Simple tokenizator of Eclipse data decks. Pure Python code with no dependencies. Pandas and Numpy are required for further data manipulations.
Eclipse data deck (Eclipse case) is a collection of ASCII files. Each file has a set of data records, specified by keywords. Eclipse has several types of keywords. We do not care about those types. We only split a case by keyword:value pairs. Empty lines and comments are ignored. Keyword owns the data that located between that keyword and the next keyword. If data without keyword is found, exception is raised.
This is not a strict parser, as we do not know all of possible keywords and their syntax, we reply on simple detection of possible keywords.
We assume that keyword is a command up to 8 chars long, starts with column 0 and could consist of A-Z, 0-9, + and - symbols. Keyword should be uppercase and start with a letter.
There is special keyword INCLUDE which includes other file into current file. When such
keyword is found, it is parsed right away, and parser continues reading current after that.
INCLUDE keywords could be nested.
We keep track of parsed files to prevent recursion. We also keep track of missing files that INCLUDE keyword refers to.
import ecl_tokenizer as et
case = et.EclCase(r"C:\GitHub\opm-tests\model5\0_BASE_MODEL5.DATA")
>>> case.describe()
+ C:\GitHub\opm-tests\model5\0_BASE_MODEL5.DATA
|-- C:\GitHub\opm-tests\model5\0_BASE_MODEL5.DATA
|-- C:\GitHub\opm-tests\model5\include\test1_20x30x10.grdecl
|-- C:\GitHub\opm-tests\model5\include\permx_model5.grdecl
|-- C:\GitHub\opm-tests\model5\include\pvt_live_oil_dgas.ecl
|-- C:\GitHub\opm-tests\model5\include\rock.inc
|-- C:\GitHub\opm-tests\model5\include\relperm.inc
|-- C:\GitHub\opm-tests\model5\include\summary.inc
|-- C:\GitHub\opm-tests\model5\include\well_vfp.ecl
|-- C:\GitHub\opm-tests\model5\include\flowl_b_vfp.ecl
|-- C:\GitHub\opm-tests\model5\include\flowl_c_vfp.ecl
# Query for keyword presence.
>>> case.has_kwd("DIMENS")
True
# Get list of all instances of given keyword.
>>> case.get_kwds("DIMENS")
[<EclKwd: DIMENS Section "RUNSPEC " Parent: "C:\GitHub\opm-tests\model5\0_BASE_MODEL5.DATA" Line_number: "17">
]
# Get raw data of first instance of given keyword.
>>> case.get_kwds("DIMENS")[0].value
[' 20 30 10 /']
# Get combined data from records of all instances of given keyword.
>>> case.get_kwds_data("DIMENS")
[['20', '30', '10']]
>>> import pandas as pd
>>> import numpy as np
# By default, N*M type of records are expanded.
>>> wsp = case.get_kwds_data("WELSPECS")
>>> df1 = pd.DataFrame(wsp)
>>> df1
0 1 2 3 4 5 6 7 8 9 10 11
0 'B-1H' 'B1' 11 3 OIL SHUT
1 'B-2H' 'B1' 4 7 OIL SHUT
2 'B-3H' 'B1' 11 12 OIL SHUT
3 'C-1H' 'C1' 13 20 OIL SHUT
4 'C-2H' 'C1' 12 27 OIL SHUT
5 'F-1H' 'F1' 19 4 WATER SHUT
6 'F-2H' 'F1' 19 12 WATER SHUT
7 'G-3H' 'G1' 19 21 WATER SHUT
8 'G-4H' 'G1' 19 25 WATER SHUT
# Disable expansion of N*M type of records.
>>> wsp2 = case.get_kwds_data("WELSPECS", expand=False)
>>> df2 = pd.DataFrame(wsp2)
>>> df2
0 1 2 3 4 5 6 7 8 9 10 11
0 'B-1H' 'B1' 11 3 1* OIL 1* 1* SHUT 1* 1* 1*
1 'B-2H' 'B1' 4 7 1* OIL 1* 1* SHUT 1* 1* 1*
2 'B-3H' 'B1' 11 12 1* OIL 1* 1* SHUT 1* 1* 1*
3 'C-1H' 'C1' 13 20 1* OIL 1* 1* SHUT 1* 1* 1*
4 'C-2H' 'C1' 12 27 1* OIL 1* 1* SHUT 1* 1* 1*
5 'F-1H' 'F1' 19 4 1* WATER 1* 1* SHUT 1* 1* 1*
6 'F-2H' 'F1' 19 12 1* WATER 1* 1* SHUT 1* 1* 1*
7 'G-3H' 'G1' 19 21 1* WATER 1* 1* SHUT 1* 1* 1*
8 'G-4H' 'G1' 19 25 1* WATER 1* 1* SHUT 1* 1* 1*
# By default, all of columns are of string type. See the next section for workaround.
>>> df2.dtypes
0 object
1 object
2 object
3 object
4 object
5 object
6 object
7 object
8 object
9 object
10 object
11 object
dtype: object
By default, data returned from case.get_kwds_data("kwd") is of string type.
To recognize them as numbers, supply the dtype=float32 argument to pd.DataFrame constructor.
(It seems like older versions of pandas do not support integer columns).
>>> df2 = pd.DataFrame(wsp2, dtype="float32")
>>> df2
0 1 2 3 4 5 6 7 8 9 10 11
0 'B-1H' 'B1' 11.0 3.0 1* OIL 1* 1* SHUT 1* 1* 1*
1 'B-2H' 'B1' 4.0 7.0 1* OIL 1* 1* SHUT 1* 1* 1*
2 'B-3H' 'B1' 11.0 12.0 1* OIL 1* 1* SHUT 1* 1* 1*
3 'C-1H' 'C1' 13.0 20.0 1* OIL 1* 1* SHUT 1* 1* 1*
4 'C-2H' 'C1' 12.0 27.0 1* OIL 1* 1* SHUT 1* 1* 1*
5 'F-1H' 'F1' 19.0 4.0 1* WATER 1* 1* SHUT 1* 1* 1*
6 'F-2H' 'F1' 19.0 12.0 1* WATER 1* 1* SHUT 1* 1* 1*
7 'G-3H' 'G1' 19.0 21.0 1* WATER 1* 1* SHUT 1* 1* 1*
8 'G-4H' 'G1' 19.0 25.0 1* WATER 1* 1* SHUT 1* 1* 1*
>>> df2.dtypes
0 object
1 object
2 float32
3 float32
4 object
5 object
6 object
7 object
8 object
9 object
10 object
11 object
dtype: object
# Get the model dimensions.
>>> case.get_kwds_data("DIMENS")
[['20', '30', '10']]
# How many PERMX values we should have?
>>> 20*30*10
6000
# Check for presense of PERMX and PORO keywords in this case.
>>> case.has_kwd("PERMX")
True
>>> case.has_kwd("PORO")
True
# Get PERMX data and convert to numpy array of proper type.
>>> permx = case.get_kwds_data("PERMX")
>>> permx_arr = np.array(permx, dtype="float32")
# Number of data records is correct.
>>> permx_arr.shape
(1, 6000)
You are welcome to use this code in accordance with its license. Copyrights should be preserved. The Author is not responsible for any outcome that you experience from this code. See the license for more details.
All code contains bugs. This code is reached the state where it deserved sharing. If you find any bug, please contribute via raising tickets or submitting pull requests, as long as you agree with the license terms.