environment stringclasses 8
values | n int64 32 32 | extent listlengths 3 3 | cell_m listlengths 3 3 | engine_nz int64 64 64 | window_z0 listlengths 32 32 | elongated_along_flow bool 2
classes | grid dict | seed_base int64 2.03B 2.03B | mean_ntg float64 0.14 0.62 | flow_fifths listlengths 5 5 | flow_last_over_first float64 0.57 1.64 | complete bool 1
class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
channel:CB_JIGSAW | 32 | [
512,
128,
32
] | [
10,
10,
1
] | 64 | [
16,
24,
21,
1,
31,
31,
15,
12,
13,
24,
20,
8,
9,
21,
1,
14,
30,
28,
24,
4,
12,
9,
22,
28,
3,
24,
3,
14,
3,
21,
20,
28
] | true | {
"nx": 512,
"ny": 128,
"nz": 64,
"x_len": 5120,
"y_len": 1280,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.384945 | [
0.28034037351608276,
0.35290002822875977,
0.4022984206676483,
0.4313673973083496,
0.4569447934627533
] | 1.629964 | true |
channel:CB_LABYRINTH | 32 | [
512,
128,
32
] | [
10,
10,
1
] | 64 | [
30,
15,
29,
13,
9,
8,
11,
18,
24,
22,
25,
11,
25,
5,
11,
11,
10,
15,
13,
21,
26,
15,
6,
6,
9,
2,
10,
27,
22,
18,
31,
16
] | true | {
"nx": 512,
"ny": 128,
"nz": 64,
"x_len": 5120,
"y_len": 1280,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.309925 | [
0.27046629786491394,
0.3093675971031189,
0.3199358582496643,
0.32373666763305664,
0.32586655020713806
] | 1.204832 | true |
channel:MEANDER_OXBOW | 32 | [
512,
128,
32
] | [
10,
10,
1
] | 64 | [
15,
9,
1,
25,
4,
30,
25,
18,
3,
3,
19,
27,
2,
27,
6,
8,
15,
22,
27,
18,
4,
28,
23,
26,
3,
31,
13,
17,
18,
26,
11,
9
] | true | {
"nx": 512,
"ny": 128,
"nz": 64,
"x_len": 5120,
"y_len": 1280,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.38096 | [
0.4685423672199249,
0.4502770006656647,
0.3870704472064972,
0.333965003490448,
0.2660130560398102
] | 0.567746 | true |
channel:PV_SHOESTRING | 32 | [
512,
128,
32
] | [
10,
10,
1
] | 64 | [
15,
13,
21,
22,
18,
19,
26,
22,
29,
14,
19,
24,
17,
5,
8,
25,
24,
14,
31,
24,
14,
19,
3,
4,
24,
23,
1,
1,
7,
18,
6,
3
] | true | {
"nx": 512,
"ny": 128,
"nz": 64,
"x_len": 5120,
"y_len": 1280,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.171211 | [
0.1926858127117157,
0.19838474690914154,
0.17791607975959778,
0.1536610871553421,
0.13370676338672638
] | 0.693911 | true |
channel:SH_DISTAL | 32 | [
512,
128,
32
] | [
10,
10,
1
] | 64 | [
13,
28,
28,
13,
16,
30,
7,
22,
8,
28,
11,
13,
10,
5,
25,
2,
20,
2,
2,
21,
5,
11,
20,
3,
22,
24,
12,
30,
18,
5,
25,
14
] | true | {
"nx": 512,
"ny": 128,
"nz": 64,
"x_len": 5120,
"y_len": 1280,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.621454 | [
0.5843032598495483,
0.6285147666931152,
0.6283391118049622,
0.6320132613182068,
0.6339098811149597
] | 1.084899 | true |
channel:SH_PROXIMAL | 32 | [
512,
128,
32
] | [
10,
10,
1
] | 64 | [
26,
8,
8,
26,
6,
3,
8,
2,
4,
11,
31,
15,
11,
3,
4,
22,
31,
17,
26,
22,
1,
24,
21,
2,
2,
5,
30,
26,
5,
24,
9,
1
] | true | {
"nx": 512,
"ny": 128,
"nz": 64,
"x_len": 5120,
"y_len": 1280,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.471275 | [
0.33926495909690857,
0.43656936287879944,
0.49154743552207947,
0.5312603712081909,
0.5566949844360352
] | 1.640886 | true |
delta | 32 | [
512,
512,
32
] | [
10,
10,
1
] | 64 | [
25,
27,
12,
16,
30,
7,
17,
11,
18,
6,
1,
9,
15,
10,
28,
24,
18,
12,
25,
23,
14,
5,
19,
20,
1,
29,
2,
7,
30,
22,
19,
21
] | false | {
"nx": 512,
"ny": 512,
"nz": 64,
"x_len": 5120,
"y_len": 5120,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.14155 | [
0.10307631641626358,
0.16086752712726593,
0.21393442153930664,
0.14236848056316376,
0.087322898209095
] | 0.847167 | true |
lobe | 32 | [
512,
512,
32
] | [
100,
100,
1
] | 64 | [
28,
7,
12,
13,
26,
21,
28,
17,
2,
3,
26,
2,
6,
1,
11,
30,
27,
20,
16,
3,
18,
12,
17,
13,
5,
1,
5,
10,
26,
26,
23,
9
] | false | {
"nx": 512,
"ny": 512,
"nz": 64,
"x_len": 51200,
"y_len": 51200,
"z_len": 64,
"top_depth": 5000,
"dip": 0,
"kzkx": 0.1
} | 2,026,091,800 | 0.513853 | [
0.5161235332489014,
0.5120559334754944,
0.5123841166496277,
0.5129463076591492,
0.5157498121261597
] | 0.999276 | true |
ResBench reference
The reference data that ResBench scores
generative reservoir models against. Every volume was produced by the ResMill
rule-based 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] @ git+https://github.com/SciLM-ai/ResBench"
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 (tools/reference_self_check.py), the repeats-based checks land at 1.00
on average and the others at about 0.7, since the self-check reuses the reference volumes
(details in the repository's STATUS.md).
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