xpertsystems/oil046-sample

OIL-046 — Synthetic Training Simulation Dataset (Sample)

A schema-identical preview of OIL-046, the XpertSystems.ai synthetic VR-based operator training simulation dataset for upstream + offshore + refinery oil & gas operations. The full product covers ~350,000 trainees × ~8,500 facilities × ~85 million sessions across a 5-year horizon. This sample is HF-sized (500 trainees × 30 facilities × 2,500 sessions × 15,000 VR movements) covering all 13 product tables.

Built by XpertSystems.ai — Synthetic Data Platform Contact pradeep@xpertsystems.ai · xpertsystems.ai License CC-BY-NC-4.0 (sample); commercial license available for the full product.

---

What OIL-046 does that nothing else in the catalog does

OIL-046 is the catalog's first VR / immersive training simulation SKU. Where OIL-035 (Safety / HSE) models incidents after the fact and OIL-045 (Workforce) models scheduling and fatigue, OIL-046 models the training data that determines whether operators are competent to handle those incidents when they occur. This is the substrate underneath every other safety-related dataset in the catalog.

This is the substrate VR training platform vendors, operator competency analytics teams, simulator fidelity researchers, OPITO/IADC training auditors, and human-performance modelers have been waiting for: a coherent, joinable dataset where VR sessions, equipment interactions, alarm acknowledgments, communication chains, evacuations, safety violations, fatigue, and incident progression all share session_id and trainee_id for cross-modal training analytics.

Buyer Persona	Use Case
VR Training Platform Vendor	Simulator fidelity validation, scenario completion analytics
Operator Competency Analytics	Pass/fail prediction, retraining recommendation models
Simulator Fidelity Researcher	VR realism scoring, hardware profile impact
OPITO/IADC Training Auditor	Compliance reporting + competency benchmarking
Human-Performance Modeler	Fatigue-in-training × decision quality × stress correlation
HSE Training Director	Drill effectiveness + violation pattern detection
Insurance Underwriter	Training-quality risk pricing for upstream operators

---

What's inside

13 CSV tables organized around session_id / trainee_id / facility_id join keys: facility master → trainee master → training sessions → VR movements (3D position + head rotation) → equipment interactions → alarm events → emergency response → communication logs → evacuation sequences → safety violations → fatigue profiles → incident progression → pre-built ML training labels.

Table	Rows (sample)	What it represents
facility_master.csv	30	10-class facility × 10-region × VR environment version + operational complexity
trainee_master.csv	500	8-class role × skill level × certifications + fatigue + stress susceptibility
training_sessions.csv	2,500	20-class scenario × severity × fatigue × stress × completion score + grade
vr_movements.csv	15,000	3D position (x, y, z) + head rotation (yaw, pitch) + hazard proximity + collision flag
equipment_interactions.csv	~29,000	15 equipment types × 15 interaction types with correct-action flag + quality score
emergency_response.csv	~14,800	Multi-step response workflows with delay + success + containment status
alarm_events.csv	~12,800	ISA 18.2-aligned alarm priority + acknowledgment time + alarm flood flag
communication_logs.csv	~16,200	Communication type × clarity score × failure flag × command chain level
evacuation_sequences.csv	~760	Route × muster point × expected vs actual completion time
safety_violations.csv	~810	10-class violation × severity × correction × coach intervention
fatigue_profiles.csv	~7,500	Per-session × 3-stage fatigue + reaction delay + cognitive load
incident_progression.csv	~11,900	Cascade staging × escalation probability × stabilization probability
ai_training_labels.csv	2,500	Pre-built ML labels: 8 columns spanning hazard prob + response grade + containment + retraining flag + VR realism

Total: ~115,000 rows, ~14 MB. The full OIL-046 product is ~85 million sessions and ~950 million VR movement records.

---

Calibration sources

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

• IPIECA Competency Framework — upstream operator competency classification and scenario taxonomy.
• IOGP Process Safety Fundamentals — facility classification and scenario severity bands.
• IADC Well Control + WellCAP — well-control training scenario taxonomy.
• NFPA 1006 Technical Rescue Personnel Professional Qualifications — emergency responder training standards.
• OPITO Offshore Petroleum Industry Training Organization — VR- augmented offshore training requirements + session duration norms.
• CCPS Process Safety + LOPA — containment success benchmarks and rare-event drill scheduling.
• NEBOSH International General Certificate — safety violation taxonomy.
• ISA 18.2 / EEMUA 191 — alarm priority bands and acknowledgment conventions.
• UK HSE OHRA + API RP 755 — fatigue management applied to training environments.
• DNV-RP-A203 simulator validation + emerging VR training-fidelity standards — realism scoring conventions.
• ISO 14224:2016 — equipment classification compatible taxonomy.

---

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	Facility-Type Taxonomy (floor)	≥ 10	10	IPIECA / IOGP
M02	Scenario Taxonomy (floor)	≥ 20	20	IADC / IPIECA / NFPA 1006
M03	Equipment Taxonomy (floor)	≥ 15	15	ISO 14224 / IADC
M04	Violation Taxonomy (floor)	≥ 10	10	NEBOSH / CCPS
M05	Session Duration Median (min)	30–90	65	OPITO / IADC
M06	Containment Success Rate	0.65–0.85	0.727	IPIECA / CCPS LOPA
M07	Fatigue Exceedance Share	0.06–0.18	0.110	UK HSE OHRA / API RP 755
M08	VR Realism Score (mean, floor)	≥ 0.87	0.918	DNV-RP-A203 / OPITO
M09	Rare-Event Label Rate	0.005–0.045	0.022	IPIECA / CCPS
M10	Response Grade (mean)	0.45–0.75	0.582	IPIECA / IADC

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

Note: 6 of 10 metrics fall within 5% of target midpoint, and all 4 floor metrics deliver complete taxonomy coverage at sample scale. The scorecard is anchored to 11 distinct training-industry standards spanning IADC, IPIECA, OPITO, NFPA, CCPS, NEBOSH, ISA, UK HSE, API, DNV, and ISO — the deepest standards-anchoring of any SKU in the catalog.

---

Suggested use cases

• Pass/fail prediction — pre-built training_pass_label in ai_training_labels.csv enables binary classifier training for competency assessment.
• Recommended retraining detection — recommended_retraining_flag
  • response_grade_score × scenario_type supports retraining-recommender model training.
• VR realism × performance correlation — vr_realism_score per session × procedural_accuracy × pass_label enables simulator- fidelity ROI studies.
• Multi-modal training event prediction — join alarm_events + communication_logs + equipment_interactions + vr_movements on session_id to train multi-modal trainee-behavior models.
• Fatigue-in-training analytics — fatigue_profiles 3-stage scoring × session severity × procedural accuracy enables fatigue-aware training scheduling models.
• Cascade-failure response training — incident_progression.csv cascade staging × escalation probability × emergency response actions enables Bow-Tie / LOPA training-effectiveness modeling.
• Equipment-interaction quality scoring — per-interaction correct_action_flag × actual_response_time_sec vs expected_response_time_sec enables interaction-quality ML.
• Evacuation timing prediction — evacuation_sequences.csv expected vs actual completion time + route clear flag enables evacuation effectiveness modeling.
• Cross-vertical immersive-training methodology — the OIL-046 generator architecture (20 scenarios × 15 equipment × VR movements × labels) ports directly to Aviation, Maritime, Healthcare, Defense, Mining, and Manufacturing VR training research.

---

Loading

from datasets import load_dataset

trainees = load_dataset(
    "xpertsystems/oil046-sample",
    data_files="trainee_master.csv",
    split="train",
)
sessions = load_dataset(
    "xpertsystems/oil046-sample",
    data_files="training_sessions.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil046-sample",
    data_files="ai_training_labels.csv",
    split="train",
)

Or with pandas directly:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/oil046-sample",
    filename="vr_movements.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

All 13 tables share these primary join keys:

• trainee_id → trainee_master ↔ sessions ↔ vr_movements ↔ equipment_interactions ↔ emergency_response ↔ communication_logs ↔ evacuations ↔ violations ↔ fatigue ↔ labels
• facility_id → facility_master ↔ trainee_master (home) ↔ sessions ↔ alarms ↔ evacuations ↔ labels
• session_id → sessions ↔ all event tables ↔ labels (1:1 or 1:N alignment)
• incident_id → emergency_response ↔ incident_progression (1:N cascade staging)

---

Schema highlights

training_sessions.csv — session_id, trainee_id, facility_id, scenario_type (20-class), severity_level ∈ {low, medium, high, critical}, severity_score, rare_event_flag, fatigue_score, stress_score, mean_response_time_sec, procedural_accuracy, communication_failure_flag, safety_violation_flag, containment_success_flag, completion_score, training_grade, ai_assist_enabled, vr_hardware_profile.

vr_movements.csv — movement_id, session_id, trainee_id, timestamp, position_x, position_y, position_z, movement_vector, head_rotation_yaw, head_rotation_pitch, proximity_to_hazard_m, collision_or_trip_flag, safe_zone_flag.

equipment_interactions.csv — interaction_id, session_id, trainee_id, equipment_id, equipment_type (15-class), interaction_type (15-class), expected_response_time_sec, actual_response_time_sec, correct_action_flag, manual_override_flag, equipment_state_before/after, interaction_quality_score.

alarm_events.csv — alarm_id, session_id, facility_id, alarm_type, severity_level (ISA 18.2), acknowledged_flag, acknowledgment_time_sec, alarm_flood_flag, false_alarm_flag.

safety_violations.csv — violation_type (10-class: wrong_valve_sequence, incorrect_ppe, missed_loto_step, incomplete_permit_check, delayed_alarm_acknowledgment, failed_communication_protocol, missed_gas_test, unauthorized_override,

• 2 more), procedure_breached, severity, coach_intervention_required_flag, repeat_violation_flag.

ai_training_labels.csv — pre-built ML labels: hazard_probability ∈ [0, 1], response_grade_score ∈ [0, 1], operator_error_probability ∈ [0, 1], containment_success_label (binary), emergency_escalation_label (binary), rare_event_label (binary), training_pass_label (binary), vr_realism_score ∈ [0, 1], recommended_retraining_flag (binary).

---

Calibration notes & limitations

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

1. Training pass rate is ~14% — much lower than industry-mature 60–80%. The generator's training_pass_label requires containment success AND procedural accuracy AND low fatigue AND correct emergency response all combining; this multi-AND gate produces a low pass rate by design, biased toward identifying improvement opportunities. The scorecard validates the more useful response_grade_score mean (0.58) which sits in the IPIECA/IADC competency-development band. For pass-rate modeling work, threshold response_grade_score > 0.65 directly to recover an industry-realistic ~60% pass rate.

2. Operator error probability mean is ~71%. Again, this is a training environment — operators are learning. Real-world (post-certification) operator error rates are much lower (~1–5%). For deployed-operator modeling, use OIL-038/039/040/045 which carry calibrated steady-state error rates.

3. Recommended retraining flag ~86%. This flag identifies any improvement opportunity, not just material competency gaps — most training sessions identify something to improve. For "actual retraining required" subset, intersect with training_pass_label == 0 AND response_grade_score < 0.50.

4. Violation severity is approximately uniform (~25% each across LOW / MEDIUM / HIGH / CRITICAL). Industry-mature operations have pyramid- shaped violation distributions. The uniform distribution is intentional for balanced ML training; for pyramid-shaped sampling, use OIL-037 (Regulatory Compliance) or OIL-045 (Workforce Safety Violations).

5. VR movement data uses simple 3D position + head rotation. No hand/controller pose data, no eye tracking, no biometric streams. For research requiring full VR biometric channels, the full product includes optional hand-tracking + eye-tracking + heart-rate streams.

6. Equipment interactions assume binary correct/incorrect action. Real operator training systems use graded correctness (e.g., "partially correct, sequenced wrong"). The full product carries a 5-tier correctness scale; sample uses the binary collapse.

7. HF preview sizing — default sample mode is 5K trainees × 25K sessions × 150K VR rows producing ~134 MB. The HF preview is reduced to 500/30/2,500/15,000, ~14 MB. All schemas, taxonomies, and scorecard calibrations are preserved at the smaller scale.

8. Deterministic seeding. All 13 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-046 product covers ~350,000 trainees × ~8,500 facilities × ~85 million sessions × ~950 million VR movement records across a 5-year horizon, with optional hand-tracking / eye-tracking / heart-rate biometric streams, 5-tier graded correctness on equipment interactions, calibrated industry-realistic pass-rate distributions, and configurable scenario- portfolio composition for industry-specific competency stress testing. Available under commercial license — contact 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.