README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- synthetic
- oil-and-gas
- upstream
- core-analysis
- petrophysics
- rock-properties
- SCAL
- mineralogy
- geomechanics
- xpertsystems
pretty_name: "OIL-006 — Synthetic Core Sample Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-006 — Synthetic Core Sample Dataset (Sample)

**SKU:** `OIL006-SAMPLE` · **Vertical:** Oil & Gas / Upstream Core Analysis & Petrophysics
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil006.v1`
**Generator version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise core-analysis
dataset for petrophysics, SCAL, mineralogy, and geomechanics ML.
The sample covers **500 cores** across **10 global hydrocarbon
basins** with **37,398 plug measurements** linked across **11 tables**.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `cores_master.csv` | 500 | 12 | Core spine: basin, formation, depth, recovery, preservation |
| `plug_measurements.csv` | 37,398 | 10 | Plug-level rock physics: porosity, permeability, grain & bulk density, lithology |
| `routine_core_analysis.csv` | 37,398 | 10 | RCA: helium φ, Klinkenberg k, Dean-Stark Sw/So/Sg, net overburden |
| `special_core_analysis.csv` | 9,212 | 13 | SCAL: capillary pressure, relperm, Archie a/m/n, wettability, Swirr/Sor |
| `fluid_saturations.csv` | 68,642 | 9 | Multi-state saturations (native / restored / cleaned) per plug |
| `lithology_descriptions.csv` | 38,012 | 10 | Per-foot lithology: grain size, sorting, cement, bedding, mineralogy |
| `xrd_xrf_analysis.csv` | 26,153 | 13 | Mineralogy: quartz/feldspar/clay/carbonate, illite/smectite/kaolinite/chlorite, TOC, kerogen, Ro |
| `thin_section_petrography.csv` | 18,521 | 9 | Pore architecture: primary/secondary φ, throat radius, diagenesis, fabric |
| `mercury_injection.csv` | 12,992 | 8 | MICP: entry pressure, median throat, displacement pressure, Swanson parameter |
| `geomechanical_tests.csv` | 9,993 | 10 | Geomech: Young's modulus, Poisson, UCS, brittleness, tensile strength |
| `core_labels.csv` | 500 | 6 | ML labels: reservoir quality A/B/C/D, pay zone flag, net pay, HC type |

Total: **259,321 rows** across 11 CSVs, ~20.6 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: API RP-40 (Recommended Practices for Core
Analysis), Society of Core Analysts (SCA), SPWLA petrophysical conventions,
Archie (1942), Anderson (1986) wettability survey (JPT), Kozeny-Carman,
ASTM D934 (XRD), SPE Petroleum Engineering Handbook, SPE Geomechanics
Handbook, and Chang et al. (2006) on E-UCS empirical correlation.

**Sample run** (seed `42`, n_cores=500):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg helium porosity pct | 14.3748 | 14.0 | ±4.0 | ✓ PASS | API RP-40 + SCA protocols — global mean helium porosity, mixed unconventional/conventional basin portfolio |
| 2 | avg grain density gcc | 2.7035 | 2.68 | ±0.08 | ✓ PASS | SPWLA Petrophysical Properties Reference — mixed mineralogy grain density (2.65 SS, 2.71 LS, 2.85 dolo) blended portfolio |
| 3 | avg water saturation pct | 31.7136 | 32.5 | ±8.0 | ✓ PASS | API RP-40 + SCA — Dean-Stark global mean water saturation, mixed reservoir portfolio |
| 4 | saturation mass balance pct | 100.0000 | 100.0 | ±1.0 | ✓ PASS | SCA / RP-40 — Sw + So + Sg sums to 100% within Dean-Stark measurement tolerance |
| 5 | log perm porosity correlation | 0.8164 | 0.75 | ±0.2 | ✓ PASS | Kozeny-Carman + SPE Petroleum Engineering Handbook — log(k) vs φ correlation, mixed-lithology core sample sets |
| 6 | mineralogy mass balance rate | 1.0000 | 0.99 | ±0.05 | ✓ PASS | ASTM D934 + SPWLA XRD/XRF protocols — mineralogy fractions sum to 100% within measurement uncertainty |
| 7 | avg archie m | 1.9514 | 2.0 | ±0.3 | ✓ PASS | Archie (1942) + SPWLA — cementation exponent m, global core analysis literature (typically 1.8-2.2) |
| 8 | avg wettability index | -0.1025 | -0.1 | ±0.3 | ✓ PASS | Amott-Harvey wettability index + Anderson (1986) JPT survey — mixed-to-oil-wet global portfolio mean |
| 9 | youngs ucs correlation | 0.9804 | 0.92 | ±0.1 | ✓ PASS | SPE Geomechanics Handbook + Chang et al. (2006) — static Young's modulus vs UCS empirical correlation |
| 10 | lithology diversity entropy | 0.9046 | 0.85 | ±0.15 | ✓ PASS | Global core analysis literature — 6-class lithology diversity benchmark (clean SS, shaly SS, tight SS, shale, carbonate, dolomite), normalized Shannon entropy |

**Overall: 100.0/100 — Grade A+**
(10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

---

## Schema highlights

**`plug_measurements.csv`** — the petrophysical spine, one row per plug.
Key columns: `plug_id`, `core_id`, `plug_depth_ft`, `lithology`
(6-class: clean_ss, shaly_ss, tight_ss, shale, carbonate, dolomite),
`porosity_pct`, `permeability_md`, `grain_density_gcc`, `bulk_density_gcc`.

**Porosity-permeability follows a Kozeny-Carman-style relation per basin:**

> log(k) ≈ log(k_basin_mean) + 6.0·(φ − φ_basin_mean) + ε

with basin priors calibrated to industry-typical values: Permian Wolfcamp
(φ̄ ≈ 8.5%, k̄ ≈ 0.08 mD), Marcellus (φ̄ ≈ 6.5%, k̄ ≈ 0.0003 mD),
North Sea Sandstone (φ̄ ≈ 22%, k̄ ≈ 350 mD), GoM Deepwater (φ̄ ≈ 26%, k̄ ≈ 800 mD),
Canadian Oil Sands (φ̄ ≈ 32%, k̄ ≈ 2500 mD), etc.

**`special_core_analysis.csv`** — Archie's law parameters per plug:

> F = a / φᵐ      (formation resistivity factor)

with a/m/n drawn from industry-typical ranges (a ≈ 1.0, m ≈ 1.95, n ≈ 2.0)
matching the SPWLA conventions and the original Archie (1942) JPT paper.

**`xrd_xrf_analysis.csv`** — Dirichlet-sampled mineralogy guaranteeing
mass balance (quartz + feldspar + clay + carbonate = 100% per row), plus
clay sub-fractions (illite/smectite/kaolinite/chlorite), TOC, kerogen
type (I/II/II-S/III/IV), and vitrinite reflectance (oil window ~0.6-1.3%,
gas window >1.3%).

**`geomechanical_tests.csv`** — porosity-modulated elastic properties:

> E_static ≈ 8e6 · (1 − 2.5·φ) + ε     (psi)
> UCS ≈ E_static / 250 + ε             (psi)

matching the Chang et al. (2006) empirical correlation for sedimentary
rocks.

---

## Suggested use cases

1. **Porosity-permeability regression** — train ML estimators of permeability
   from porosity + lithology + grain density using the 37,398-plug
   spine.
2. **Reservoir quality classification** — multi-class classifier on
   `reservoir_quality_grade` (A/B/C/D) from petrophysical features.
3. **Pay zone identification** — binary classification on `pay_zone_flag`
   from RCA + lithology + mineralogy features.
4. **SCAL surrogate models** — predict Archie m/n, wettability index, and
   relperm endpoints from petrophysical and mineralogical inputs (multi-
   output regression).
5. **Hydrocarbon type prediction** — 7-class classifier on `hydrocarbon_type`
   from basin, depth, and rock properties.
6. **Multi-table relational ML** — entity-resolution and graph-based
   learning across the 11 joinable tables via `core_id` / `plug_id`.
7. **Mineralogy → petrophysics ML** — predict porosity and permeability
   from XRD/XRF mineralogy (quartz/clay/carbonate/feldspar fractions).
8. **Geomechanical surrogates** — predict Young's modulus, UCS, and
   brittleness from porosity + lithology for unconventional completion
   design.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil006-sample", data_files="plug_measurements.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
cores = pd.read_csv("hf://datasets/xpertsystems/oil006-sample/cores_master.csv")
plugs = pd.read_csv("hf://datasets/xpertsystems/oil006-sample/plug_measurements.csv")
rca   = pd.read_csv("hf://datasets/xpertsystems/oil006-sample/routine_core_analysis.csv")
scal  = pd.read_csv("hf://datasets/xpertsystems/oil006-sample/special_core_analysis.csv")
joined = plugs.merge(rca, on="plug_id").merge(cores, on="core_id")
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter. The ID
conventions (`CORE_{i:06d}`, `PLUG_{i:08d}`, `RCA_{i:08d}`, etc.)
guarantee schema-stable joins across runs.

A seed sweep across `[42, 7, 123, 2024, 99, 1]` confirms Grade A+ on every
seed in this sample.

---

## Honest disclosure of sample-scale limitations

This is a **sample** product calibrated for ML prototyping and core-analysis
research, not for live drilling or completion decisions. A few notes:

1. **Permeability is heavy-tailed.** The lognormal Kozeny-Carman model
   produces realistic but right-skewed permeability distributions
   (sample p90 ≈ 1100 mD, median ≈ 0.3 mD). Use log-transformed permeability
   for statistical work and `np.log10(permeability_md + 1e-5)` for
   correlation analyses to match the φ-k coefficient reported in the
   scorecard.

2. **Basin / lithology coverage at sample scale** — at 500 cores, each
   basin has 29-79 cores. All 6 lithologies are present but tight_ss and
   dolomite are under-represented (~10% and ~6% of plugs respectively).
   Full product (25,000 cores) gives 2,000-4,000 cores per basin and
   converges all lithology distributions.

3. **2.8% controlled anomaly injection** is present in `plug_measurements`
   (`anomaly_flag` column) and `routine_core_analysis` (`anomaly_flag` column).
   This simulates stress-relief microfractures inflating permeability
   (plug level, 2-10× multipliers) and measurement repeatability artifacts
   (RCA helium porosity, ±1.5% noise). Use these flags as QC training
   targets or filter them out for clean regression baselines.

4. **Wettability index** is sampled with a global mean of -0.10 (mixed-to-
   slightly-oil-wet), not stratified by basin wettability prior. The
   v1.1 generator will introduce basin-stratified wettability sampling
   for tighter calibration.

---

## Full product

The **full OIL-006 dataset** ships at **25,000 cores** with **~3.5M plug
measurements**, full per-basin wettability stratification, basin-conditioned
TOC sampling, and complete petrophysics-SCAL-mineralogy-geomechanics
relational schema — licensed commercially. Contact XpertSystems.ai for
licensing terms.

📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**

---

## Citation

```bibtex
@dataset{xpertsystems_oil006_sample_2026,
  title  = {OIL-006: Synthetic Core Sample Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil006-sample}
}
```

## Generation details

- Generator version : 1.0.0
- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-21 22:43:24 UTC
- Cores             : 500
- Plugs             : 37,398
- Basins            : 10 (Permian Wolfcamp, Eagle Ford, Bakken, Marcellus,
                      North Sea Sandstone, GoM Deepwater, Middle East Carbonate,
                      Canadian Oil Sands, Pre-Salt Brazil, North Africa Carbonate)
- Lithologies       : 6 (clean SS, shaly SS, tight SS, shale, carbonate, dolomite)
- Calibration basis : API RP-40, SCA, SPWLA, Archie (1942), Anderson (1986),
                      Kozeny-Carman, Chang et al. (2006), SPE PEH
- Overall validation: 100.0/100 — Grade A+