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.
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 bemasterordevelop, depending how newer or bleeding edge you like it.
- 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
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.
get_dimensis_dualget_actnumget_seqnum_datesread_prop_arrayread_prop_timeread_vectors
get_vectors_shape: get the shape or dimensions of the vectors dataframeget_vector_names: get the names of all the vectorsget_vector_column: get the values for a vector-column
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: /home/superuser/anaconda3/envs/pyres/bin/python
#> libpython: /home/superuser/anaconda3/envs/pyres/lib/libpython3.6m.so
#> pythonhome: /home/superuser/anaconda3/envs/pyres:/home/superuser/anaconda3/envs/pyres
#> version: 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0]
#> numpy: /home/superuser/anaconda3/envs/pyres/lib/python3.6/site-packages/numpy
#> numpy_version: 1.16.2
#>
#> NOTE: Python version was forced by RETICULATE_PYTHONlibrary(rEcl)
ecl_folder <- system.file("rawdata", package = "rEcl")
ecl_folder
#> [1] "/home/superuser/R/x86_64-pc-linux-gnu-library/3.5/rEcl/rawdata"
unsmry_file <- file.path(ecl_folder, "spe6", "SPE6_FRAC.UNSMRY")
file.exists(unsmry_file)
#> [1] TRUEWe 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 32We 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 0.0000000Finally, 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 0.0000000PUNQ-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: /home/superuser/anaconda3/envs/pyres/bin/python
#> libpython: /home/superuser/anaconda3/envs/pyres/lib/libpython3.6m.so
#> pythonhome: /home/superuser/anaconda3/envs/pyres:/home/superuser/anaconda3/envs/pyres
#> version: 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0]
#> numpy: /home/superuser/anaconda3/envs/pyres/lib/python3.6/site-packages/numpy
#> numpy_version: 1.16.2
#>
#> NOTE: Python version was forced by RETICULATE_PYTHONlibrary(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-01Eclipse 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