#> 92   880.6496
#> 93     0.0000
#> 94     0.0000
#> 95     0.0000
#> 96     0.0000
#> 97   897.3006
#> 98   893.7467
#> 99   889.1471
#> 100  884.1654
#> 101  877.8188
#> 102  874.2231
#> 103  860.3490
#> 104    0.0000
#> 105    0.0000
#> 106    0.0000
#> 107    0.0000
#> 108  889.7090
#> 109  884.1975
#> 110  873.6402
#> 111  861.9726
#> 112  851.0178
#> 113  842.5769
#> 114  820.3649
#> 115    0.0000
#> 116    0.0000
#> 117    0.0000
#> 118    0.0000
#> 119  851.9354
#> 120  843.3365
#> 121  832.3793
#> 122  821.0728
#> 123  809.2431
#> 124  796.5966
#> 125  765.6595
#> 126    0.0000
#> 127    0.0000
#> 128    0.0000
#> 129    0.0000
#> 130  808.3836
#> 131  798.3890
#> 132  783.0869
#> 133  767.2960
#> 134  751.8171
#> 135  740.5112
#> 136  710.6010
#> 137    0.0000
#> 138    0.0000
#> 139    0.0000
#> 140    0.0000
#> 141  747.0986
#> 142  737.8694
#> 143  724.7645
#> 144  710.1848
#> 145  694.0875
#> 146  681.2035
#> 147  651.1420
#> 148    0.0000
#> 149    0.0000
#> 150    0.0000
#> 151    0.0000
#> 152  681.1065
#> 153  673.4787
#> 154  662.4407
#> 155  650.0897
#> 156  637.5364
#> 157  628.9332
#> 158  611.1095
#> 159    0.0000
#> 160    0.0000
#> 161    0.0000
#> 162    0.0000
#> 163  637.6225
#> 164  630.9486
#> 165  621.4207
#> 166  611.5229
#> 167  603.4231
#> 168  598.5114
#> 169  587.7632
#> 170    0.0000
#> 171    0.0000
#> 172    0.0000
#> 173    0.0000
#> 174  612.2325
#> 175  606.1571
#> 176  597.6083
#> 177  589.1541
#> 178  583.0701
#> 179  579.7202
# get a dataframe of multiple columns
# vectorized function to get several vectors at once
df_vars <- get_vector_column(parser, c("YEARS", "FGOR", "FOPR", "FWCT"))
df_vars
#>            YEARS     FGOR      FOPR         FWCT
#> 1   0.000000e+00  0.00000    0.0000 0.000000e+00
#> 2   2.737851e-05  0.00000    0.0000 0.000000e+00
#> 3   3.011636e-04 74.00000  600.0000 1.991938e-07
#> 4   1.122519e-03 74.00000  600.0000 6.092408e-07
#> 5   2.765229e-03 73.98709  600.0000 1.132328e-06
#> 6   7.693361e-03 73.86024  600.0000 1.893949e-06
#> 7   2.247776e-02 73.55646  600.0000 2.900575e-06
#> 8   5.367557e-02 73.21950  600.0000 3.938527e-06
#> 9   8.487337e-02 73.00764  600.0000 4.601429e-06
#> 10  1.615332e-01 72.68385  600.0000 5.630160e-06
#> 11  2.464066e-01 72.41563  600.0000 6.494209e-06
#> 12  2.491444e-01 72.15381 1200.0000 7.345203e-06
#> 13  2.573580e-01 71.76186 1200.0000 8.643978e-06
#> 14  2.819986e-01 71.25349 1200.0000 1.037846e-05
#> 15  3.285421e-01 70.75266 1200.0000 1.214476e-05
#> 16  4.134155e-01 70.17004 1200.0000 1.427279e-05
#> 17  4.955510e-01 69.71758 1200.0000 1.598290e-05
#> 18  4.982888e-01 69.71760  600.0000 1.494059e-05
#> 19  5.065024e-01 69.71758  600.0000 1.371125e-05
#> 20  5.311431e-01 69.71758  600.0000 1.269446e-05
#> 21  5.804244e-01 69.71757  600.0000 1.229585e-05
#> 22  6.652977e-01 69.71755  600.0000 1.241445e-05
#> 23  7.474332e-01 69.71754  600.0000 1.270893e-05
#> 24  7.501711e-01 69.71757  300.0000 1.215319e-05
#> 25  7.583846e-01 69.71756  300.0000 1.146253e-05
#> 26  7.830253e-01 69.71754  300.0000 1.078179e-05
#> 27  8.323066e-01 69.71754  300.0000 1.035980e-05
#> 28  9.144422e-01 69.71754  300.0000 1.017579e-05
#> 29  9.993156e-01 69.71753  300.0000 1.015527e-05
#> 30  1.002053e+00  0.00000    0.0000 0.000000e+00
#> 31  1.010267e+00  0.00000    0.0000 0.000000e+00
#> 32  1.034908e+00  0.00000    0.0000 0.000000e+00
#> 33  1.108830e+00  0.00000    0.0000 0.000000e+00
#> 34  1.330595e+00  0.00000    0.0000 0.000000e+00
#> 35  1.665982e+00  0.00000    0.0000 0.000000e+00
#> 36  2.001369e+00  0.00000    0.0000 0.000000e+00
#> 37  3.000684e+00  0.00000    0.0000 0.000000e+00
#> 38  4.000000e+00  0.00000    0.0000 0.000000e+00
#> 39  4.002738e+00 69.71751  900.0000 4.737859e-06
#> 40  4.010952e+00 69.71751  900.0000 7.063799e-06
#> 41  4.035592e+00 69.71752  900.0000 9.577207e-06
#> 42  4.109514e+00 69.71754  900.0000 1.231731e-05
#> 43  4.278152e+00 69.71426  900.0000 1.543161e-05
#> 44  4.495551e+00 69.37884  900.0000 1.844080e-05
#> 45  4.747433e+00 68.98219  900.0000 2.287366e-05
#> 46  4.999316e+00 70.45412  900.0000 2.878149e-05
#> 47  5.002053e+00  0.00000    0.0000 0.000000e+00
#> 48  5.010267e+00  0.00000    0.0000 0.000000e+00
#> 49  5.023956e+00  0.00000    0.0000 0.000000e+00
#> 50  5.037645e+00  0.00000    0.0000 0.000000e+00
#> 51  5.040383e+00 69.88659  900.0000 2.333518e-05
#> 52  5.048597e+00 70.27151  900.0000 2.540866e-05
#> 53  5.073237e+00 71.19053  900.0000 2.780255e-05
#> 54  5.147160e+00 73.33044  900.0000 3.096905e-05
#> 55  5.322382e+00 80.44121  900.0000 3.758295e-05
#> 56  5.497604e+00 87.36105  900.0000 4.494251e-05
#> 57  5.749486e+00 92.70155  900.0000 5.707620e-05
#> 58  6.001369e+00 96.02811  900.0000 7.095271e-05
#> 59  6.004107e+00  0.00000    0.0000 0.000000e+00
#> 60  6.012321e+00  0.00000    0.0000 0.000000e+00
#> 61  6.026010e+00  0.00000    0.0000 0.000000e+00
#> 62  6.039699e+00  0.00000    0.0000 0.000000e+00
#> 63  6.042437e+00 91.45794  900.0000 6.279253e-05
#> 64  6.050650e+00 92.18629  900.0000 6.570385e-05
#> 65  6.075291e+00 93.51507  900.0000 6.967890e-05
#> 66  6.149213e+00 95.59954  900.0000 7.567459e-05
#> 67  6.323066e+00 97.99612  900.0000 8.774507e-05
#> 68  6.496920e+00 92.81242  862.5000 9.963721e-05
#> 69  6.748802e+00 87.26064  834.3750 2.618798e-04
#> 70  7.000684e+00 85.83868  834.3750 4.863491e-04
#> 71  7.003422e+00  0.00000    0.0000 0.000000e+00
#> 72  7.011636e+00  0.00000    0.0000 0.000000e+00
#> 73  7.025325e+00  0.00000    0.0000 0.000000e+00
#> 74  7.039014e+00  0.00000    0.0000 0.000000e+00
#> 75  7.041752e+00 86.70294  900.0000 4.489492e-04
#> 76  7.049966e+00 87.83990  900.0000 4.583297e-04
#> 77  7.074606e+00 89.97019  900.0000 4.820266e-04
#> 78  7.148529e+00 92.37147  900.0000 5.483835e-04
#> 79  7.322382e+00 93.35823  900.0000 7.107569e-04
#> 80  7.496235e+00 93.45702  900.0000 8.821991e-04
#> 81  8.000000e+00 91.03961  892.9762 2.582569e-03
#> 82  8.002738e+00  0.00000    0.0000 0.000000e+00
#> 83  8.010951e+00  0.00000    0.0000 0.000000e+00
#> 84  8.024641e+00  0.00000    0.0000 0.000000e+00
#> 85  8.038330e+00  0.00000    0.0000 0.000000e+00
#> 86  8.041068e+00 85.23295  900.0000 2.548188e-03
#> 87  8.049281e+00 86.28516  900.0000 2.580381e-03
#> 88  8.073922e+00 87.84223  898.3598 2.679979e-03
#> 89  8.147844e+00 89.01424  894.4315 2.975781e-03
#> 90  8.321697e+00 88.99358  890.6831 3.652469e-03
#> 91  8.495551e+00 88.36535  888.4182 4.312418e-03
#> 92  8.999315e+00 85.94995  880.6496 6.275867e-03
#> 93  9.002053e+00  0.00000    0.0000 0.000000e+00
#> 94  9.010267e+00  0.00000    0.0000 0.000000e+00
#> 95  9.023956e+00  0.00000    0.0000 0.000000e+00
#> 96  9.037645e+00  0.00000    0.0000 0.000000e+00
#> 97  9.040383e+00 80.63602  897.3006 6.069751e-03
#> 98  9.048596e+00 81.49801  893.7467 6.148878e-03
#> 99  9.073237e+00 82.93967  889.1471 6.343846e-03
#> 100 9.147160e+00 84.22858  884.1654 6.848571e-03
#> 101 9.322382e+00 84.12869  877.8188 7.899145e-03
#> 102 9.497604e+00 83.41657  874.2231 8.837595e-03
#> 103 1.000137e+01 81.24713  860.3490 1.098902e-02
#> 104 1.000411e+01  0.00000    0.0000 0.000000e+00
#> 105 1.001232e+01  0.00000    0.0000 0.000000e+00
#> 106 1.002601e+01  0.00000    0.0000 0.000000e+00
#> 107 1.003970e+01  0.00000    0.0000 0.000000e+00
#> 108 1.004244e+01 76.28202  889.7090 1.051499e-02
#> 109 1.005065e+01 77.26228  884.1975 1.059827e-02
#> 110 1.007529e+01 78.67303  873.6402 1.080811e-02
#> 111 1.014921e+01 79.81296  861.9726 1.122721e-02
#> 112 1.032307e+01 79.90886  851.0178 1.202733e-02
#> 113 1.049692e+01 79.46638  842.5769 1.280397e-02
#> 114 1.100068e+01 77.76192  820.3649 1.501318e-02
#> 115 1.100342e+01  0.00000    0.0000 0.000000e+00
#> 116 1.101164e+01  0.00000    0.0000 0.000000e+00
#> 117 1.102532e+01  0.00000    0.0000 0.000000e+00
#> 118 1.103901e+01  0.00000    0.0000 0.000000e+00
#> 119 1.104175e+01 74.01922  851.9354 1.427874e-02
#> 120 1.104997e+01 74.25053  843.3365 1.446566e-02
#> 121 1.107461e+01 75.35580  832.3793 1.478798e-02
#> 122 1.114853e+01 76.53138  821.0728 1.540074e-02
#> 123 1.132238e+01 76.59998  809.2431 1.687727e-02
#> 124 1.149624e+01 76.16484  796.5966 1.851964e-02
#> 125 1.200000e+01 75.66885  765.6595 2.392858e-02
#> 126 1.200274e+01  0.00000    0.0000 0.000000e+00
#> 127 1.201095e+01  0.00000    0.0000 0.000000e+00
#> 128 1.202464e+01  0.00000    0.0000 0.000000e+00
#> 129 1.203833e+01  0.00000    0.0000 0.000000e+00
#> 130 1.204107e+01 73.34157  808.3836 2.283560e-02
#> 131 1.204928e+01 73.58937  798.3890 2.318827e-02
#> 132 1.207392e+01 74.12044  783.0869 2.391178e-02
#> 133 1.214784e+01 74.87678  767.2960 2.542312e-02
#> 134 1.232170e+01 75.25618  751.8171 2.851319e-02
#> 135 1.249555e+01 75.31657  740.5112 3.422970e-02
#> 136 1.299932e+01 75.54026  710.6010 5.062482e-02
#> 137 1.300205e+01  0.00000    0.0000 0.000000e+00
#> 138 1.301027e+01  0.00000    0.0000 0.000000e+00
#> 139 1.302396e+01  0.00000    0.0000 0.000000e+00
#> 140 1.303765e+01  0.00000    0.0000 0.000000e+00
#> 141 1.304038e+01 72.94955  747.0986 5.180321e-02
#> 142 1.304860e+01 73.27383  737.8694 5.236844e-02
#> 143 1.307324e+01 73.87908  724.7645 5.389619e-02
#> 144 1.314716e+01 74.68990  710.1848 5.790187e-02
#> 145 1.332238e+01 75.43992  694.0875 6.573480e-02
#> 146 1.349760e+01 75.81366  681.2035 7.270485e-02
#> 147 1.400137e+01 76.38380  651.1420 8.832157e-02
#> 148 1.400411e+01  0.00000    0.0000 0.000000e+00
#> 149 1.401232e+01  0.00000    0.0000 0.000000e+00
#> 150 1.402601e+01  0.00000    0.0000 0.000000e+00
#> 151 1.403970e+01  0.00000    0.0000 0.000000e+00
#> 152 1.404244e+01 73.59885  681.1065 9.002720e-02
#> 153 1.405065e+01 73.94077  673.4787 9.016947e-02
#> 154 1.407529e+01 74.58649  662.4407 9.088600e-02
#> 155 1.414921e+01 75.45151  650.0897 9.317731e-02
#> 156 1.432307e+01 76.21648  637.5364 9.829839e-02
#> 157 1.449692e+01 76.55766  628.9332 1.050718e-01
#> 158 1.500068e+01 76.94323  611.1095 1.222561e-01
#> 159 1.500342e+01  0.00000    0.0000 0.000000e+00
#> 160 1.501164e+01  0.00000    0.0000 0.000000e+00
#> 161 1.502532e+01  0.00000    0.0000 0.000000e+00
#> 162 1.503901e+01  0.00000    0.0000 0.000000e+00
#> 163 1.504175e+01 73.94164  637.6225 1.269501e-01
#> 164 1.504997e+01 74.31033  630.9486 1.262482e-01
#> 165 1.507461e+01 75.00164  621.4207 1.256994e-01
#> 166 1.514853e+01 75.90739  611.5229 1.268540e-01
#> 167 1.532238e+01 76.96578  603.4231 1.312406e-01
#> 168 1.549624e+01 77.58962  598.5114 1.356596e-01
#> 169 1.600000e+01 78.53827  587.7632 1.459805e-01
#> 170 1.600274e+01  0.00000    0.0000 0.000000e+00
#> 171 1.601095e+01  0.00000    0.0000 0.000000e+00
#> 172 1.602464e+01  0.00000    0.0000 0.000000e+00
#> 173 1.603833e+01  0.00000    0.0000 0.000000e+00
#> 174 1.604107e+01 75.18638  612.2325 1.512183e-01
#> 175 1.604928e+01 75.61112  606.1571 1.500371e-01
#> 176 1.607392e+01 76.41542  597.6083 1.486682e-01
#> 177 1.614784e+01 77.49184  589.1541 1.485046e-01
#> 178 1.632170e+01 78.48283  583.0701 1.517355e-01
#> 179 1.649555e+01 79.01240  579.7202 1.553172e-01

Eclipse vectors that are different in nature, such as well (start with W) variables, will get a different size than field variables (start with F):

# read a well variable
tibble::as_tibble(get_vector_column(parser, "WBHP"))
#> # A tibble: 1,074 x 1
#>     WBHP
#>    <dbl>
#>  1    0 
#>  2    0 
#>  3  221.
#>  4  221.
#>  5  220.
#>  6  219.
#>  7  218.
#>  8  217.
#>  9  216.
#> 10  215.
#> # ... with 1,064 more rows

recl's People

Contributors

f0nzie avatar nynaamme avatar

Stargazers

avatar avatar avatar avatar avatar avatar avatar avatar avatar

Watchers

avatar avatar avatar

Forkers

iskander-saitbatalov clementetienam

recl's Issues

binary_parser.py will not work with INTERSECT output

In the SMSPEC file, the NAMES keyword is used instead of WGNAMES between Petrel and INTERSECT to support names longer
than 8 characters and extended names (Like networks, branches, multisegmented wells and region properties tied to region families.

To support multiple networks, network node and branch names are prefixed with the network name separated with ":". For properties on named completions and segmented well devices the device or completion name is prefixed by the well name separated with ":". For region properties the region name is prefixed by the region family name separated by ":" — this is addition to the numerical region number.

The current code in binary_parser.py only identifies WGNAMES:

wgnames = byte2str(EclArray(smspec, keyword='WGNAMES', with_fakes=True).array)

Changing the line to 'NAMES' will not fix the issue as the data type for this keyword is 'C0##', where ## is the size of the char field. The current python script only allow for a CHAR type size.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.