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oil007-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Languages:
English
Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
drilling
drilling-parameters
mwd
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Initial release: OIL-007 sample, 100 wells / 110K timeseries rows, Grade A+ (10/10)
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - oil-and-gas
  - upstream
  - drilling
  - drilling-parameters
  - mwd
  - lwd
  - rop-optimization
  - mse
  - bit-wear
  - drilling-dysfunctions
  - xpertsystems
pretty_name: OIL-007 — Synthetic Drilling Parameters Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-007 — Synthetic Drilling Parameters Dataset (Sample)

SKU: OIL007-SAMPLE · Vertical: Oil & Gas / Upstream Drilling Operations License: CC-BY-NC-4.0 (sample) · Schema version: oil007.v1 Generator version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise drilling- parameters dataset for ROP optimization, MSE analysis, dysfunction detection, and drilling-process ML. The sample covers 100 wells across 11 global basins and 12 well types with 220,871 rows of high-cadence drilling telemetry linked across 9 tables.

What's in the box

File Rows Cols Description
wells_master.csv 100 13 Well spine: type, basin, trajectory, rig, mud system, casing
drilling_timeseries.csv 109,870 10 High-cadence per-stand ROP / WOB / torque / RPM / SPP / hook load
mud_properties.csv 21,974 10 Mud weight, PV, YP, ECD, gels, mud temp
hydraulics_log.csv 21,974 9 Flow, pump pressure, annular pressure, annular velocity, bit HHP
vibration_spectra.csv 54,935 8 Axial / lateral / torsional g's, stick-slip index, whirl index
mse_log.csv 10,987 6 Per-stand MSE (Teale formulation), bit efficiency, formation UCS
bit_wear_log.csv 344 9 IADC dull grades, footage, bit size/type (PDC/RC/hybrid)
drilling_events.csv 587 7 11-class dysfunction events (stick-slip, stuck pipe, washout, mud loss, etc.)
drilling_labels.csv 100 6 ML labels: optimal ROP, dysfunction risk, MSE efficiency, drilling grade

Total: 220,871 rows across 9 CSVs, ~14.0 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: SPE 178850, SPE 96652 (Dupriest & Koederitz), Teale (1965) MSE foundational paper, API RP-7G (drill stem design), API RP-13B-1 (drilling fluids), IADC Drilling Manual, IADC dull grading, Bourgoyne et al. (1986) Applied Drilling Engineering, Rystad Energy global rig fleet, and Spears & Associates bit market reports.

Sample run (seed 42, n_wells=100):

# Metric Observed Target Tolerance Status Source
1 avg rop ft hr 83.8562 85.0 ±25.0 ✓ PASS SPE 178850 + Rystad Energy — global mean ROP across mixed onshore/offshore land/platform portfolio
2 avg wob klbs 33.7322 32.0 ±8.0 ✓ PASS API RP-7G + SPE Drilling Handbook — global mean WOB across PDC/RC mixed bit portfolio
3 avg surface torque klbft 13.7738 14.5 ±4.0 ✓ PASS API RP-7G + SPE Drilling Engineering — global mean surface torque across mixed trajectory portfolio
4 avg mud weight ppg 11.4375 11.2 ±1.5 ✓ PASS API RP-13B-1 + SPE drilling fluids literature — global mean mud weight across conventional/HPHT/deepwater mix
5 ecd margin ppg 0.3709 0.45 ±0.15 ✓ PASS SPE 178850 + IADC Drilling Manual — ECD margin (ecd minus static MW) maintained during circulation
6 avg mse psi 37376.3374 38000.0 ±9000.0 ✓ PASS Teale (1965) + SPE 96652 (Dupriest & Koederitz) — global mean MSE across mixed-formation drilling portfolio
7 pv yp correlation 0.7833 0.78 ±0.15 ✓ PASS API RP-13B-1 + Bourgoyne et al. (1986) — plastic viscosity / yield point shared-rheology correlation in field-mixed mud systems (typically 0.70-0.85)
8 pdc bit share 0.7384 0.72 ±0.1 ✓ PASS Spears & Associates + IADC bit market reports — global PDC bit share in modern drilling (2020-2024)
9 mse ucs correlation 0.7453 0.65 ±0.2 ✓ PASS Teale (1965) + Dupriest (2005) SPE — MSE per-well average correlated with formation UCS; physics: MSE bounds approach UCS at perfect bit efficiency
10 well type diversity entropy 0.8279 0.85 ±0.15 ✓ PASS Rystad Energy global rig fleet + IADC drilling activity tracker — 12-class well-type diversity benchmark (conventional, HPHT, deepwater, ERD, multilateral, etc.), normalized Shannon entropy

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

Schema highlights

wells_master.csv — one row per well, the relational spine. Key columns: well_id, well_type (12-class: vertical_conventional, directional, horizontal_shale, extended_reach, deepwater_offshore, hpht, geothermal, managed_pressure, underbalanced, salt_section, multilateral, slim_hole), basin (11-class: Permian, Eagle Ford, Bakken, Marcellus-Utica, Gulf of Mexico, North Sea, Middle East, Brazil Pre-Salt, Canada Oil Sands, Geothermal Basins, Other), total_depth_ft, well_trajectory_type, mud_system (water/oil/synthetic/cesium formate).

MSE follows the Teale (1965) formulation with bit-size-aware area:

MSE = (WOB / A_bit) + (120·π·RPM·T_bit) / (A_bit · ROP)

where A_bit varies by hole section (17.5" surface, 12.25" intermediate, 8.5" production, 6.125" lateral), and T_bit is downhole bit torque estimated as 25-45% of surface torque depending on trajectory and depth — a critical detail for accurate MSE in long-reach and horizontal wells.

Formation strength profiles (mse_log.formation_strength_psi) follow basin-specific UCS bands with stochastic formation transitions every 500-2000 ft: Permian 8-22 kpsi, Marcellus-Utica 11-25 kpsi, Brazil Pre-Salt 15-35 kpsi, Geothermal Basins 18-40 kpsi.

Rheology coupling — PV and YP share an underlying rheology level correlated with mud weight, producing the field-realistic PV-YP correlation of ~0.70-0.85 (Bourgoyne et al. 1986).

ECD margin (ecd_ppg - mud_weight_ppg) is calibrated to maintain the ~0.45 ppg overpressure circulation envelope per IADC guidance, with depth-scaled annular friction and ROP-dependent cuttings loading.

Bit wear uses the IADC dull grading taxonomy (8 codes spanning worn-teeth, broken-teeth, lost-teeth, ring-out, balled-up patterns), with dull grade escalation driven by cumulative footage and average formation strength per bit run.

Suggested use cases

  1. ROP optimization regression — predict ROP from WOB, RPM, torque, mud weight, formation UCS using the 109,870-row time-series spine.
  2. MSE efficiency classification — train models on bit_efficiency to identify low-energy-efficient drilling sections.
  3. Dysfunction detection — multi-class classifier on dysfunction_class (11-class: stick-slip, lateral vibration, stuck pipe, washout, twist-off, mud loss/gain, bit balling, pack-off, whirl, axial bounce) from vibration
    • mechanical telemetry.
  4. Stuck pipe early warning — binary classification with the stuck_pipe_precursor events as positive labels, ROP/WOB/torque time- series as features.
  5. Bit dull grade prediction — regress IADC dull grade from cumulative footage and formation strength exposure per bit run.
  6. Drilling efficiency grading — multi-class classification on the drilling_efficiency_grade (A+/A/B/C/D) target from per-well aggregated features.
  7. Time-series forecasting — predict next-stand ROP / MSE / vibration from the prior stand's drilling parameters (sequence models, transformers).
  8. Multi-table relational ML — entity-resolution and graph-based learning across the 9 joinable tables via well_id.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil007-sample", data_files="drilling_timeseries.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
wells = pd.read_csv("hf://datasets/xpertsystems/oil007-sample/wells_master.csv")
ts    = pd.read_csv("hf://datasets/xpertsystems/oil007-sample/drilling_timeseries.csv")
mse   = pd.read_csv("hf://datasets/xpertsystems/oil007-sample/mse_log.csv")
events = pd.read_csv("hf://datasets/xpertsystems/oil007-sample/drilling_events.csv")
joined = ts.merge(wells, on="well_id")

Reproducibility

All generation is deterministic via the integer seed parameter through per-well SeedSequence([master_seed, well_idx]) derivation, guaranteeing schema-stable joins across runs and seed-by-seed reproducibility.

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 drilling- parameter research, not for live drilling operations. A few notes:

  1. Time-series is decimated 4× in the sample. Full product runs at the generator's native cadence (~42 samples per stand at 1 Hz / 85 ft/hr). The sample uses --decimate 4 to keep file sizes <15 MB while preserving stand-resolution detail. For high-frequency vibration ML, use the full product (12,000 wells, undecimated).

  2. ECD margin runs slightly below the 0.45 ppg target (observed mean ~0.37 ppg) at sample scale, well within the ±0.15 ppg tolerance. This reflects the stochastic depth-scaled annular friction modeling; full- product scale converges closer to 0.45 ppg as the basin/well-type mix averages out.

  3. Max depth capped at 12,000 ft in the sample (vs 22,000 ft in the full product). This caps the Pre-Salt and HPHT extreme-depth physics slightly under-represented. Wells assigned to those types still get their depth/mud-weight modifiers applied within the 12 kft envelope.

  4. Inter-stand jitter uses per-stand-seeded RNG for intra-stand sample variation. This produces stand-coherent telemetry (good for ML) but means within-stand noise is correlated stand-to-stand at fixed offsets. For pure-noise modeling, filter out the per-stand structure first.

  5. Per-well stuck pipe / washout / dysfunction event rates are per-stand Bernoulli probabilities, so total event counts per well are Poisson-distributed (~6 events/well average at sample scale). Production runs (longer wells, more stands) will see proportionally more events per well.

Full product

The full OIL-007 dataset ships at 12,000 wells, 22,000 ft max depth, undecimated 1Hz timeseries, full 12-class well-type coverage with HPHT and Pre-Salt extreme-depth physics, and full per-stand dysfunction event modeling — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil007_sample_2026,
  title  = {OIL-007: Synthetic Drilling Parameters Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil007-sample}
}

Generation details

  • Generator version : 1.0.0
  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-21 22:54:12 UTC
  • Wells : 100
  • Max depth : 12,000 ft (capped for sample; full product: 22,000 ft)
  • Timeseries decim. : 4× (sample); full product: 1× native
  • Basins : 11 (Permian, Eagle Ford, Bakken, Marcellus-Utica, Gulf of Mexico, North Sea, Middle East, Brazil Pre-Salt, Canada Oil Sands, Geothermal Basins, Other)
  • Well types : 12 (vertical, directional, horizontal_shale, extended_reach, deepwater, HPHT, geothermal, managed_pressure, underbalanced, salt_section, multilateral, slim_hole)
  • Calibration basis : SPE 178850, SPE 96652, Teale (1965), API RP-7G, API RP-13B-1, IADC Drilling Manual, IADC dull grading, Bourgoyne et al. (1986), Rystad Energy, Spears & Associates
  • Overall validation: 100.0/100 — Grade A+