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.gitattributes

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+# Hugging Face — recommended .gitattributes for monteirot/wellbench
+#
+# All current files in this dataset are CSVs under 10 MB, so LFS is optional —
+# but tracking these patterns now means future Parquet exports, larger CSV
+# bundles, or model checkpoints uploaded into the repo will go through LFS
+# automatically. Hugging Face requires LFS for any single file > 10 MB.
+
+# --- Tabular data formats ---
+*.csv     filter=lfs diff=lfs merge=lfs -text
+*.tsv     filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.feather filter=lfs diff=lfs merge=lfs -text
+*.arrow   filter=lfs diff=lfs merge=lfs -text
+*.jsonl   filter=lfs diff=lfs merge=lfs -text
+*.ndjson  filter=lfs diff=lfs merge=lfs -text
+
+# --- Compressed archives (in case you ever bundle a release zip) ---
+*.zip     filter=lfs diff=lfs merge=lfs -text
+*.tar.gz  filter=lfs diff=lfs merge=lfs -text
+*.tgz     filter=lfs diff=lfs merge=lfs -text
+
+# --- Model checkpoints (in case CTGAN .pkl files are mirrored here) ---
+*.pkl     filter=lfs diff=lfs merge=lfs -text
+*.pt      filter=lfs diff=lfs merge=lfs -text
+*.pth     filter=lfs diff=lfs merge=lfs -text
+*.bin     filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+
+# --- Plain-text files that should NOT go through LFS ---
+*.md      text
+*.txt     text
+*.json    text
+*.yaml    text
+*.yml     text
+*.py      text
+*.toml    text
+.gitattributes text

DEPLOY.md

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+# Deploying the upgraded Hugging Face dataset card
+
+This folder contains drop-in replacements for the metadata files in the
+[`monteirot/wellbench`](https://huggingface.co/datasets/monteirot/wellbench)
+dataset repo:
+
+| File              | Replaces                                                      |
+|-------------------|---------------------------------------------------------------|
+| `README.md`       | The current 376-byte README that only says "Benchmark data: …"|
+| `.gitattributes`  | The current `.gitattributes` (recommended LFS rules)          |
+
+Your existing `croissant.json` (31.9 kB) and the data folders
+(`synthetic_datasets/`, `CTGAN_synthetic_data/`) are **not** modified.
+
+---
+
+## How to upload
+
+### Option A — Web UI (one file at a time)
+
+1. Open <https://huggingface.co/datasets/monteirot/wellbench/tree/main>.
+2. Click the existing `README.md` → **Edit** (pencil icon) → paste the new
+   contents of `README.md` → **Commit changes**.
+3. Repeat for `.gitattributes`.
+
+### Option B — `huggingface-cli` (recommended for both files at once)
+
+```bash
+pip install -U huggingface_hub
+huggingface-cli login
+
+cd huggingface/    # this folder
+huggingface-cli upload monteirot/wellbench README.md       --repo-type dataset
+huggingface-cli upload monteirot/wellbench .gitattributes  --repo-type dataset
+```
+
+### Option C — Git over HTTPS (full repo flow)
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/monteirot/wellbench
+cd wellbench
+
+cp ../huggingface/README.md       README.md
+cp ../huggingface/.gitattributes  .gitattributes
+
+git add README.md .gitattributes
+git commit -m "Upgrade dataset card and LFS rules"
+git push
+```
+
+---
+
+## After uploading — verify in this order
+
+1. **Card YAML parses cleanly.** Refresh
+   <https://huggingface.co/datasets/monteirot/wellbench>. The left rail
+   should now show:
+   - **Tasks:** Tabular Regression · Time Series Forecasting
+   - **Modalities:** Tabular
+   - **Tags:** synthetic-data · synthetic-tabular · well-logs · petrophysics ·
+     pore-pressure · ctgan · physics-based · benchmark · neurips · …
+   - **License:** MIT
+   - **Size:** 100K&nbsp;<&nbsp;n&nbsp;<&nbsp;1M
+
+2. **All six configs appear.** The Dataset Viewer top-bar should show the
+   selector with: `physics_zone_1` (default) · `physics_zone_2` ·
+   `physics_zone_3` · `ctgan_zone_1` · `ctgan_zone_2` · `ctgan_zone_3`.
+   Each should expose its three named splits.
+
+3. **The Viewer renders rows.** Pick `physics_zone_1 / missa_keswal_01` —
+   you should see numerical columns `DEPTH`, `GR`, `DT`, `RHOB`, `RT`, `HP`,
+   `OB`, `DT_NCT`, `PPP`. If column types look wrong, the auto-Parquet
+   conversion needs a re-run; click **Refresh dataset** in the Settings.
+
+4. **`load_dataset` round-trips.** From a fresh Python:
+
+   ```python
+   from datasets import load_dataset
+   ds = load_dataset("monteirot/wellbench", "physics_zone_1")
+   print(ds)
+   df = ds["missa_keswal_01"].to_pandas()
+   assert {"DEPTH","GR","DT","RHOB","RT","HP","OB","DT_NCT","PPP"} <= set(df.columns)
+   ```
+
+5. **Croissant validates.** The Hub auto-generates a Croissant record from
+   the README YAML at the `/croissant` endpoint. Confirm it's well-formed:
+
+   ```bash
+   pip install "mlcroissant[parquet]"
+   mlcroissant validate \
+     --jsonld https://huggingface.co/api/datasets/monteirot/wellbench/croissant
+   # exit 0 → ready for the NeurIPS D&B submission requirement
+   ```
+
+   Optional: run the same validator on the **manually authored**
+   `croissant.json` you already host:
+
+   ```bash
+   curl -L https://huggingface.co/datasets/monteirot/wellbench/resolve/main/croissant.json \
+     -o /tmp/croissant.json
+   mlcroissant validate --jsonld /tmp/croissant.json
+   ```
+
+---
+
+## Croissant: the two files explained
+
+There are now effectively **two** Croissant records for this dataset, and
+that's fine:
+
+| Source                                              | Generated from        | Use it for                                 |
+|-----------------------------------------------------|-----------------------|--------------------------------------------|
+| `…/api/datasets/monteirot/wellbench/croissant`      | README YAML (auto)    | Discovery, NeurIPS D&B compliance check    |
+| `…/resolve/main/croissant.json`                     | Hand-authored         | The `mlc.Dataset("croissant.json")` example in your code, custom record sets |
+
+**Recommended:** keep both. The auto-generated one is regenerated on every
+push and always reflects the current YAML; the hand-authored one lets you
+expose richer record sets (like your `physics-samples` example) that the
+auto-generator doesn't infer from filenames alone.
+
+If the two ever drift in licence, citation, or column types, fix the
+hand-authored file — reviewers will compare them.
+
+---
+
+## Optional next steps
+
+These are not in this folder, but are worth doing before NeurIPS submission:
+
+1. **Add a Croissant-RAI block** to `croissant.json` — `dataLifecycle`,
+   `dataCollection`, `dataPreprocessing`, `dataLimitations`, `dataBiases`,
+   `personalSensitiveInformation`. Use <https://croissant-editor.com/>.
+
+2. **Convert CSVs to Parquet** for the Viewer to run faster and column
+   types to be preserved without inference. Hugging Face will auto-convert
+   on the `refs/convert/parquet` branch, but committing Parquet directly
+   gives you control over schemas and row-group sizes.
+
+3. **Mint a Zenodo DOI** by tagging a release of the GitHub repo and
+   enabling the Zenodo–GitHub integration. Add the DOI to both the GitHub
+   README and the Citation block of this dataset card.
+
+4. **Cross-link**: ensure the Hugging Face dataset card and the GitHub
+   README both link bidirectionally and that the BibTeX entries match.

README.md

@@ -1,21 +1,434 @@
 ---
+pretty_name: wellbench
 license: mit
-task_categories:
-- time-series-forecasting
 language:
-- en
+  - en
+multilinguality: monolingual
+modalities:
+  - tabular
+task_categories:
+  - tabular-regression
+  - time-series-forecasting
+task_ids:
+  - tabular-multi-output-regression
 size_categories:
-- 1K<n<10K
+  - 100K<n<1M
+annotations_creators:
+  - machine-generated
+language_creators:
+  - machine-generated
+source_datasets:
+  - extended|other
+tags:
+  - synthetic-data
+  - synthetic-tabular
+  - tabular
+  - well-logs
+  - petrophysics
+  - pore-pressure
+  - geoscience
+  - ctgan
+  - physics-based
+  - benchmark
+  - neurips
+viewer: true
+configs:
+  # ---------- Physics-based generator (deterministic, calibrated) ----------
+  - config_name: physics_zone_1
+    description: Eastern Potwar Basin — Missa Keswal field. Three synthetic wells calibrated against MISSA-KESWAL-01/02/03.
+    default: true
+    data_files:
+      - split: missa_keswal_01
+        path: "synthetic_datasets/zone_1/synth_MISSA-KESWAL-01.csv"
+      - split: missa_keswal_02
+        path: "synthetic_datasets/zone_1/synth_MISSA-KESWAL-02.csv"
+      - split: missa_keswal_03
+        path: "synthetic_datasets/zone_1/synth_MISSA-KESWAL-03.csv"
+  - config_name: physics_zone_2
+    description: Eastern Potwar Basin — Pindori field. Three synthetic wells calibrated against PINDORI-1/2/3.
+    data_files:
+      - split: pindori_1
+        path: "synthetic_datasets/zone_2/synth_PINDORI-1.csv"
+      - split: pindori_2
+        path: "synthetic_datasets/zone_2/synth_PINDORI-2.csv"
+      - split: pindori_3
+        path: "synthetic_datasets/zone_2/synth_PINDORI-3.csv"
+  - config_name: physics_zone_3
+    description: Eastern Potwar Basin — Joyamair / Minwal area. Three synthetic wells calibrated against JOYAMAIR-4, MINWAL-2, MINWAL-X-1.
+    data_files:
+      - split: joyamair_4
+        path: "synthetic_datasets/zone_3/synth_JOYAMAIR-4.csv"
+      - split: minwal_2
+        path: "synthetic_datasets/zone_3/synth_MINWAL-2.csv"
+      - split: minwal_x_1
+        path: "synthetic_datasets/zone_3/synth_MINWAL-X-1.csv"
+
+  # ---------- CTGAN baseline (learned tabular generator) ----------
+  - config_name: ctgan_zone_1
+    description: CTGAN baseline trained on Missa Keswal logs.
+    data_files:
+      - split: missa_keswal_01
+        path: "CTGAN_synthetic_data/zone_1/synth_MISSA-KESWAL-01.csv"
+      - split: missa_keswal_02
+        path: "CTGAN_synthetic_data/zone_1/synth_MISSA-KESWAL-02.csv"
+      - split: missa_keswal_03
+        path: "CTGAN_synthetic_data/zone_1/synth_MISSA-KESWAL-03.csv"
+  - config_name: ctgan_zone_2
+    description: CTGAN baseline trained on Pindori logs.
+    data_files:
+      - split: pindori_1
+        path: "CTGAN_synthetic_data/zone_2/synth_PINDORI-1.csv"
+      - split: pindori_2
+        path: "CTGAN_synthetic_data/zone_2/synth_PINDORI-2.csv"
+      - split: pindori_3
+        path: "CTGAN_synthetic_data/zone_2/synth_PINDORI-3.csv"
+  - config_name: ctgan_zone_3
+    description: CTGAN baseline trained on Joyamair / Minwal logs.
+    data_files:
+      - split: joyamair_4
+        path: "CTGAN_synthetic_data/zone_3/synth_JOYAMAIR-4.csv"
+      - split: minwal_2
+        path: "CTGAN_synthetic_data/zone_3/synth_MINWAL-2.csv"
+      - split: minwal_x_1
+        path: "CTGAN_synthetic_data/zone_3/synth_MINWAL-X-1.csv"
 ---
 
+# wellbench — Synthetic Well-Log Benchmark for Pore-Pressure Prediction
+
+> Companion dataset for the [`wellbench`](https://github.com/monteirot/wellbench) Python library: a physics-based and CTGAN-based synthetic-data benchmark calibrated against real wells from the Eastern Potwar Basin (Pakistan), released for reproducible pore-pressure prediction research.
+
+| | |
+|---|---|
+| **Modality** | Tabular (single-table well logs) |
+| **Generators** | Physics-based (deterministic) + CTGAN baseline |
+| **Wells** | 9 real wells × 2 generators = 18 synthetic CSVs |
+| **Total size** | ~43 MB |
+| **License** | MIT |
+| **Library** | [`wellbench`](https://pypi.org/project/wellbench/) on PyPI |
+| **Code** | <https://github.com/monteirot/wellbench> |
+| **Croissant** | <https://huggingface.co/api/datasets/monteirot/wellbench/croissant> |
+
+---
+
+## Quick start
+
+### Load with 🤗 Datasets
+
+```python
+from datasets import load_dataset
+
+# All three Missa Keswal wells (physics generator), zone 1
+ds = load_dataset("monteirot/wellbench", "physics_zone_1")
+print(ds)
+# DatasetDict({
+#   missa_keswal_01: Dataset({features: [...], num_rows: ...}),
+#   missa_keswal_02: Dataset({features: [...], num_rows: ...}),
+#   missa_keswal_03: Dataset({features: [...], num_rows: ...}),
+# })
+
+# A single well as a pandas DataFrame
+df = load_dataset(
+    "monteirot/wellbench",
+    "physics_zone_1",
+    split="missa_keswal_01",
+).to_pandas()
+print(df.head())
+```
+
+### Load via Croissant + mlcroissant
 
+```python
 import mlcroissant as mlc
 import pandas as pd
 
-ds = mlc.Dataset("croissant.json")
+ds = mlc.Dataset("https://huggingface.co/api/datasets/monteirot/wellbench/croissant")
 df = pd.DataFrame(ds.records(record_set="physics-samples"))
 
+# A reproducible train / val / test split inside zone 1
 train = df[df.well == "MISSA-KESWAL-01"]
 val   = df[df.well == "MISSA-KESWAL-02"]
 test  = df[df.well == "MISSA-KESWAL-03"]
+```
+
+### Load directly with pandas
+
+```python
+import pandas as pd
+
+URL = "https://huggingface.co/datasets/monteirot/wellbench/resolve/main"
+df = pd.read_csv(f"{URL}/synthetic_datasets/zone_1/synth_MISSA-KESWAL-01.csv")
+```
+
+### Validate the Croissant metadata locally
+
+```bash
+pip install "mlcroissant[parquet]"
+mlcroissant validate \
+  --jsonld https://huggingface.co/api/datasets/monteirot/wellbench/croissant
+```
+
+---
+
+## Dataset description
+
+`wellbench` is a synthetic well-log benchmark designed for reproducible methods research on **pore-pressure prediction** and related petrophysical inverse problems. It is the data artefact for the [`wellbench`](https://github.com/monteirot/wellbench) Python package and the [arXiv:XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX) paper *(placeholder — replace with the canonical reference once available)*.
+
+The dataset addresses two long-standing pain points in machine learning for subsurface geoscience:
+
+1. **Real well logs are scarce, fragmented, and frequently encumbered by commercial confidentiality**, which limits reproducible benchmarking across studies.
+2. **General-purpose tabular synthesisers** (CTGAN, TVAE, TabDDPM, …) treat well logs as i.i.d. rows and ignore the petrophysical relationships (Athy compaction, Wyllie / Archie / Eaton equations) that any plausible log must satisfy.
+
+`wellbench` provides synthetic copies of nine real wells, each generated by two complementary methods:
+
+- A **deterministic physics generator** whose outputs are valid by construction — they obey the same closed-form transforms a petrophysicist would apply, with regional parameters tuned via [Optuna](https://optuna.org/) using **Jensen–Shannon divergence** and **Wasserstein-1 distance** against real-data marginals.
+- A **CTGAN baseline** (Xu et al. 2019) trained on the same cleaned real wells, exposed under an identical `.generate()` interface for side-by-side comparison.
+
+### Supported tasks
+
+- **Tabular regression** — predict pore pressure (`PPP`) from `GR`, `DT`, `RHOB`, `RT`, depth, and the derived hydrostatic / overburden / NCT columns.
+- **Time-series forecasting** — depth-indexed log forecasting, where `DEPTH` plays the role of the time axis.
+- **Synthetic-data evaluation** — fidelity / utility / privacy comparison between physics-based and learned tabular synthesis.
+- **Data augmentation** — pretrain or augment small real-well training sets for downstream petrophysical tasks.
+
+### Languages
+
+Column names and documentation are in English (`en`). The data itself is numerical and language-agnostic.
+
+---
+
+## Dataset structure
+
+### File layout
+
+```
+monteirot/wellbench
+├── synthetic_datasets/                  # Physics-based generator outputs
+│   ├── zone_1/                          # Missa Keswal (Eastern Potwar Basin)
+│   │   ├── synth_MISSA-KESWAL-01.csv
+│   │   ├── synth_MISSA-KESWAL-02.csv
+│   │   └── synth_MISSA-KESWAL-03.csv
+│   ├── zone_2/                          # Pindori field
+│   │   ├── synth_PINDORI-1.csv
+│   │   ├── synth_PINDORI-2.csv
+│   │   └── synth_PINDORI-3.csv
+│   └── zone_3/                          # Joyamair / Minwal area
+│       ├── synth_JOYAMAIR-4.csv
+│       ├── synth_MINWAL-2.csv
+│       └── synth_MINWAL-X-1.csv
+├── CTGAN_synthetic_data/                # CTGAN-baseline outputs (same layout)
+│   ├── zone_1/  …
+│   ├── zone_2/  …
+│   └── zone_3/  …
+├── README.md                            # this dataset card
+├── croissant.json                       # MLCommons Croissant 1.0 metadata
+└── .gitattributes
+```
+
+### Configurations and splits
+
+The dataset exposes **6 configs** (2 generators × 3 zones), with one **split per real well** under each config:
+
+| Config              | Generator     | Zone | Splits                                            | Default |
+|---------------------|---------------|:----:|---------------------------------------------------|:-------:|
+| `physics_zone_1`    | Physics       |   1  | `missa_keswal_01` · `missa_keswal_02` · `missa_keswal_03` | ✅ |
+| `physics_zone_2`    | Physics       |   2  | `pindori_1` · `pindori_2` · `pindori_3`           |        |
+| `physics_zone_3`    | Physics       |   3  | `joyamair_4` · `minwal_2` · `minwal_x_1`          |        |
+| `ctgan_zone_1`      | CTGAN         |   1  | `missa_keswal_01` · `missa_keswal_02` · `missa_keswal_03` |        |
+| `ctgan_zone_2`      | CTGAN         |   2  | `pindori_1` · `pindori_2` · `pindori_3`           |        |
+| `ctgan_zone_3`      | CTGAN         |   3  | `joyamair_4` · `minwal_2` · `minwal_x_1`          |        |
+
+A **suggested train/val/test mapping** mirroring the convention used in the paper:
+
+| Zone | train            | validation       | test             |
+|:----:|------------------|------------------|------------------|
+|  1   | `missa_keswal_01`| `missa_keswal_02`| `missa_keswal_03`|
+|  2   | `pindori_1`      | `pindori_2`      | `pindori_3`      |
+|  3   | `joyamair_4`     | `minwal_2`       | `minwal_x_1`     |
+
+Users are free to ignore this convention; each split is a self-contained synthetic well.
+
+### Data instances
+
+Each row is a single depth measurement of a single (synthetic) well. An example row from `physics_zone_1 / missa_keswal_01`:
+
+```json
+{
+  "DEPTH": 500.0,
+  "GR": 140.2155,
+  "DT": 138.1432,
+  "RHOB": 1.6418,
+  "RT": 12.7382,
+  "HP": 645.94,
+  "OB": 926.85,
+  "DT_NCT": 137.43,
+  "PPP": 615.33
+}
+```
+
+### Data fields
+
+All 18 CSVs in this release belong to pore-pressure regions (zones 1–3), so all of the columns below are present:
+
+| Column     | Type   | Units / range                  | Description                                          |
+|------------|:------:|--------------------------------|------------------------------------------------------|
+| `DEPTH`    | float  | ft (region-dependent)          | Measured depth — the row index along the well axis  |
+| `GR`       | float  | API, `[0, 200]`                | Gamma ray (shale-volume proxy)                       |
+| `DT`       | float  | µs/ft, `[30, 180]`             | Sonic transit time                                   |
+| `RHOB`     | float  | g/cc, `[1.2, 2.9]`             | Bulk density                                         |
+| `RT`       | float  | Ω·m, `[0.01, 10 000]`          | True resistivity                                     |
+| `HP`       | float  | psi                            | Hydrostatic pressure                                 |
+| `OB`       | float  | psi                            | Overburden pressure                                  |
+| `DT_NCT`   | float  | µs/ft                          | Sonic normal-compaction-trend reference              |
+| `PPP`      | float  | psi                            | **Pore pressure** (Eaton's method) — primary target  |
+
+All values are clipped to `wellbench.PHYSICAL_BOUNDS`, the same physical envelope enforced by the library's `clean_well_data` routine.
+
+---
+
+## Dataset creation
+
+### Curation rationale
+
+Public well-log corpora suitable for ML benchmarking are dominated by a handful of repeatedly-cited datasets (e.g. Volve, Force-2020), whose pore-pressure labels are not always disclosed and whose proprietary status complicates redistribution. `wellbench` provides a fully synthetic, openly licensed alternative that:
+
+- **Is calibrated** to real-world basin parameters rather than sampled from a synthetic prior in vacuum.
+- **Pairs physics and learned generators** under one schema, so studies can isolate the contribution of physical priors.
+- **Ships under MIT** so there is no friction for downstream redistribution.
+
+### Source data
+
+Regional parameter sets (`REGION_1`, `REGION_2`, `REGION_3` in the [`wellbench`](https://github.com/monteirot/wellbench) library) were calibrated against publicly described wells from the **Eastern Potwar Basin, Punjab, Pakistan**:
+
+- **Zone 1 — Missa Keswal field** (MISSA-KESWAL-01, -02, -03)
+- **Zone 2 — Pindori field** (PINDORI-1, -2, -3)
+- **Zone 3 — Joyamair / Minwal area** (JOYAMAIR-4, MINWAL-2, MINWAL-X-1)
+
+The synthetic outputs published here are *not* anonymised real data; they are generated outputs of the calibrated `wellbench` models. Real-well log values are not redistributed.
+
+### Generation pipeline
+
+#### Physics generator
+
+1. Athy's exponential compaction → porosity profile φ(z).
+2. Wyllie time-average equation → DT from φ.
+3. Bulk-density mixing law → RHOB from φ + lithology trend.
+4. Archie's equation → RT from φ + saturation prior.
+5. Shale-volume linear mixing → GR.
+6. Hydrostatic + overburden integration; Eaton's method on the normal compaction trend → `HP`, `OB`, `DT_NCT`, `PPP`.
+
+Per-region parameters were optimised with **Optuna** to minimise a weighted sum of **Jensen–Shannon divergence** and **Wasserstein-1 distance** between synthetic and real marginals across all log columns. The library version, region dictionary, and seed used to produce each CSV are recorded in `croissant.json`.
+
+#### CTGAN baseline
+
+The five CTGAN checkpoints bundled in the [`wellbench`](https://pypi.org/project/wellbench/) package (`ctgan_r1.pkl` … `ctgan_r5.pkl`) were trained on cleaned real wells from each region. CTGAN samples are i.i.d. rows; `wellbench` orders them along a user-supplied depth axis, applies a column-rename to match the physics schema, and clips to `PHYSICAL_BOUNDS`. The CSVs in `CTGAN_synthetic_data/` were generated with deterministic seeds and the bundled checkpoints — see `wellbench.load_ctgan_generator(region_index=…)` for the exact loader.
+
+### Annotations
+
+None. The dataset is fully machine-generated; there is no human annotation step.
+
+### Personal and sensitive information
+
+The synthetic data does not contain personally identifying information. Real wells used for calibration are publicly described in the petroleum-geology literature; no proprietary log values are redistributed in this release.
+
+---
+
+## Considerations for using the data
+
+### Social impact
+
+Open synthetic well logs lower the barrier to entry for ML research on subsurface geoscience and reduce reliance on confidentiality-restricted operator data. Better pore-pressure prediction tools have direct safety implications for drilling.
+
+### Discussion of biases
+
+- **Geographic bias.** All nine wells are from a single basin (Eastern Potwar). Generalisation to other tectonic settings is the user's responsibility.
+- **Lithological bias.** The bundled regions emphasise shale-dominated, clastic settings. Carbonates and evaporites are not represented.
+- **Calibration target bias.** Calibration optimises marginal fidelity (JS + Wasserstein-1) — joint fidelity and cross-column residual structure are not directly optimised, so multivariate downstream models may see slightly different correlation structure than in real wells.
+- **Public-record selection bias.** Real wells used for CTGAN training are those whose logs are publicly described. Wells absent from the open record (often the most challenging ones) are by construction absent here.
+
+### Known limitations
+
+- **Synthetic ≠ real.** Subtle stratigraphic features useful for prospect evaluation are deliberately not modelled. Use this dataset for *methods* research, not for operational prospect ranking.
+- **CTGAN depth ordering is post-hoc.** CTGAN samples i.i.d. rows; vertical correlation is therefore weaker than in the physics generator and weaker than in real wells. This is by design — it is a *baseline* for comparison.
+- **Eaton-only PP model.** Pore-pressure outputs reflect under-compaction signatures; other overpressure mechanisms (kerogen maturation, tectonic loading) are not modelled.
+- **Three zones only.** The library defines five regions, but only the three pore-pressure regions are included in this release.
+
+### Out-of-scope use
+
+- ❌ **Operational drilling decisions.** The synthetic logs capture *distributional* properties of real basins, not the physics of any specific surveyed location.
+- ❌ **Substituting for proprietary well data.** Use this for benchmarking, not to evade data-sharing constraints.
+- ❌ **Privacy proxy without further audit.** The physics generator does not memorise real samples by construction, but the bundled CTGAN baseline was trained on cleaned real wells and has not been audited for membership inference. Treat CTGAN outputs as research artefacts, not as anonymised data.
+- ❌ **Training models that will be deployed against carbonate or evaporite reservoirs without recalibration.**
+
+### Recommended evaluation
+
+When using this dataset to benchmark pore-pressure or petrophysical models, we recommend reporting at minimum:
+
+- **Fidelity.** Jensen–Shannon divergence and Wasserstein-1 distance per log column, vs. the corresponding real well distribution (where available to the user).
+- **Utility.** TSTR (train-on-synthetic, test-on-real) AUROC / RMSE on a downstream pore-pressure classifier or regressor.
+- **Generator comparison.** Run the same downstream model on `physics_zone_*` and `ctgan_zone_*` and report the gap.
+
+A reference evaluation harness is provided in [`wellbench.metrics`](https://github.com/monteirot/wellbench).
+
+---
+
+## Additional information
+
+### Dataset curators
+
+Maintained by [@monteirot](https://huggingface.co/monteirot). See the [GitHub repository](https://github.com/monteirot/wellbench) for the full author list and affiliations.
+
+### Licensing information
+
+This dataset is released under the **MIT License**. The synthetic outputs are derivative of the [`wellbench`](https://github.com/monteirot/wellbench) library, which is also MIT-licensed. Real wells used for calibration are publicly described in the petroleum-geology literature and their values are not redistributed here.
+
+### Citation
+
+If you use `wellbench` data in your research, please cite both the paper and the software release:
+
+```bibtex
+@inproceedings{wellbench2026,
+  title     = {wellbench: A Physics-Calibrated Synthetic Benchmark for Pore-Pressure Prediction},
+  author    = {Monteiro, T. and ...},
+  booktitle = {Advances in Neural Information Processing Systems, Datasets and Benchmarks Track},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/XXXX.XXXXX}
+}
+
+@software{wellbench_software,
+  author  = {Monteiro, T. and ...},
+  title   = {wellbench: Physics-based synthetic well-log benchmark generator},
+  year    = {2026},
+  version = {<release>},
+  doi     = {10.5281/zenodo.XXXXXXX},
+  url     = {https://github.com/monteirot/wellbench}
+}
+```
+
+For the dataset specifically, please also link to <https://huggingface.co/datasets/monteirot/wellbench> so the Croissant record is discoverable.
+
+### Contributions
+
+Issues, corrections, and additional regional calibrations are very welcome. Please open a discussion on the [Hugging Face Community tab](https://huggingface.co/datasets/monteirot/wellbench/discussions) or a pull request on [GitHub](https://github.com/monteirot/wellbench).
+
+### Reproducing the dataset
+
+The CSVs in this repository are exactly reproducible from the `wellbench` library:
+
+```bash
+pip install "wellbench[ctgan]"
+
+# Physics generator — zone 1, all three Missa Keswal wells
+python -c "
+import pandas as pd
+from wellbench import SyntheticWellLogGenerator, REGION_1
+real = pd.read_csv('real_well.csv', usecols=['DEPTH'])  # supply your own
+SyntheticWellLogGenerator(REGION_1).generate(
+    seed=42, depth=real['DEPTH'].to_numpy(),
+).to_csv('synth_MISSA-KESWAL-01.csv', index=False)
+"
+
+# Or reproduce the canonical 15-dataset benchmark
+wellbench -o benchmark/
+```
 
+The exact commands and seeds used to produce each CSV are recorded in the `wellbench` repository under `benchmarks/` and in the Croissant metadata (`croissant.json`) of this dataset.