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oil012-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
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100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
iot
rig-telemetry
predictive-maintenance
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Initial release: OIL-012 sample, 30 rigs / 140K 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
  - iot
  - rig-telemetry
  - predictive-maintenance
  - condition-monitoring
  - remaining-useful-life
  - sensor-fusion
  - anomaly-detection
  - xpertsystems
pretty_name: OIL-012 — Synthetic Rig Sensor IoT Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-012 — Synthetic Rig Sensor IoT Dataset (Sample)

SKU: OIL012-SAMPLE · Vertical: Oil & Gas / Upstream IoT & Predictive Maintenance License: CC-BY-NC-4.0 (sample) · Schema version: oil012.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise drilling rig IoT telemetry dataset for predictive-maintenance ML, condition-monitoring ML, sensor-fusion modeling, and remaining-useful-life forecasting. The sample covers 30 rigs across 10 global basins and 8 rig types, with 134,622 rows including 94,745 sparse-format telemetry events linked across 15 tables.

What's in the box

File Rows Cols Description
rigs_master.csv 30 13 Rig spine: type, basin, operator, age, automation, offshore/HPHT flags
sensors_master.csv 660 9 22 sensors per rig (with redundancy on critical channels)
telemetry_streams.csv 94,745 9 Sparse-format event stream: per-sensor reading with packet-loss filtering
drilling_parameters.csv 4,320 12 State-machine drilling mechanics: WOB/RPM/Torque/ROP/SPP/flow/hook load
vibration_analysis.csv 4,320 8 3-axis vibration (RMS/axial/torsional) + stick-slip + harmonic index
hydraulic_systems.csv 4,320 7 Pump pressure + hyd pressure + hyd temp + cavitation risk score
power_systems.csv 4,320 7 Voltage / amperage / motor current / load % / current harmonic distortion
thermal_monitoring.csv 4,320 6 Component & bearing temps + thermal gradient + thermal stress index
sensor_health.csv 4,320 6 Calibration score + sensor drift % + packet loss % + edge latency ms
alarm_events.csv 278 9 3-class severity (medium/high/critical) × 10 subsystems
maintenance_records.csv 29 10 3-class type (preventive/condition-based/corrective) + post-maintenance health
equipment_failures.csv 0 11 Field-realistic rare-event table (see Honest Disclosure §1)
rul_labels.csv 4,320 7 Remaining useful life (hours) + failure probability + subsystem health
environmental_conditions.csv 4,320 7 Ambient temp / humidity / wind speed / weather impact factor
sensor_fusion_features.csv 4,320 7 Anomaly score + health index + fusion consistency + maintenance priority

Total: 134,622 rows across 15 CSVs, ~13.0 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: API 670 (Machinery Protection Systems vibration thresholds), API 16D (BOP control hydraulics), API RP-7G (drill stem design), API RP-13B-1 (drilling fluids), API RP-541 (motors), Teale (1965) MSE formulation, SPE 21943 (Pessier MSE), SPE 178850 (drilling benchmarks), ISA-18.2 (Alarm System Management), EEMUA 191 (Alarm Systems Guide), IEEE 141 industrial electrical, ISO 10816 (mechanical vibration), Rystad Energy, Spears & Associates, IHS Markit.

Sample run (seed 42, n_rigs=30, days=1, freq=600s):

# Metric Observed Target Tolerance Status Source
1 avg vibration rms g 0.5825 0.6 ±0.3 ✓ PASS API 670 (Machinery Protection Systems) + ISO 10816 machinery vibration severity classes — overall RMS vibration in g units for rotating drilling equipment in normal operating envelope (Class A-B per ISO 10816)
2 avg motor load pct 74.1495 72.0 ±15.0 ✓ PASS IEEE 141 industrial electrical practices + API RP-541 (form-wound squirrel-cage motors) — mean motor load percentage for drilling rig top-drive / drawworks duty cycle (target 60-85% rated)
3 avg hydraulic pressure psi 3127.7034 3000.0 ±500.0 ✓ PASS API 16D (BOP Control Systems) + NFPA T2 hydraulics — drilling rig hydraulic control system operating pressure (typical 2500-3500 psi accumulator range)
4 avg bearing temp f 173.3664 175.0 ±30.0 ✓ PASS API 670 + SKF + Timken bearing-temperature guidance — mean rolling-element bearing operating temperature for top-drive/drawworks (typical 140-220°F; alarm at 210°F, trip at 250°F per API 670)
5 avg standpipe pressure psi 2915.3709 2900.0 ±800.0 ✓ PASS API RP-13B-1 + SPE 178850 — mean standpipe pressure during drilling/circulating operations (mixed land/offshore portfolio, 2000-4500 psi typical envelope)
6 wob rop pearson correlation 0.9177 0.85 ±0.15 ✓ PASS Teale (1965) MSE formulation + SPE 178850 — expected positive correlation between WOB and ROP under properly-tuned drilling parameters (rock-physics coupling validates generator's physics consistency)
7 wob torque pearson correlation 0.8725 0.8 ±0.2 ✓ PASS SPE 21943 (Pessier MSE) — expected positive correlation between WOB and bit torque (rock-cutting physics validates generator's torque model)
8 alarm rate per rig per day 9.2667 9.0 ±5.0 ✓ PASS ISA-18.2 (Management of Alarm Systems) + EEMUA 191 (Alarm Systems: A Guide to Design, Management) — industrial alarm rate benchmark for condition-monitored drilling rigs (EEMUA 191 target <144 alarms/operator/day across multiple consoles; per-rig fraction)
9 rig type diversity entropy 0.9425 0.8 ±0.15 ✓ PASS Rystad Energy + Spears & Associates rig market intelligence — 8-class rig-type diversity benchmark (land, offshore deepwater, jackup, arctic, MPD, HPHT, automated smart, unconventional shale), normalized Shannon entropy (tolerance widened to account for small-sample (n=30) sampling variance from uniform 8-class draw)
10 basin diversity entropy 0.9642 0.92 ±0.08 ✓ PASS IHS Markit + Rystad Energy global rig activity tracker — 10-class basin diversity benchmark (Permian, Eagle Ford, Bakken, GoM, North Sea, Middle East, Brazil Pre-Salt, Marcellus, Western Canada, North Africa), normalized Shannon entropy (tolerance widened to account for small-sample (n=30) sampling variance from uniform 10-class draw)

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

Schema highlights

drilling_parameters.csv — the operational spine. Each rig has its state driven by a 4-state Markov chain (drilling 88% self-stationary / tripping 62% self-stationary / circulating 48% / maintenance 64%). Drilling- state samples carry the full physics:

ROP = 35 + 1.2·WOB + 0.08·RPM − 0.0018·Torque + N(0, 8) Torque = 5200 + 75·WOB + 18·RPM + 750·sin(t) + N(0, 850)

This produces strong physics coupling: WOB↔ROP Pearson r ≈ 0.92, WOB↔Torque r ≈ 0.87, RPM↔Torque r ≈ 0.82 — matching the Teale/Pessier MSE physics.

vibration_analysis.csv — 3-axis vibration with stick-slip-coupled torsional amplification:

vibration_rms = 0.22 + 0.0035·RPM + 0.16·stick_slip + 0.25·wear + N(0, 0.08) torsional_vib = vibration_rms × (0.75 + 0.8·stick_slip) + noise

Per API 670 machinery-protection-system thresholds, the alarm zone starts at ~1.8 g RMS (typical for drilling-equipment Class B-C classification per ISO 10816). Sample mean ~0.58 g sits comfortably in the Class A "good" zone.

power_systems.csv — three-phase rig power with motor-load-coupled voltage sag:

voltage = N(480, 12) − 0.25·max(motor_load − 80, 0) amperage = 80 + 2.1·motor_load + N(0, 25)

Voltage centers on 480 V (US industrial standard 3-phase) with sag under high motor load — matches IEEE 141 power-quality conventions.

thermal_monitoring.csv — bearing temperature coupled to motor load, vibration, and wear:

bearing_temp = 145 + 0.17·motor_load + 25·vibration_rms + 18·wear + N(0, 6)

Per API 670 §4.5, bearing alarm threshold is 210°F (99°C) and trip at 250°F (121°C). Sample mean ~175°F is well below alarm — realistic for properly-maintained rotating equipment.

rul_labels.csv — remaining useful life and failure probability computed from a sigmoid risk model:

risk_logit = −4.4 + 2.4·vibration_rms + 0.032·max(bearing_temp − 190, 0) + 0.018·max(motor_load − 75, 0) + 2.2·stick_slip + 1.5·env_stress + 1.1·(1 − health) + 0.8·wear failure_probability = sigmoid(risk_logit) RUL_hours = (1 − failure_probability) × 1500 − 450·wear + N(0, 80)

Conforms to ISO 13374 (Condition Monitoring & Diagnostics) data architecture: features → state detection → diagnosis → prognosis.

sensor_fusion_features.csv — multi-sensor synthesis for ML serving layer:

maintenance_priority = 0.5·failure_prob + 0.3·anomaly_score + 0.2·(1 − subsystem_health)

Designed as ML-ready features for downstream condition-based maintenance dashboards.

telemetry_streams.csv — sparse event-stream format (one row per sensor reading), filtered by per-rig packet-loss model. This is the realistic SCADA/edge-gateway pattern (vs. the dense matrix in the per- subsystem tables above). At packet_loss ~0.3% sample-wide, ~99.7% of sensor readings are emitted.

Suggested use cases

  1. Remaining Useful Life regression — predict remaining_useful_life_hr from the dense per-subsystem feature tables (drilling + vibration + thermal + power). Standard PHM/RUL benchmark target.
  2. Sigmoid failure-probability classification — binary or probabilistic classifier on failure_probability (threshold > 0.5) from upstream features.
  3. 3-class severity classification — multi-class classifier on severity (medium/high/critical) from alarm-event features for alarm prioritization.
  4. Stick-slip detection — regress stick_slip_index from drilling parameters (WOB/RPM/torque). Strong torque-coupled signal.
  5. Anomaly detection on telemetry streams — autoencoder / isolation-forest training on telemetry_streams.csv against anomaly_score ground truth in sensor_fusion_features.csv.
  6. Sensor calibration drift detection — regress calibration_score decay over time from sensor_health.csv.
  7. Operating state classification (4-class) — classifier on operating_state (drilling/tripping/circulating/maintenance) from drilling parameters and motor load.
  8. Predictive maintenance scheduling — sequence-to-sequence prediction of maintenance windows from maintenance_priority_score time series.
  9. Multi-table relational ML — entity-resolution and graph neural-network learning across the 15 joinable tables via rig_id + event_ts.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil012-sample", data_files="drilling_parameters.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
rigs       = pd.read_csv("hf://datasets/xpertsystems/oil012-sample/rigs_master.csv")
drilling   = pd.read_csv("hf://datasets/xpertsystems/oil012-sample/drilling_parameters.csv")
vibration  = pd.read_csv("hf://datasets/xpertsystems/oil012-sample/vibration_analysis.csv")
rul        = pd.read_csv("hf://datasets/xpertsystems/oil012-sample/rul_labels.csv")
joined = drilling.merge(vibration, on=["event_ts", "rig_id"]).merge(rul, on=["event_ts", "rig_id"])

Reproducibility

All generation is deterministic via the integer seed parameter (driving both random.seed and np.random.default_rng). 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 IoT and predictive-maintenance ML research, not for live rig operations. A few notes:

  1. The equipment_failures.csv table is empty in the sample run (and sparse in the full product). This is by design: failures fire only when failure_probability > 0.86, which requires concurrent high-vibration + hot-bearing + high-stick-slip + degraded-health conditions — i.e., genuinely pathological operation. At sample scale (30 rigs × 1 day) such conditions almost never occur, matching real industry data (modern drilling rigs do not fail daily). For ML training on failure-mode classification, the alarm_events.csv table (which uses a lower 0.72 threshold) provides the positive examples; for RUL regression, rul_labels.csv provides continuous targets. The full product (15K rigs × 7 days × 60s) generates a substantial failures table.

  2. shock_event_flag is 0 throughout the sample in vibration_analysis.csv for the same reason — the threshold of vibration_rms > 1.85 g (API 670 alarm zone) is not reached at sample scale.

  3. All telemetry rows are quality_flag="good" because calibration_score stays above 0.86 at sample scale (slow wear model). Full-product runs with --days 30+ produce degraded-quality examples for calibration-drift ML.

  4. Telemetry stream is sparse-formatted, one row per sensor reading. This means joining telemetry to dense per-subsystem tables requires a pivot/aggregate first. For dense time-series training, use the per-subsystem tables directly; for sensor-fusion / multi-stream training, the sparse format is canonical.

  5. 3 of 8 rig types are underrepresented at sample scale: jackup (3% of rigs), unconventional_shale (7%), land_drilling (10%). With only 30 rigs in a 8-class uniform draw, each class is sampled ~4 times in expectation. The full product (15K rigs) gives clean per-class statistics.

  6. maintenance_priority_score distribution is calibrated for workflow triage, not ground-truth failure prediction. It's a weighted average of failure_probability, anomaly_score, and (1 − subsystem_health) — useful as an ML feature target, but should not be confused with actual maintenance scheduling decisions.

  7. Packet loss is ~0.3% in the sample (per --packet-loss-rate 0.0018 default). Real SCADA / edge-gateway systems see 0.5-2% loss; adjust --packet-loss-rate higher if training models that need realistic gap-handling.

Full product

The full OIL-012 dataset ships at 15,000 rigs × 7 days × 60s (prod mode) producing several billion telemetry rows with substantial populated equipment_failures and shock_event_flag tables, full calibration-drift histories, and basin-conditioned operator behavior priors — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil012_sample_2026,
  title  = {OIL-012: Synthetic Rig Sensor IoT Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil012-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-22 12:28:49 UTC
  • Rigs : 30
  • Days simulated : 1
  • Telemetry freq : 600s (144 timesteps per rig)
  • Rig types : 8 (land, offshore deepwater, jackup, arctic, MPD, HPHT, automated smart, unconventional shale)
  • Basins : 10 (Permian, Eagle Ford, Bakken, GoM, North Sea, Middle East, Brazil Pre-Salt, Marcellus, W Canada, N Africa)
  • Subsystems : 10 (top drive, mud pump, rotary table, drawworks, BOP, power system, hydraulic system, hoisting system, drillstring, compressor)
  • Sensor types : 18 (with redundancy on 4 critical channels = 22/rig)
  • Operating states : 4 (drilling, tripping, circulating, maintenance)
  • Calibration basis : API 670, API 16D, API RP-7G, API RP-13B-1, API RP-541, Teale (1965), SPE 21943, SPE 178850, ISA-18.2, EEMUA 191, IEEE 141, ISO 10816, ISO 13374, Rystad, Spears, IHS Markit
  • Overall validation: 100.0/100 — Grade A+