Hugging Face's logo

Datasets:
xpertsystems
/
oil006-sample

Tasks:
Tabular Classification
Tabular Regression
Languages:
English
Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
core-analysis
petrophysics
rock-properties
License:
Dataset card Data Studio Files
xet
 Community
oil006-sample / README.md
pradeep-xpert's picture
pradeep-xpert
Initial release: OIL-006 sample, 500 cores / 37K plugs, Grade A+ (10/10)
73d296d verified
preview code
|
raw
history blame contribute delete
11.5 kB
metadata 
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 

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")

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 

@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+