metadata
license: mit
pretty_name: ResBench reference
tags:
- geology
- reservoir-modeling
- benchmark
- 3d
ResBench reference
The reference data that ResBench scores
generative reservoir models against. Every volume was produced by the ResMill
process simulator (commit in ENGINE.txt) under the same rules as the
SiliciclasticReservoirs
training dataset. You do not need to download it by hand:
pip install "resbench[download]"
resbench download # fetches this repository into the local cache
resbench score SUBMISSION --out results.json
Contents
| path | what |
|---|---|
manifest.csv |
one row per reference item: task, environment, id, conditioning parameters, well position |
volumes/<env>/volumes.npz |
512 test-split 64 x 64 x 32 facies windows per environment, regenerated bit for bit from the dataset rows |
repeats/<env>/cond0-4.npz |
256 ResMill runs of each of five fixed parameter rows (unconditional repeats) |
repeats/<env>/well1-5.npz |
five well conditions per environment, each an ensemble of windows whose centre column matches the well exactly |
fields/<env>/fields.npz |
32 field-scale volumes per environment (lobe and delta 512 x 512 x 32, channels 512 x 128 x 32), each the dataset-rule 32-cell window of a 64-cell engine column |
ENGINE.txt |
the ResMill commit every volume came from |
Eight environments: lobe, delta and six channel presets (PV_SHOESTRING,
CB_LABYRINTH, CB_JIGSAW, SH_DISTAL, SH_PROXIMAL, MEANDER_OXBOW). Volumes are
binary sand (1) / mud (0), int8.
Everything here can be rebuilt from the ResBench repository's tools/
(command sequence in its SPEC.md) and ResMill at the commit in ENGINE.txt.
Scored against itself, this reference matches in all 31 (task, check) cells.