xpertsystems/oil009-sample

OIL-009 — Synthetic Mud Logging Dataset (Sample)

SKU: OIL009-SAMPLE · Vertical: Oil & Gas / Upstream Formation Evaluation License: CC-BY-NC-4.0 (sample) · Schema version: oil009.v1 Generator version: 1.0-file1-simulation-engine · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise mud-logging dataset for real-time formation evaluation, gas chromatography ML, pore-pressure detection, and kick-risk monitoring. The sample covers 150 wells across 10 global basins with 217,275 depth-resolved mud-log records linked across 12 tables.

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What's in the box

File	Rows	Cols	Description
wells_master.csv	150	12	Well spine: basin, formation, rig, HPHT/sour/offshore flags, planned MW
formation_tops.csv	735	5	3-7 formation tops per well with picker confidence score
mud_log_timeseries.csv	21,639	9	Depth-resolved drilling mechanics: ROP, WOB, RPM, torque, SPP, flow
gas_readings.csv	21,639	10	Total gas units + C1-C5 chromatograph composition + H2S
lithology_intervals.csv	21,639	8	9-class lithology + carbonate/shale/sand fraction %
cuttings_analysis.csv	21,639	8	Grain size, sorting, fluorescence color & intensity, oil stain flag
drilling_events.csv	21,639	6	10-class event log (drilling break, kick precursor, lost circulation, etc.)
pore_pressure_indicators.csv	21,639	7	d-exponent, shale density, overpressure flag, pore pressure ppg equiv
mud_properties.csv	21,639	7	Mud weight, viscosity, chlorides, gas-cut mud flag
gas_chromatography.csv	21,639	7	Pixler ratios: wetness, balance, character + gas quality flag
cavings_analysis.csv	21,639	6	Cavings type (5-class) + wellbore instability score
drilling_labels.csv	21,639	7	ML labels: hydrocarbon show, kick risk, reservoir quality, lithology

Total: 217,275 rows across 12 CSVs, ~16.8 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: Pixler (1969) AAPG seminal hydrocarbon-ratio classification paper, Jorden & Shirley (1966) JPT d-exponent overpressure detection, IADC Mud Logging Standards, IADC Well Control Statistics, API RP-13B-1 drilling fluids, SPE 142884 (pore pressure detection methods), Schlumberger Mud Logging Field Manual, Halliburton Mud Logging guide, IHS Markit / Rystad Energy global wildcat database.

Sample run (seed 42, n_wells=150, depth_step=100 ft):

#	Metric	Observed	Target	Tolerance	Status	Source
1	avg total gas units	225.6205	200.0	±80.0	✓ PASS	IADC Mud Logging Standards + Schlumberger Mud Logging Field Manual — global mean background total gas units, mixed unconventional/conventional basin portfolio
2	avg methane pct	71.3605	72.0	±8.0	✓ PASS	Pixler (1969) AAPG — C1 fraction in mixed oil/gas/condensate global wildcat portfolio, dry-to-wet-gas transition zone
3	avg wetness ratio	11.8237	12.0	±4.0	✓ PASS	Pixler (1969) AAPG — wetness ratio (ΣC2-C5/ΣC1-C5×100), wet-gas / gas-condensate Pixler classification zone
4	avg balance ratio	9.9006	10.0	±5.0	✓ PASS	Pixler (1969) AAPG + Halliburton Mud Logging guide — balance ratio C1/(C2+C3) light-oil-to-wet-gas envelope
5	avg mud weight ppg	12.0585	11.5	±2.0	✓ PASS	API RP-13B-1 + SPE drilling fluids literature — global mean mud weight, mixed conventional/HPHT/deepwater portfolio
6	avg d exponent	1.3000	1.3	±0.3	✓ PASS	Wyllie + Jorden & Shirley (1966) JPT — corrected d-exponent normal-compaction shale baseline value (typically 1.0-1.5; decreasing trend indicates overpressure)
7	hydrocarbon show rate	0.0987	0.1	±0.05	✓ PASS	IHS Markit + Schlumberger wildcat database — fraction of drilled depth intervals exhibiting hydrocarbon shows (gas + fluorescence + reservoir lithology), global mixed exploration portfolio
8	kick risk rate	0.0102	0.012	±0.01	✓ PASS	IADC Well Control Statistics + SPE 142884 — fraction of drilled depth intervals showing kick precursor signatures (overpressure + elevated gas + mud-weight underbalance), global mud-logging dataset
9	lithology diversity entropy	0.7421	0.65	±0.1	✓ PASS	Global mud-logging literature — 9-class lithology diversity benchmark (shale, sandstone, siltstone, limestone, dolomite, marl, anhydrite, volcanic, coal); normalized Shannon entropy. Shale-dominant global mix produces a deliberately sub-uniform distribution
10	basin diversity entropy	0.9881	0.95	±0.05	✓ PASS	Rystad Energy + IHS Markit global mud-logging coverage — 10-class basin diversity benchmark (Permian, Eagle Ford, Bakken, Marcellus, GoM Deepwater, North Sea, Middle East, Brazil Pre-Salt, Canadian Oil Sands, Tight Gas Sandstone), normalized Shannon entropy

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

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Schema highlights

gas_chromatography.csv — the Pixler (1969) hydrocarbon ratio canonical formulation for mud-log gas typing:

wetness_ratio = (C2 + C3 + C4 + C5) / (C1 + C2 + C3 + C4 + C5) × 100 balance_ratio = C1 / (C2 + C3) character_ratio = (C4 + C5) / C3

These are the three ratios used by every commercial mud-logging service (Halliburton, SLB, Geoservices, Pason) to classify shows as dry gas / wet gas / condensate / oil. Sample wetness mean ~12 is in the wet-gas / oil- rich-gas Pixler zone (5-17.5); sample balance ~10 is in the light oil zone (1.5-100).

gas_readings.csv — basin-specific gas means with in-reservoir amplification (1.65×) and overpressure amplification (1.28×), plus log-normal noise. Background gas levels match the IADC mud-logging convention (50-500 units typical, >500 anomalous).

pore_pressure_indicators.csv — implements the Jorden & Shirley (1966) d-exponent overpressure detection method:

d = log(ROP/60·N) / log(12·WOB/10⁶·D) (corrected for mud weight)

Normal-compaction shale baseline is ~1.0-1.5; values decreasing with depth indicate undercompacted shales and impending overpressure. Sample mean d-exponent ~1.30 with downward deviations correlating with the overpressure_flag column.

lithology_intervals.csv — 9-class lithology (shale, sandstone, siltstone, limestone, dolomite, marl, anhydrite, volcanic, coal) drawn from basin-conditioned probability mixes. Shale dominates at 36% reflecting the modern unconventional-heavy global drilling portfolio.

drilling_events.csv — 10-class event taxonomy (normal drilling, drilling break, connection gas, trip gas, lost circulation, kick precursor, tight hole, differential sticking, sensor dropout, lag correction). Kick precursors gated by (overpressure + gas > 220 + mud-weight underbalance); drilling breaks gated by (hydrocarbon show + 38% draw rate).

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Suggested use cases

1. Pixler hydrocarbon-ratio classification ML — train classifiers on wetness / balance / character ratios → dry-gas / wet-gas / condensate / oil / no-show labels. Pixler crossplot zones are well-separated targets.
2. Lithology identification from gas + chromatograph — multi-class classifier on lithology_label (9-class) from drilling mechanics + gas composition + cuttings fluorescence features.
3. Kick-risk early warning — binary classifier on kick_risk_flag from upstream features (d-exponent decline, gas elevation, mud-weight underbalance). Sample has 1% positive rate matching IADC field statistics.
4. Pore-pressure regression — regress pore_pressure_ppg_equiv from d-exponent, shale density, depth, and drilling-mechanics features.
5. Hydrocarbon show detection — binary classifier on hydrocarbon_show_flag from gas + fluorescence + lithology features.
6. Reservoir quality grading — multi-class classifier on reservoir_quality (low/medium/high) from petrophysical and show-related features.
7. Drilling event classification — 10-class classifier on drilling_event_type from time-series drilling-mechanics features.
8. Multi-table relational ML — entity-resolution and graph-based learning across the 12 joinable tables via well_id and depth.

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Loading

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil009-sample", data_files="gas_readings.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
gas   = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/gas_readings.csv")
chr_  = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/gas_chromatography.csv")
lith  = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/lithology_intervals.csv")
lbl   = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/drilling_labels.csv")
joined = gas.merge(chr_, on=["well_id","depth_ft"]).merge(lbl, on=["well_id","depth_ft"])

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Reproducibility

All generation is deterministic via the integer seed parameter (driving random.Random(seed)). 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 mud-logging research, not for live drilling decisions. A few notes:

1. Long-tail lithology classes are under-represented at sample scale. Anhydrite (0.9%), volcanic (1.4%), and coal (~0.3%) are rare classes that appear only when their parent basins are drawn. Full product (18,000 wells) gives sufficient samples for these rare classes; the sample provides only handful-of-rows demonstrations of the schema.

2. All detail tables are co-resolved at the depth_step granularity (100 ft in the sample). Real mud-logging data has higher-frequency gas readings (1-5 ft intervals) and lower-frequency cuttings descriptions (5-30 ft intervals). The schema is the same; only the resolution differs. For high-frequency ML, use the full product with --depth-step-ft 5.

3. Anomaly injection rate is 3% (anomaly_injection_rate=0.03) — gas units randomly multiplied by [0.25, 0.45, 1.9, 2.8] to simulate sensor dropouts and lag corrections. These appear as outliers in gas_readings and can be filtered out via gas_chromatography.gas_quality_flag == 1.

4. Hydrocarbon show rate (10%) and kick risk rate (1%) match aggregate IADC field statistics but are not stratified by basin. Per-basin show rates in real data range from 2-3% (Marcellus dry gas) to 25-30% (Pre-Salt carbonate plays). Future generator v1.1 will introduce basin-conditioned show priors.

5. Mud-log timeseries uses mud_log_timeseries.csv as the canonical time-axis spine — all other tables (gas, lithology, cuttings, etc.) are indexed at the same depth grid for clean ML joins. This makes the tables more relational and less "time-series-y" than real MWD/LWD streams; treat the sample as depth-domain mud-log records, not time-domain telemetry.

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Full product

The full OIL-009 dataset ships at 18,000 wells with ~9M depth- resolved mud-log records, 5-ft default depth resolution, basin-conditioned hydrocarbon show priors, and per-basin chromatograph stratification — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

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Citation

@dataset{xpertsystems_oil009_sample_2026,
  title  = {OIL-009: Synthetic Mud Logging Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil009-sample}
}

Generation details

• Generator version : 1.0-file1-simulation-engine
• Sample version : 1.0.0
• Random seed : 42
• Generated : 2026-05-21 23:20:37 UTC
• Wells : 150
• Depth step : 100 ft
• Anomaly rate : 3.0%
• Basins : 10 (Permian, Eagle Ford, Bakken, Marcellus, GoM Deepwater, North Sea, Middle East Carbonate, Brazil Pre-Salt, Canadian Oil Sands, Tight Gas Sandstone)
• Lithologies : 9 (shale, sandstone, siltstone, limestone, dolomite, marl, anhydrite, volcanic, coal)
• Calibration basis : Pixler (1969), Jorden & Shirley (1966), IADC Mud Logging Standards, IADC Well Control Statistics, API RP-13B-1, SPE 142884, Schlumberger Mud Logging Field Manual, Halliburton Mud Logging guide
• Overall validation: 100.0/100 — Grade A+