| --- |
| license: mit |
| pretty_name: ResBench reference |
| tags: |
| - geology |
| - reservoir-modeling |
| - benchmark |
| - 3d |
| --- |
| |
| # ResBench reference |
|
|
| The reference data that [ResBench](https://github.com/SciLM-ai/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](https://huggingface.co/datasets/SciLM/SiliciclasticReservoirs) |
| training dataset. You do not need to download it by hand: |
|
|
| ```bash |
| 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. |
|
|
