| --- |
| 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+ |
| |
