README.md

---
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<n<100K
---

# OIL-044 — Synthetic Autonomous Oilfield Dataset (Sample)

A schema-identical preview of **OIL-044**, the XpertSystems.ai synthetic
**autonomous oilfield operations** dataset for autonomous robotics, drone
inspection, edge AI inference, remote operations, predictive maintenance,
and human-in-the-loop decision-support AI training. The full product covers
5,000 assets / 500,000 telemetry rows / 100,000 robotic operations / 50,000
edge AI decisions. This sample is HF-sized (500 assets, ~75K rows total)
covering all 8 product tables.

> **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).