| ---
|
| 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+
|
| |
