--- 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 **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).