| --- |
| 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 |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("xpertsystems/oil012-sample", data_files="drilling_parameters.csv") |
| print(ds["train"][0]) |
| ``` |
|
|
| Or with pandas: |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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+ |
| |
