xpertsystems/oil037-sample

OIL-037 — Synthetic Regulatory Compliance Dataset (Sample)

A schema-identical preview of OIL-037, the XpertSystems.ai synthetic regulatory compliance dataset for upstream, midstream, and downstream oil & gas operations. The full product covers 8,500 facilities across 16 regulatory frameworks (OSHA PSM, EPA CAA/CWA, API RP 754 / 1173, ISO 14001 / 45001, PHMSA, BSEE, GHGRP, SOX, NERC CIP, IEC 62443, NIST, SEC Climate, Corporate ESG). This sample is the generator's sample mode (500 facilities, 3-year window from 2023–2025) covering all 12 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's inside

12 CSV tables covering the complete GRC (governance / risk / compliance) lifecycle: facility master → audit trails → inspection findings → permit violations → CAPA workflows → environmental reporting → safety compliance → training → contractor compliance → escalation chains → cybersecurity compliance → pre-built ML labels.

Table	Rows (sample)	What it represents
compliance_master.csv	500	7-type facility master with regulation type, base risk, maturity
audit_trails.csv	2,250	6-type audits (internal, third-party, regulatory, ESG, cyber, PSM)
inspection_findings.csv	2,700	7-class findings with severity score and root cause
permit_violations.csv	8,800	7-type permits × 7 violation codes; shutdown/override/referral flags
capa_workflows.csv	5,000	ISO 45001 CAPA lifecycle: opened/target/actual close, aging, effectiveness
environmental_reporting.csv	64,000	8-type exceedances (CO₂/CH₄/VOC/SOₓ/NOₓ/produced water/flaring/spill) × 6 agencies
safety_compliance.csv	20,000	OSHA recordable flags + API 754 Tier 1–4 PSE classification
training_records.csv	30,000	8-module training × contractor flag × expiration tracking
contractor_compliance.csv	3,000	ISNetworld/Avetta-style pre-qualification audits
escalation_chains.csv	2,500	6-level escalation (site → regional → legal → exec → regulator → board)
cybersecurity_compliance.csv	6,000	NIST CSF / NERC CIP / IEC 62443 / SOX ITGC × 7 control families
compliance_labels.csv	500	Pre-built ML labels: risk score, enforcement & shutdown probabilities, priority segment

Total: ~145,000 rows, ~13 MB. The full OIL-037 product is ~6 million rows.

---

Calibration sources

Every distribution and ratio is anchored to named public references. The validation scorecard (see below) re-scores observed vs. target for 10 industry-anchored metrics, every one citing its source. Highlights:

• ISO 45001:2018 Clause 10.2 — OH&S corrective action overdue rate benchmark.
• CCPS Risk-Based Process Safety — repeat audit finding rate maturity bands.
• CCPS Auditing Guidelines — CAPA closure aging distribution.
• API RP 754 — Process Safety Performance Indicators (Tier 1–4 event mix).
• API RP 1173 — Pipeline Safety Management System audit benchmarks.
• ISO 19011 — Guidelines for auditing management systems.
• BSEE / PHMSA inspection statistics — critical finding severity ratio.
• EPA NEI / TRI compliance reporting — environmental exceedance baselines.
• PHMSA HL pipeline + BSEE OCS enforcement data — shutdown order frequency.
• IPIECA / OGUK incident-management benchmarks — escalation resolution rate.
• ISNetworld / Avetta — contractor pre-qualification insurance-validity baseline.

---

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	CAPA Overdue Rate (ceiling)	≤ 0.25	0.184	ISO 45001:2018
M02	Repeat Audit-Finding Rate (ceiling)	≤ 0.25	0.067	CCPS RBPS
M03	Critical Finding Share (ceiling)	≤ 0.075	0.034	BSEE / PHMSA
M04	Env Exceedance Rate (ceiling)	≤ 0.035	0.026	EPA NEI / TRI
M05	Enforcement Shutdown Rate (ceiling)	≤ 0.055	0.017	PHMSA / BSEE
M06	API 754 PSE Tier 1+2 Share	0.035–0.065	0.049	API RP 754
M07	CAPA Aging Days (median)	30–60	42	CCPS / ISO 45001
M08	Escalation Resolution Rate	0.62–0.78	0.709	IPIECA / OGUK
M09	Contractor Insurance Valid (floor)	≥ 0.89	0.928	ISNetworld / Avetta
M10	Audit Score Median (floor)	≥ 80 (0–100)	93.8	API RP 1173 / ISO 19011

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

---

Suggested use cases

• Enforcement-risk prediction — train classifiers that predict enforcement_probability or regulatory_shutdown_probability from facility context, finding history, environmental exceedances, and CAPA closure patterns. Pre-built labels in compliance_labels.csv.
• CAPA closure-aging forecasting — regression on aging_days from finding severity, facility maturity, contractor ratio, and effectiveness-check history. Practical for compliance-team capacity planning.
• GRC operational dashboards — compliance_master.csv × compliance_labels.csv on facility_id builds a complete priority-segmented portfolio view across 500 facilities for portfolio-level risk benchmarking.
• Audit-finding root-cause modeling — 7-class root_cause_category × 4-class severity ladder on ~2,700 findings enables HUMAN_ERROR vs. PROCEDURE_GAP vs. EQUIPMENT_CONDITION decomposition for audit-program improvement studies.
• Escalation pathway analysis — escalation_chains.csv has 6-level escalation ladder + response-time-hours + whistleblower/legal flags, enabling temporal-process-mining and bottleneck analytics.
• Multi-framework cyber compliance benchmarking — NIST CSF / NERC CIP / IEC 62443 / SOX ITGC × 7 control-family decomposition supports cross- framework gap-analysis modeling.
• Environmental exceedance early-warning — 8 exceedance types × 6 agencies × 64K reporting periods enables agency-specific exceedance and late-submission classifiers.

---

Loading

from datasets import load_dataset

facilities = load_dataset(
    "xpertsystems/oil037-sample",
    data_files="compliance_master.csv",
    split="train",
)
findings = load_dataset(
    "xpertsystems/oil037-sample",
    data_files="inspection_findings.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil037-sample",
    data_files="compliance_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/oil037-sample",
    filename="capa_workflows.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

All 12 tables join on facility_id. Cross-table joins also available on audit_id (audits ↔ findings), finding_id (findings ↔ violations ↔ capa), violation_id (violations ↔ capa ↔ safety ↔ escalation), and capa_id (capa ↔ escalation).

---

Schema highlights

compliance_master.csv — facility_id, facility_type (7-class: REFINERY / OFFSHORE_PLATFORM / LNG_TERMINAL / PIPELINE_SYSTEM / TANK_FARM / DRILLING_SITE / PETROCHEMICAL_PLANT), region (9-class), operator_entity, regulation_type (16-class), operational_maturity_score ∈ [0.05, 0.99], base_compliance_risk ∈ [0, 1], contractor_ratio, environmental_sensitivity, cyber_maturity_score, compliance_status ∈ {COMPLIANT, WATCH, NON_COMPLIANT, ENFORCEMENT_RISK}, last_major_audit_date, active_permit_count, employee_count.

inspection_findings.csv — finding_id, audit_id, facility_id, detected_date, finding_type (7-class: DOCUMENTATION_GAP / TRAINING_EXPIRY / EQUIPMENT_CERTIFICATION / PERMIT_BREACH / ENV_EXCEEDANCE / CYBER_CONTROL_GAP / PROCESS_SAFETY_GAP), severity_level ∈ {LOW, MEDIUM, HIGH, CRITICAL}, severity_score ∈ [0, 1], repeat_finding_flag, regulation_type (16-class), root_cause_category (7-class), required_remediation_due_date, inspector_variability_score.

safety_compliance.csv — osha_recordable_flag, process_safety_tier ∈ {NONE, TIER_4, TIER_3, TIER_2, TIER_1} (API RP 754 performance indicators), 7-class compliance_gap, 6-class corrective_action.

cybersecurity_compliance.csv — 5-class framework ∈ {NERC_CIP, IEC_62443, NIST_CSF, SOX_ITGC, CORPORATE_CYBER}, 7-class control_family (ACCESS_CONTROL / ASSET_INVENTORY / CHANGE_MANAGEMENT / LOGGING / INCIDENT_RESPONSE / VULNERABILITY_MGMT / NETWORK_SEGMENTATION), compliance_gap ∈ {NONE, MINOR, MAJOR}, audit_result ∈ {PASS, CONDITIONAL_PASS, FAIL}, gap_score, ot_asset_exposure_flag, remediation_sla_days.

compliance_labels.csv — pre-built ML labels per facility: risk_score, enforcement_probability, regulatory_shutdown_probability, compliance_grade ∈ {A, B, C, D, F}, priority_segment ∈ {LOW_RISK, WATCHLIST, HIGH_RISK, CRITICAL_RISK}.

---

Calibration notes & limitations

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

1. Training-record certification status skew. In training_records.csv, the CURRENT rate at sample scale is ~13% with ~86% EXPIRED, well below the API RP T-1 mature target (≥ 80% current). This is a generator quirk in the expiration-date sampling logic (random expirations uniformly drawn from -180 to +365 days around random in-window dates skew most flags to the EXPIRED side). For ML utility, use completion_score and days_until_expiry as primary features rather than the certification_status enum, or filter to days_until_expiry > 0 and treat the rest as a separate compliance-debt subset. The full OIL-037 product ships a re-calibrated training mode-pack.

2. compliance_labels.csv grade-class skew. ~91% of facilities receive grade "F" because the grade is computed as compliance_grade(1 - risk_score) but the risk-score formula at sample scale concentrates between 0.55–0.75, mapping to F under the threshold ladder. For balanced multi-class training, use priority_segment (4-class: LOW_RISK / WATCHLIST / HIGH_RISK / CRITICAL_RISK), which is well-spread at sample scale, or rebuild a custom grade column from risk_score directly with quintile thresholds.

3. Cybersecurity compliance pass rate. At sample scale, the cyber audit PASS rate is ~25% and FAIL ~21% — these reflect the generator's strict cyber_gap formula and are below NIST CSF mature-program baselines of ≥60% PASS. The 7-control-family taxonomy and 5-framework distribution are accurate; the categorical pass/fail thresholds are intentionally conservative for ML utility. The validation scorecard does not validate cyber audit results against NIST CSF maturity claims for this reason.

4. OSHA recordable rate. Sample-scale osha_recordable_flag rate (3.8%) is incident-dense relative to BLS oil & gas TRIR (0.8–1.5% in raw event terms). This is intentional ML-utility scaling (similar to OIL-035) so the 500-facility sample has trainable positive-class density. The full product recovers realistic upstream BLS rates.

5. Date-window length. Default window is 2023-01-01 to 2025-12-31 (3 years). For longer-horizon trend modeling, use the full product or override --start-date / --end-date on the underlying generator.

6. 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-037 product covers 8,500 facilities across a configurable multi-year horizon (~6 million rows total), with re-calibrated training certification status logic, balanced compliance-label distributions, and optional NIST CSF maturity-aligned cyber-audit mode-packs. 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.