--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - autonomous-systems - edge-ai - robotics - drone-inspection - remote-operations - oil-and-gas - autonomy-levels - nist-ai-rmf - iso-22989 - iso-10218 - iso-13482 - sae-j3016 - iec-62443 - human-oversight - human-in-the-loop - industrial-iot - ot-network - autonomous-decision pretty_name: "OIL-044 — Synthetic Autonomous Oilfield Dataset (Sample)" size_categories: - 10K **Built by** XpertSystems.ai — Synthetic Data Platform > **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai) > **License** CC-BY-NC-4.0 (sample); commercial license available for the full product. --- ## What OIL-044 does that nothing else in the catalog does OIL-044 is the catalog's **first autonomous-systems / edge AI** SKU. Where OIL-042 (Digital Twin) models steady-state operations and OIL-043 (Scenario Simulation) models perturbations, OIL-044 models the **autonomous decision-making layer** that sits on top: SAE-J3016-style autonomy-level assets, ISO 10218 / ISO 13482 robotic operations, drone inspection missions, edge AI inference with calibrated confidence and human-override flags, and remote-operator sessions across healthy / degraded / partitioned network conditions. This is the substrate that **autonomous-systems researchers, robotics SaaS vendors, edge AI platform teams, and human-AI interaction researchers** have been waiting for: a coherent, joinable dataset where robotic ops, drone inspections, edge AI decisions, and human overrides share asset_id and timestamps for cross-layer correlation research. | Buyer Persona | Use Case | |---|---| | Robotics SaaS Vendor | Robotic operations success modeling, fleet analytics | | Edge AI Platform Team | Confidence calibration, human-override-trigger learning | | Autonomous Systems Researcher | SAE J3016 autonomy-level performance benchmarking | | Human-AI Interaction Researcher | Override decision modeling, network-conditional autonomy | | Industrial IoT Vendor | OT network health × autonomous-decision correlation | | Drone Inspection Vendor | Anomaly detection rate calibration across drone types | | C-suite AI Demo | "Show me autonomous oilfield AI in 60 seconds" | --- ## What's inside 8 CSV tables organized around an `asset_id` master key: autonomous asset inventory → robotic operations → drone inspection missions → edge AI decisions → predictive maintenance → remote-operator sessions → sensor telemetry → pre-built ML labels. | Table | Rows (sample) | What it represents | |---|---:|---| | `autonomous_assets.csv` | 500 | 6-class asset taxonomy × 5-tier autonomy level × 3-state operational status | | `robotic_operations.csv` | 10,000 | 5-class robot × 6-class task × 3-class execution status + battery + failure prob | | `drone_missions.csv` | 3,000 | 3-class drone × mission type × flight duration + anomaly detected + collision risk | | `edge_ai_decisions.csv` | 6,000 | 5-class decision × confidence score + inference latency + human override flag | | `predictive_maintenance.csv` | 4,000 | Degradation + failure probability + remaining days + action taken | | `remote_control_sessions.csv` | 2,500 | Latency ms + 3-state network health + commands sent per session | | `equipment_telemetry.csv` | 50,000 | 5-class sensor (pressure/temp/flow/vibration/RPM) with calibrated value distributions | | `autonomous_labels.csv` | 500 | **Pre-built ML labels: autonomy risk score + intervention probability + 4-tier criticality** | Total: ~76,000 rows, ~9 MB. The full OIL-044 product is ~700K rows. --- ## Calibration sources Every distribution and ratio is anchored to **named public references**. Highlights: - **NIST AI-RMF 1.0 (NIST AI 100-1)** + **ISO/IEC TR 24028** — autonomous-AI confidence calibration conventions. - **ISO 22989** AI concepts and terminology — autonomous decision and human-oversight conventions. - **NIST SP 1011** Autonomy Levels for Unmanned Systems + **NIST Robotic Systems Test Methods** — autonomous-mission success benchmarks. - **ISO 10218** Industrial robots — failure-probability and safety norms. - **ISO 13482** Service robots — autonomous-machinery safety conventions. - **ISO 10816 / ISO 20816** Mechanical vibration evaluation — vibration sensor severity bands. - **IEC 62443** Industrial network security — OT network health KPIs. - **3GPP Industrial IoT KPIs** — remote-link availability conventions. - **SAE J3016** Levels of driving automation (5-level scale) — applied analogously here to industrial asset autonomy. - **AUVSI + FAA Part 107** + Oil & Gas drone-inspection survey data — 3-class drone taxonomy. - **ISA-95 / OPC UA** + **ISO 14224** — sensor classification. --- ## Validation scorecard The wrapper ships a 10-metric scorecard (`validation_scorecard.json`) that re-scores the dataset on every generation. Default seed 42 result: | ID | Metric | Target | Observed | Source | |---|---|---|---:|---| | M01 | Asset-Type Taxonomy (floor) | ≥ 6 | **6** | XpertSystems autonomous-oilfield | | M02 | Robot-Type Taxonomy (floor) | ≥ 5 | **5** | ISO 10218 / ISO 13482 / IADC | | M03 | Drone-Type Taxonomy (floor) | ≥ 3 | **3** | AUVSI / FAA Part 107 | | M04 | Edge AI Decision-Type (floor) | ≥ 5 | **5** | NIST AI-RMF / ISO 22989 | | M05 | Sensor-Type Taxonomy (floor) | ≥ 5 | **5** | ISA-95 / OPC UA / ISO 14224 | | M06 | Vibration Sensor Mean (mm/s) | 3.5–5.5 | **4.49** | ISO 10816 Class III | | M07 | Robotic Success Rate (floor) | ≥ 0.87 | **0.919** | NIST SP 1011 | | M08 | Network Healthy Rate (floor) | ≥ 0.87 | **0.909** | IEC 62443 / 3GPP IIoT | | M09 | Edge AI Confidence (mean) | 0.89–0.95 | **0.917** | NIST AI-RMF / ISO 24028 | | M10 | Human-Override Rate | 0.03–0.07 | **0.053** | ISO 22989 / NIST AI-RMF | **Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.** The scorecard intentionally focuses on **NIST AI-RMF / ISO 22989 calibration anchors** — autonomous-systems standards where the synthetic data must faithfully represent the standard's defined ranges to be useful for regulator-compliant decision-support AI training. --- ## Suggested use cases - **Human-override prediction modeling** — `edge_ai_decisions.csv` has per-decision `human_override_required` binary flag plus `confidence_score`, enabling training of override-trigger models for NIST AI-RMF Govern-1 human-oversight workflows. - **Confidence calibration research** — `edge_ai_decisions.csv` confidence distribution (mean 0.92, calibrated against NIST 100-1) is ground truth for calibration-error studies, Platt scaling, and isotonic regression benchmarking. - **Autonomous-mission success classification** — `robotic_operations.csv` 3-class status (SUCCESS / PARTIAL_SUCCESS / FAILED) × battery level × task duration × failure probability. Train mission-success classifiers with 92% positive class. - **Drone-inspection anomaly detection** — `drone_missions.csv` provides anomaly_detected binary flag × drone type × collision risk score, suitable for AUVSI/FAA-aligned inspection-quality ML. - **Network-conditional autonomy modeling** — `remote_control_sessions.csv` × `edge_ai_decisions.csv` joinable on asset_id supports network-aware human-AI handoff research (when does the link quality justify falling back to edge AI vs human operator). - **Telemetry-driven predictive maintenance** — `equipment_telemetry.csv` has calibrated normal distributions per sensor type, joinable with `predictive_maintenance.csv` for degradation modeling. - **Pre-built autonomy-risk ML labels** — `autonomous_labels.csv` provides asset-level `autonomy_risk_score`, `intervention_probability`, and 4-tier `criticality_level`, ready for downstream regression or multi-class classification. - **Cross-vertical autonomous-systems methodology** — OIL-044 schemas apply analogously to manufacturing, mining, ports, and warehouse autonomy research; buyers can use the same data plane for non-O&G autonomous research. --- ## Loading ```python from datasets import load_dataset assets = load_dataset( "xpertsystems/oil044-sample", data_files="autonomous_assets.csv", split="train", ) edge_ai = load_dataset( "xpertsystems/oil044-sample", data_files="edge_ai_decisions.csv", split="train", ) labels = load_dataset( "xpertsystems/oil044-sample", data_files="autonomous_labels.csv", split="train", ) ``` Or with pandas directly: ```python import pandas as pd from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="xpertsystems/oil044-sample", filename="robotic_operations.csv", repo_type="dataset", ) df = pd.read_csv(path) ``` All 8 tables share `asset_id` as the master join key, supporting cross-table joins: - `autonomous_assets ⨝ autonomous_labels` (1:1) — every asset has labels - `autonomous_assets ⨝ robotic_operations` (1:N) — ~20 operations per asset - `autonomous_assets ⨝ drone_missions` (1:N) — ~6 missions per asset - `autonomous_assets ⨝ edge_ai_decisions` (1:N) — ~12 decisions per asset - `autonomous_assets ⨝ equipment_telemetry` (1:N) — ~100 telemetry rows per asset --- ## Schema highlights **`autonomous_assets.csv`** — `asset_id`, `asset_type` (6-class: DRILLING_RIG / PIPELINE_STATION / REFINERY_UNIT / COMPRESSOR / LNG_TERMINAL / OFFSHORE_PLATFORM), `autonomy_level` ∈ {1, 2, 3, 4, 5} (SAE J3016-like 5-tier scale: 1=Driver Assistance, 5=Full Automation), `operational_status` ∈ {ACTIVE, STANDBY, MAINTENANCE}, `location_lat`, `location_lon`. **`robotic_operations.csv`** — `operation_id`, `robot_id`, `asset_id`, `robot_type` (5-class: INSPECTION_ROBOT / PIPE_CRAWLER / AUTONOMOUS_TRUCK / ROBOTIC_ARM / VALVE_CONTROLLER), `task_type` (6-class), `execution_status` ∈ {SUCCESS, FAILED, PARTIAL_SUCCESS}, `task_duration_minutes`, `battery_level`, `failure_probability`, `timestamp`. **`drone_missions.csv`** — `drone_type` (3-class: THERMAL_DRONE / VISUAL_INSPECTION_DRONE / LEAK_DETECTION_DRONE), `mission_type`, `flight_duration_minutes`, `anomaly_detected` (binary), `collision_risk_score`. **`edge_ai_decisions.csv`** — `decision_type` (5-class: SHUTDOWN / CONTINUE_OPERATION / ESCALATE / DISPATCH_ROBOT / REQUEST_HUMAN_OVERRIDE), `confidence_score` ∈ [0, 1] (calibrated to NIST AI-RMF norms), `inference_latency_ms`, `human_override_required` (binary), `timestamp`. **`remote_control_sessions.csv`** — `latency_ms` (calibrated to satellite + terrestrial Edge mix), `network_health` ∈ {HEALTHY, DEGRADED, PARTITIONED}, `commands_sent`, `timestamp`. **`equipment_telemetry.csv`** — 5-class `sensor_type` with calibrated normal distributions: - PRESSURE: mean 1,200 psi, σ 80 - TEMPERATURE: mean 85 °C, σ 12 - FLOW_RATE: mean 420 bpd, σ 55 - VIBRATION: mean 4.5 mm/s, σ 1.1 (ISO 10816) - RPM: mean 1,800 RPM, σ 250 **`autonomous_labels.csv`** — pre-built ML labels: `autonomy_risk_score` ∈ [0, 1], `intervention_probability` ∈ [0, 1], `criticality_level` ∈ {LOW, MEDIUM, HIGH, CRITICAL}. --- ## Calibration notes & limitations In the spirit of honest synthetic data, a few things buyers of the sample should know: 1. **Edge AI inference latency uses uniform random 10–3,000 ms with median ~1,500 ms.** This is **much slower than industry "edge AI" (<100 ms for real-time control)**. The synthetic distribution is intentionally wide for ML training utility — it covers the full range from on-asset edge inference (<100 ms) through fog computing (200–500 ms) through cloud-fallback (1–3 s). For pure on-device edge AI work, **filter to `inference_latency_ms < 100`** to recover a real-time-control subset. The scorecard validates confidence and override rates instead of latency for this reason. 2. **Predictive maintenance action distribution is uniform 25% each across NONE / INSPECTION / PART_REPLACEMENT / EMERGENCY_SHUTDOWN.** Industry mature operations sustain EMERGENCY_SHUTDOWN at ≤5%. This uniform distribution is intentional for ML training utility (balanced multi-class target). For realistic action-distribution work, **threshold from `degradation_score`** with custom mapping (e.g., NONE when score < 0.3, INSPECTION 0.3–0.5, PART_REPLACEMENT 0.5–0.7, EMERGENCY_SHUTDOWN > 0.7), or use the OIL-038 / OIL-039 PdM SKUs which carry calibrated action distributions. 3. **Operational status uniform across ACTIVE / STANDBY / MAINTENANCE (~33% each).** Industry mature is 70–85% ACTIVE. This is by design to give all 3 status classes equal ML training density at sample scale. 4. **Criticality level uniform across LOW / MEDIUM / HIGH / CRITICAL (~25% each).** Industry mature criticality distributions are pyramid- shaped (most LOW). The uniform distribution gives balanced multi-class training; for pyramid-shaped sampling, threshold from `autonomy_risk_score` directly. 5. **HF preview sizing** — default generator sizing is 5K assets / 500K telemetry / 100K robotic operations producing ~150 MB. The HF preview is reduced to 500 assets / 50K telemetry / 10K robotic operations, ~9 MB. All schemas, taxonomies, and scorecard calibrations are preserved at the smaller scale. For higher-density studies, override the underlying generator's `--n-assets` / `--n-telemetry-rows` flags. 6. **Sensor telemetry uses simple Gaussian per sensor type.** There's no cross-modal coupling, no temporal autocorrelation, no degradation trajectory linkage. For multi-modal anomaly detection with realistic covariance, use OIL-038 (16-modality with degradation trajectories) or OIL-039 (RUL prognostics with sigmoid-calibrated degradation). OIL-044 is optimized for **autonomous-decision and human-oversight research**, not multi-modal anomaly detection. 7. **Battery level uniform 5–100%.** Real fleet battery distributions are bimodal (charging stations + active duty). For realistic battery analytics, use the full product or condition on `execution_status` (low battery is correlated with FAILED status in the full generator). 8. **Deterministic seeding.** All 8 tables are deterministic on `--seed`. Catalog default is seed 42. Seed sweep verifies Grade A+ across {42, 7, 123, 2024, 99, 1}. --- ## Commercial / full product The full **OIL-044** product covers ~5,000 assets × 100,000 robotic operations × 50,000 edge AI decisions × 500,000 telemetry rows across a 1-year horizon (~700K rows total), with calibrated industry-pyramid distributions for operational status, criticality, and predictive maintenance actions, plus realistic battery / fleet bimodal distributions and edge-vs-cloud latency segmentation. Available under commercial license — contact [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai). XpertSystems.ai also publishes synthetic data products across Cybersecurity, Healthcare, Insurance & Risk, Materials & Energy, and Oil & Gas verticals. Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).