xpertsystems/oil006-sample

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.

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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.

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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)

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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.

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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.

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Loading

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

Or with pandas:

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")

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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.

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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.

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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

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Citation

@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+