README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - scenario-simulation
  - what-if-analysis
  - decision-support
  - executive-ai
  - oil-and-gas
  - price-shock
  - operational-risk
  - supply-chain-disruption
  - cyberattack-scenarios
  - emergency-response
  - recovery-timeline
  - black-swan
  - ipieca
  - iea
  - eia
  - ccps
  - ics-cert
  - business-continuity
  - enterprise-risk
pretty_name: "OIL-043 — Synthetic Scenario Simulation Dataset (Sample)"
size_categories:
  - 100K<n<1M
---

# OIL-043 — Synthetic Scenario Simulation Dataset (Sample)

A schema-identical preview of **OIL-043**, the XpertSystems.ai synthetic
**what-if scenario simulation** dataset for oil & gas decision-support AI,
business-continuity modeling, enterprise risk management (ERM), and
executive-tier decision-support training. The full product covers 12,000
facilities × 250,000 scenarios across a 5-year horizon. This sample is the
generator's `sample` mode (750 facilities × 8,000 scenarios) covering all
12 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-043 does that nothing else in the catalog does

OIL-043 is the catalog's **first decision-support / what-if scenario** SKU.
Where OIL-042 (Digital Twin) models the *steady-state operations* of an
oilfield, OIL-043 models the **perturbations** to those operations —
price shocks, operational disruptions, equipment failure cascades, supply
chain interruptions, inventory stress, logistics constraints, cyberattacks,
emergency response, market recovery — each linked to a scenario_id with
pre-built ML labels (disruption probability, resilience score, financial
impact, decision priority).

This is **the substrate that ERM, business-continuity, and executive
decision-support AI teams have been waiting for**: a coherent, joinable
dataset where commodity shocks, OT cyber incidents, supply chain delays,
and equipment failure cascades can be modeled together with shared
severity, region, and decision-priority labels.

| Buyer Persona | Use Case |
|---|---|
| Chief Risk Officer / ERM | Enterprise risk scoring across 9 scenario types |
| Business Continuity Director | Recovery time estimation, escalation modeling |
| C-suite Decision Support AI | Executive priority labels (low/medium/high/critical) |
| CISO / OT Security | ICS attack impact on operations (SCADA availability) |
| Strategic Planning / S&OP | Multi-scenario portfolio stress testing |
| Insurance / Reinsurance | Loss-severity distribution modeling for upstream |

---

## What's inside

12 CSV tables organized around a `scenario_id` master key: scenario master
→ price shocks → operational disruptions → equipment failure chains →
production impacts → supply chain interruptions → inventory depletion →
logistics constraints → cyberattack scenarios → emergency response → market
recovery timelines → pre-built ML labels.

| Table | Rows (sample) | What it represents |
|---|---:|---|
| `scenario_master.csv` | 8,000 | 9-class scenario type × 4-class severity × facility/region/duration |
| `price_shock_events.csv` | ~12,000 | 7-commodity panel: WTI, Brent, HenryHubGas, Diesel, Gasoline, LNG_JKM, FuelOil |
| `operational_disruptions.csv` | ~26,000 | 6-class disruption × 8-class root cause × throughput loss + downtime |
| `equipment_failure_chains.csv` | ~19,000 | 8-class asset × 8-class failure mode × cascade level + spare availability |
| `production_impacts.csv` | 8,000 | Lost volume boe + revenue loss + ramp-down/up hours per scenario |
| `supply_chain_interruptions.csv` | ~15,000 | Route disruption with cost-increase + rerouting + supplier risk |
| `inventory_depletion.csv` | 8,000 | 4-class stress level × depletion rate × days-to-stockout |
| `logistics_constraints.csv` | ~4,500 | 5-class transport mode × congestion + demurrage cost |
| `cyberattack_scenarios.csv` | ~1,100 | 5-class ICS attack × SCADA availability + manual operation flag |
| `emergency_response.csv` | ~5,600 | 4-level escalation (site/regional/corporate/regulatory) + IC + exec brief |
| `market_recovery_timelines.csv` | 8,000 | Stabilization + full-recovery days + residual risk + lessons-learned |
| `scenario_labels.csv` | 8,000 | **Pre-built ML labels: disruption prob + resilience + financial impact + decision priority** |

Total: ~123,000 rows, ~12 MB. The full OIL-043 product is ~4 million rows.

---

## Calibration sources

Every distribution and ratio is anchored to **named public references**.
Highlights:

- **IPIECA Operating Risk Framework + IEA Black-Swan Scenario Library** —
  scenario severity and rare-event distributions.
- **IEA / EIA / S&P Platts** commodity reference panels — 7-commodity
  price-shock taxonomy.
- **ISO 14224:2016 + API RP 691** — rotating equipment failure-mode
  taxonomy.
- **CCPS Bow-Tie + LOPA** cascade analysis — equipment failure cascade
  depth ranges.
- **ICS-CERT + NIST SP 800-82** — ICS/OT incident-impact SCADA-availability
  degradation bands.
- **EIA / API midstream statistics** — pipeline transport-mode share.
- **IEA Energy Transport Network** — 5-class logistics transport-mode
  taxonomy.
- **OECD / IEA Scenario Recovery** — disruption-event recovery timelines.
- **CCPS Root-Cause Analysis + ASSE/ASSP** — lessons-learned and corrective
  action norms.

---

## 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 | Scenario-Type Taxonomy (floor) | ≥ 9 | **9** | IPIECA / IEA |
| M02 | Commodity Panel Coverage (floor) | ≥ 7 | **7** | IEA / EIA / Platts |
| M03 | Failure-Mode Taxonomy (floor) | ≥ 8 | **8** | ISO 14224 / API RP 691 |
| M04 | Critical-Severity Scenario Share | 0.04–0.08 | **0.067** | IPIECA Operating Risk |
| M05 | Cascade Level (mean) | 1.5–3.5 | **2.41** | CCPS Bow-Tie / LOPA |
| M06 | Cyber-Active SCADA Availability % | 55–85 | **72.9** | ICS-CERT / NIST 800-82 |
| M07 | Transport-Mode Taxonomy (floor) | ≥ 5 | **5** | IEA Energy Transport |
| M08 | Pipeline Transport Share | 0.30–0.50 | **0.38** | EIA / API midstream |
| M09 | Full Recovery Days (median) | 0–60 | **21.2** | OECD / IEA Scenario |
| M10 | Lessons Learned (mean) | 3–7 | **4.97** | CCPS RCA / ASSE |

**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**

---

## Suggested use cases

- **Decision-support AI training** — `scenario_labels.csv` provides 4-class
  decision priority labels (low / medium / high / critical) plus a binary
  `model_label` calibrated against disruption probability + financial
  impact. Train executive priority-classification models with ~27% positive
  class density.
- **Enterprise risk scoring (ERM)** — `disruption_probability`,
  `resilience_score`, `financial_impact_score`, and `operational_risk_score`
  are per-scenario continuous-valued ML targets. Train regression models
  for portfolio-wide risk scoring.
- **Multi-modal scenario impact modeling** — join across all 11 event
  tables on `scenario_id` to train models that predict downstream impact
  (production loss, recovery time) from upstream signals (price shock,
  cyber event, equipment failure).
- **Cascading failure modeling** — `equipment_failure_chains.csv` has
  `cascade_level` (1–6) for upstream → downstream failure propagation.
  Train graph-neural-network or Bow-Tie analysis models.
- **Cyber-physical impact estimation** — `cyberattack_scenarios.csv` ×
  `operational_disruptions.csv` × `production_impacts.csv` enable
  Industroyer / TRITON / Colonial Pipeline-class incident impact modeling.
- **Supply chain stress testing** — scenario portfolios with linked
  inventory depletion + logistics constraints + cost increase enable
  multi-tier supply-chain network resilience modeling.
- **Black-swan rare-event modeling** — `is_rare_event` flag identifies
  critical-severity scenarios with explicit rare-event injection.
- **Cross-vertical scenario validation** — the 9-class scenario taxonomy
  applies analogously to other XpertSystems verticals (Insurance,
  Healthcare, Cybersecurity); buyers can use OIL-043 as the framework for
  building their own scenario libraries.

---

## Loading

```python
from datasets import load_dataset

scenarios = load_dataset(
    "xpertsystems/oil043-sample",
    data_files="scenario_master.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil043-sample",
    data_files="scenario_labels.csv",
    split="train",
)
disruptions = load_dataset(
    "xpertsystems/oil043-sample",
    data_files="operational_disruptions.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/oil043-sample",
    filename="market_recovery_timelines.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)
```

All 12 tables share `scenario_id` as the master join key. Most tables also
carry `facility_id` for cross-cutting joins. Aggregation patterns:

- `scenario_master ⨝ scenario_labels` (1:1) — every scenario has labels
- `scenario_master ⨝ operational_disruptions` (1:N) — multiple disruptions per scenario
- `scenario_master ⨝ equipment_failure_chains` (1:N) — failure cascades
- `scenario_master ⨝ cyberattack_scenarios` (1:0–1) — cyber-only scenarios
- `scenario_master ⨝ market_recovery_timelines` (1:1) — every scenario has recovery

---

## Schema highlights

**`scenario_master.csv`** — `scenario_id`, `facility_id`, `scenario_type`
(9-class: price_shock / equipment_failure / operational_disruption /
supply_chain_interruption / inventory_stress / cyberattack /
weather_disruption / geopolitical_event / regulatory_shutdown),
`severity_level` ∈ {low, medium, high, critical}, `region` (8-class),
`facility_type` (8-class), `start_timestamp`, `duration_hours`,
`is_rare_event`, `dependency_count`, `baseline_capacity_boe_per_day`,
`scenario_complexity_score` ∈ [0, 1].

**`price_shock_events.csv`** — `commodity` (7-class IEA/EIA panel),
`shock_direction` ∈ {up, down}, `shock_magnitude_pct`,
`volatility_regime` ∈ {normal, elevated, stressed, crisis},
`spread_impact_bps`, `mean_reversion_days`.

**`equipment_failure_chains.csv`** — `asset_type` (8-class:
compressor / pump / valve / pipeline_segment / turbine / heat_exchanger /
storage_tank / separator), `failure_mode` (8-class ISO 14224),
`cascade_level` ∈ {1, …, 6} (CCPS Bow-Tie), `mtbf_hours_before_failure`,
`estimated_repair_hours`, `spare_part_available` (links to OIL-041
spare-parts demand), `failure_probability`.

**`cyberattack_scenarios.csv`** — `attack_type` (5-class:
scada_lockout / ransomware / sensor_spoofing / data_exfiltration /
network_segmentation_failure), `ot_network_impact_score`,
`scada_availability_pct`, `manual_operation_required`,
`containment_hours`, `estimated_cyber_loss_usd`.

**`scenario_labels.csv`** — pre-built ML labels:
`disruption_probability` ∈ [0, 1], `resilience_score` ∈ [0, 1],
`financial_impact_score` ∈ [0, 1], `operational_risk_score` ∈ [0, 1],
`recommended_decision_priority` ∈ {low, medium, high, critical},
`requires_executive_action` (binary), `model_label` (binary,
high+critical = 1).

---

## Calibration notes & limitations

In the spirit of honest synthetic data, a few things buyers of the sample
should know:

1. **Throughput loss median is 33% — well above industry-mature 5–15%.**
   The `operational_disruptions.csv` table is biased toward stressed-scenario
   training utility: throughput losses are sampled as `0.08 + sev × 0.55`
   plus noise. The dataset is designed to give ML models trainable
   positive-class density for *severe* scenarios, not to estimate routine
   operations. For routine-disruption analytics, filter to
   `severity_level == 'low'` (33% of records) to recover median throughput
   loss ~10%.

2. **SCADA availability ~73% on cyber-active scenarios.** This is the
   *conditional* availability *during* an active cyber incident — not the
   steady-state SCADA quality (which is ~99.9% in OIL-042's
   `scada_telemetry.csv`). The 73% figure is anchored to ICS-CERT incident
   reports (55–85% degradation band) and is the metric of interest for
   cyber-impact modeling.

3. **Critical severity rate 6.7%, rare event flag 4.8%.** The
   `is_rare_event` flag is **stricter** than `severity_level == 'critical'`
   — it fires only when `severity == 'critical' AND random < 0.72`. This
   models the IPIECA distinction between "high-severity scenario" (any
   crit) and "tail-risk / black-swan" (truly novel + catastrophic). Use
   `is_rare_event` for black-swan modeling, `severity_level == 'critical'`
   for general high-severity work.

4. **Cyber-attack scenarios are sparse (~1,100 rows).** Calibrated to
   IPIECA's cyber-attack base rate of ~6% of scenarios (with
   `cyberattack_probability` config flag). For dense cyber-attack ML
   training, use the full product (`prod` mode → ~34,000 cyber
   scenarios) or oversample with weights from `attack_type`.

5. **Logistics constraints sparse (~4,500 rows).** Only fires on
   supply_chain / weather / geopolitical scenarios + 40% random others.
   For dense logistics ML, filter to those 3 scenario types directly.

6. **Spare-part availability ~72%, not OIL-041's industry-mature 85%+.**
   In OIL-043, spare availability is **conditional on stressed scenarios**
   — it degrades as severity increases by design. Use OIL-041 for
   steady-state spare-parts inventory analytics; use OIL-043 for crisis-
   scenario spare-parts unavailability modeling.

7. **Equipment failure mode taxonomy is 8-class** here, vs OIL-038's
   10-class generator and OIL-042's 10-class. The 8 modes are a subset
   (the 2 dropped: `wax_deposition`, `scale_blockage` — which are more
   process-side than mechanical). Cross-SKU joins on `failure_mode` may
   need value normalization.

8. **Operational disruption types: 6-class.** Smaller than the 18-class
   OIL-038 failure modes — by design (operational disruptions are at the
   *event level*, not the *mechanical mode level*).

9. **Deterministic seeding.** All 12 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-043** product covers ~12,000 facilities × ~250,000
scenarios across a 5-year horizon (~4 million rows total), with dense
coverage of all categorical taxonomies including the rare cyber-attack
scenarios (~34,000), heavy-tail black-swan injection at IPIECA-specified
rates, and configurable scenario-portfolio composition for industry-
specific stress testing. 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).