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oil010-sample

Tasks:
Tabular Classification
Tabular Regression
Languages:
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Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
drilling
drill-bits
bit-performance
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Initial release: OIL-010 sample, 3K bits / 228K rows, Grade A+ (10/10)
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - oil-and-gas
  - upstream
  - drilling
  - drill-bits
  - bit-performance
  - bit-wear
  - dysfunction-detection
  - mse-efficiency
  - hydraulic-performance
  - xpertsystems
pretty_name: OIL-010 — Synthetic Drill Bit Performance Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-010 — Synthetic Drill Bit Performance Dataset (Sample)

SKU: OIL010-SAMPLE · Vertical: Oil & Gas / Upstream Bit Performance License: CC-BY-NC-4.0 (sample) · Schema version: oil010.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise drill-bit performance dataset for bit-selection ML, dysfunction detection, wear prediction, and bit-economics analytics. The sample covers 3,000 bits across 4 bit types (PDC, Roller Cone, Hybrid, Diamond Impregnated) and 5 formation classes, with 15,000 drilling runs linked across 9 tables.

What's in the box

File Rows Cols Description
bits_master.csv 3,000 6 Bit catalog: type, manufacturer class, size, cutter & blade counts
drilling_runs.csv 15,000 8 Run spine: formation, depth interval, bit life, average ROP
drilling_parameters.csv 142,002 7 5-15 WOB/RPM/torque/ROP samples per run + MSE efficiency
bit_wear_logs.csv 15,000 6 IADC dull grades + cutter/bearing wear % + seal failure probability
vibration_measurements.csv 15,000 6 Axial / torsional / lateral g + whirl index per run
hydraulic_performance.csv 15,000 6 Flow, pressure drop, HSI, hydraulic efficiency
thermal_profiles.csv 15,000 5 Bottomhole / cutter temperature + thermal cycles
dysfunction_events.csv 5,204 5 ~35% of runs: 5-class dysfunction (stick-slip, whirl, bounce, balling, thermal overload)
drillbit_labels.csv 15,000 5 ML labels: ROP efficiency grade (A/B/C/D), optimal-bit flag, failure risk score

Total: 240,206 rows across 9 CSVs, ~13.6 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: SPE 21943 (Pessier MSE foundational paper), SPE 96652 (Dupriest & Koederitz MSE optimization), SPE 178850, SPE 178215, API RP-7G drill stem design, API RP-13D drilling-fluid hydraulics, ISO 13503-5, IADC dull grading taxonomy, IADC Drilling Manual, Spears & Associates bit market intelligence, Rystad Energy unconventional drilling analytics.

Sample run (seed 42, n_bits=3,000):

# Metric Observed Target Tolerance Status Source
1 avg rop fph 72.0094 72.0 ±18.0 ✓ PASS SPE 178850 + Rystad Energy unconventional drilling analytics — global mean ROP across mixed bit-type / formation portfolio
2 avg bit life hours 115.3312 115.0 ±30.0 ✓ PASS Spears & Associates bit market intelligence + IADC Drilling Manual — modern PDC/RC bit life expectancy across mixed formation portfolio
3 avg wob klbs 38.0037 38.0 ±10.0 ✓ PASS API RP-7G + SPE Drilling Engineering Handbook — global mean WOB across PDC/RC mixed bit portfolio
4 avg rpm 144.9325 145.0 ±30.0 ✓ PASS API RP-7G + SPE 178850 — global mean surface RPM across mixed top-drive / rotary-table drilling portfolio
5 avg hsi hp per in2 1.5986 1.6 ±0.5 ✓ PASS API RP-13D + ISO 13503-5 drilling-fluid hydraulics — global mean HSI (hydraulic horsepower per square inch of bit area) for modern PDC bits (target 1.5-3.0 HSI)
6 avg bottomhole temp f 244.8281 245.0 ±60.0 ✓ PASS API + SPE thermal-drilling literature — global mean bottomhole circulating temperature across mixed onshore/offshore/HPHT drilling portfolio
7 dysfunction event rate 0.3469 0.35 ±0.1 ✓ PASS SPE 178215 + IADC Drilling Manual — fraction of bit runs exhibiting at least one named dysfunction event (stick-slip, whirl, bounce, balling, thermal overload)
8 avg mse efficiency 0.8799 0.88 ±0.1 ✓ PASS SPE 21943 (Pessier MSE) + SPE 96652 (Dupriest & Koederitz) — mean MSE drilling efficiency factor (UCS / MSE ratio) under properly-trimmed drilling parameters
9 bit type diversity entropy 0.9997 0.95 ±0.05 ✓ PASS Spears & Associates + IADC bit market reports — 4-class bit-type diversity benchmark (PDC, Roller Cone, Hybrid, Diamond Impregnated), normalized Shannon entropy (uniform sampling expected for ML training portfolios)
10 formation diversity entropy 0.9999 0.95 ±0.05 ✓ PASS Rystad Energy global drilling activity tracker + IADC — 5-class formation diversity benchmark (Permian shale, carbonate, abrasive sandstone, deepwater turbidite, geothermal hard rock), normalized Shannon entropy

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

Schema highlights

bits_master.csv — the bit catalog spine, one row per bit. 4 bit types (PDC, Roller Cone, Hybrid, Diamond Impregnated), 3 manufacturer classes (Premium, Mid-Tier, Budget), bit size 6-18 inches, blade count 4-8, cutter count from N(45, 12).

drilling_runs.csv — 5 runs per bit on average. Each run carries formation context (5-class: Permian shale, carbonate, abrasive sandstone, deepwater turbidite, geothermal hard rock), depth interval, bit life in hours, and the average ROP for that run. The avg_rop_fph column is the seed for downstream drilling_parameters ROP samples (each per-stand ROP sample is N(run_avg_rop, 8)).

drilling_parameters.csv — 5-15 WOB/RPM/torque/ROP/MSE-efficiency samples per run. WOB N(38 klbs, 8), RPM N(145, 25), Torque N(18,500 ft-lb, 3,500), MSE efficiency N(0.88, 0.04).

bit_wear_logs.csv — IADC dull grade taxonomy (1-1-WT worn-teeth, 2-2-BT broken-teeth, 3-3-ER edge-rounded, 4-4-BH balled-up-with-heat), plus cutter wear % (N(46, 12)) and bearing wear % (N(35, 15)).

vibration_measurements.csv — three-axis g-force measurements matching modern downhole accelerometer ranges: axial N(1.8 g, 0.6), torsional N(2.1 g, 0.7), lateral N(1.5 g, 0.5), whirl index N(0.18, 0.04).

hydraulic_performance.csv — flow rate N(650 gpm, 120), pressure drop N(2,200 psi, 300), HSI (hydraulic horsepower per square inch of bit area) N(1.6 hp/in², 0.25), hydraulic efficiency N(0.91, 0.03).

thermal_profiles.csv — bottomhole temperature N(245°F, 30), cutter temperature N(380°F, 45), thermal cycles U(20, 400).

dysfunction_events.csv — sparse table, only ~35% of runs have a dysfunction event (per the 0.35 trigger probability). 5-class dysfunction taxonomy: stick-slip, bit whirl, bounce, balling, thermal overload. Severity 0-1, downtime 5-300 minutes.

drillbit_labels.csv — three ML targets:

  • rop_efficiency_grade (A/B/C/D) computed from avg_rop_fph / 72 thresholds (≥1.1=A, ≥0.95=B, ≥0.8=C, else D)
  • optimal_bit_flag (binary, 1 when efficiency > 1.0)
  • failure_risk_score (continuous, 0-1)

Suggested use cases

  1. Bit-type selection ML — multi-class classifier on bit_type from formation + depth + manufacturer-class features, trained against ROP-grade target.
  2. ROP-grade classification — train classifiers on rop_efficiency_grade (A/B/C/D) using drilling parameters, hydraulics, vibration, and bit spec features.
  3. Bit wear regression — predict cutter_wear_pct and bearing_wear_pct from run depth, formation, drilling parameters, and thermal exposure.
  4. Dysfunction detection — binary classifier on whether a run experiences a dysfunction event (join dysfunction_events to runs; ~35% positive rate). Then a 5-class secondary classifier on which dysfunction type given an event.
  5. MSE optimization — regress mse_efficiency from WOB, RPM, torque, ROP using the 142,002-row drilling-parameters spine (5-15 samples per run for distribution-aware training).
  6. Hydraulic efficiency prediction — predict hydraulic_efficiency from flow rate, pressure drop, HSI, and bit size.
  7. Thermal overload risk — binary classifier predicting thermal-overload dysfunction from BH temperature, cutter temperature, and thermal cycles.
  8. Multi-table relational ML — entity-resolution and graph-based learning across the 9 joinable tables via bit_id and run_id.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil010-sample", data_files="drilling_runs.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
bits    = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/bits_master.csv")
runs    = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/drilling_runs.csv")
params  = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/drilling_parameters.csv")
wear    = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/bit_wear_logs.csv")
joined  = runs.merge(bits, on="bit_id").merge(wear, on="run_id")

Reproducibility

All generation is deterministic via the integer seed parameter (driving np.random.seed). 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 bit- performance research, not for live bit-selection decisions. The generator is a direct-sampling design (independent Gaussian draws around industry-anchored target means) — fast to validate and easy to extend, but with several limitations users should know about:

  1. No cross-feature physics coupling. Each table is sampled independently of the others — WOB and torque are not correlated, vibration and dysfunction events are uncoupled, and thermal exposure does not drive wear progression. ML models trained on this sample will learn marginal distributions but will not learn realistic cross-feature relationships. The full OIL-010 product (v1.1 roadmap) will introduce physics coupling via shared latent factors (UCS-driven ROP, WOB-driven torque, thermal-driven cutter wear, vibration-driven dysfunction).

  2. Formation and dysfunction are uniformly sampled, not conditioned. In real drilling data, geothermal hard rock has very different dysfunction profiles (thermal overload dominant) than Permian shale (stick-slip dominant). The sample uses uniform 5-class draws for both; treat the joint distribution as ML-balanced rather than field-realistic.

  3. Three CSVs listed in the generator docstring are not generated in this version: lithology_transitions.csv, directional_performance.csv, and economics_metrics.csv. These will ship in OIL-010 v1.1. Current product is 9 CSVs.

  4. Long-tail cutter-count outliers. Cutter counts are drawn from N(45, 12), so a small fraction of bits (~0.1%) have unrealistically low cutter counts (<5). PDC bits in practice have 30-80 cutters; filter cutter_count >= 20 if you need clean PDC training data.

  5. MSE efficiency is sampled, not computed. The mse_efficiency column is a direct Gaussian draw N(0.88, 0.04), not derived from the Pessier MSE formulation (MSE = WOB/A + 120·π·N·T / (A·ROP)). For physically-consistent MSE labels, use OIL-007 (Drilling Parameters), which implements the full Teale/Pessier MSE physics with bit-size-aware area.

  6. All non-bit-master tables use uniform/Gaussian random IDs. The well_id field in drilling_runs.csv samples from a 50,000-well synthetic pool independently per run, so the same well will not typically appear in multiple runs in the sample. For ML that requires well-level grouping, the full product introduces realistic well clustering.

Full product

The full OIL-010 dataset ships at 5,000 bits / 25,000 runs, with the v1.1 roadmap adding cross-feature physics coupling, the three missing tables (lithology_transitions, directional_performance, economics_metrics), and basin-conditioned dysfunction priors — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil010_sample_2026,
  title  = {OIL-010: Synthetic Drill Bit Performance Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil010-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-21 23:33:12 UTC
  • Bits : 3,000
  • Runs : 15,000 (5 runs per bit on average)
  • Bit types : 4 (PDC, Roller Cone, Hybrid, Diamond Impregnated)
  • Formations : 5 (Permian shale, carbonate, abrasive sandstone, deepwater turbidite, geothermal hard rock)
  • Dysfunction types : 5 (stick-slip, bit whirl, bounce, balling, thermal overload)
  • Calibration basis : SPE 21943 (Pessier MSE), SPE 96652 (Dupriest), SPE 178850, SPE 178215, API RP-7G, API RP-13D, ISO 13503-5, IADC dull grading, IADC Drilling Manual, Spears & Associates, Rystad Energy
  • Overall validation: 100.0/100 — Grade A+