README.md

---
pretty_name: wellbench
license: mit
language:
  - en
multilinguality: monolingual
modalities:
  - tabular
task_categories:
  - tabular-regression
  - time-series-forecasting
task_ids:
  - tabular-single-column-regression
  - multivariate-time-series-forecasting
size_categories:
  - 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("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.