summary
This module extracts summary information from UNSMRY-files into Pandas Dataframes.
from res2df import summary, ResdataFiles
resdatafiles = ResdataFiles("MYDATADECK.DATA")
dframe = summary.df(resdatafiles, column_keys="F*PT", time_index="yearly")
If you don’t specify column_keys, all included summary vectors will be
retrieved. Default for time_index is the report datetimes written by
Eclipse equivalent to time_index="raw", other options are daily, weekly,
monthly or yearly. See below for how to interpred “interpolated” summary
data.
Additional arguments are available, see the API documentation for an extensive overview.
DATE |
FGPT |
FOPT |
FVPT |
FWPT |
|---|---|---|---|---|
2000-01-01 |
0.0 |
0.0 |
0.0 |
0.0 |
2001-01-01 |
475826688.0 |
2591849.0 |
4628205.0 |
439485.375 |
2002-01-01 |
919193216.0 |
5062933.5 |
9755398.0 |
1565453.125 |
2003-01-01 |
1206145664.0 |
6665779.0 |
14876264.0 |
4038432.5 |
2004-01-01 |
1206714880.0 |
6668939.5 |
14889953.0 |
4046778.5 |
Rate handling in Eclipse summary vectors
Eclipse summary vectors with of rate type (oil rate, water rate etc.) are to be interpreted carefully. A value of e.g. FOPR at a specific date means that the value is valid backwards in time, until the prior point in time where data is available. For correct rates, you must use the raw time index for get_smry(), anything else will only give you an approximation. Also, you can not assume that summing the rates at every point in time corresponds to the associated cumulative summary vectors, e.g. FOPT, as there are multiple features into play here with efficienty factors etc.