README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- synthetic
- oil-and-gas
- downstream
- refining
- turnaround
- shutdown
- maintenance-planning
- rbi
- api-510
- psm
- xpertsystems
pretty_name: "OIL-022 — Synthetic Shutdown & Turnaround Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-022 — Synthetic Shutdown & Turnaround Dataset (Sample)

**SKU:** `OIL022-SAMPLE` · **Vertical:** Oil & Gas / Downstream Refining
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil022.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise refinery
shutdown & turnaround dataset for **maintenance planning ML, inspection
optimization, schedule slippage prediction, restart readiness assessment,
turnaround cost forecasting, and RBI (risk-based inspection) analytics**.
The sample covers **1,200 turnaround campaigns** across
**15 refineries** with **2,250 pieces of
equipment** in **10 global regions**, with **165,114 rows**
linked across **15 tables**.

**OIL-022 is the third downstream (refining) SKU** in the catalog,
complementing OIL-019 (refinery process operations) and OIL-020 (product
yields + economics) with **maintenance, inspection, and turnaround
operations** specialization.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `refineries_master.csv` | 15 | 6 | Refinery catalog: 10 regions × 4 operator types × Nelson complexity × capacity × PSM maturity |
| `equipment_master.csv` | 2,250 | 11 | Equipment inventory: 14 classes × 14 units × 5 material families × 4 RBI categories per API 580 |
| `shutdown_campaigns.csv` | 1,200 | 12 | Turnaround campaigns: 5 shutdown types × planned/actual duration × schedule slippage × scope complexity |
| `corrosion_monitoring.csv` | 11,250 | 9 | Per-equipment 5-point time-series: UT/RT/Guided Wave measurements + 10-class corrosion mechanisms per API 570 + NACE |
| `maintenance_work_orders.csv` | 39,048 | 12 | Per-campaign WOs: 12 maintenance types × 4 priorities × 4 statuses × QA/QC flags |
| `inspection_findings.csv` | 21,479 | 11 | **API 510 RBI findings**: wall thickness, corrosion rate, remaining life, anomaly score per API 580/581 |
| `turnaround_schedule.csv` | 36,000 | 9 | Critical path tasks: predecessor logic, 10 craft types, planned hours, schedule risk score |
| `workforce_allocation.csv` | 24,000 | 8 | Contractor allocations: 350 contractors × 10 craft types × shift hours × fatigue risk |
| `permit_to_work.csv` | 15,771 | 8 | OSHA 1910.119 PSM permits: 7 permit types × 4 hazard levels × isolation/gas test/approval delay |
| `equipment_failures.csv` | 1,031 | 7 | 10 failure modes × 7 root causes × downtime + startup-detection flag |
| `catalyst_replacement.csv` | 1,045 | 7 | Reactor catalyst events: 6 catalyst types × activity % × age days × replacement cost |
| `startup_readiness.csv` | 9,600 | 6 | 8-step startup readiness per CCPS: Mechanical Completion → Stability Test + risk scores |
| `turnaround_costs.csv` | 1,200 | 7 | Per-campaign cost breakdown: labor + material + delay + contractor + total |
| `safety_events.csv` | 25 | 7 | 7-class CCPS events: near miss, first aid, recordable, lost time + severity + corrective action days |
| `shutdown_labels.csv` | 1,200 | 9 | **FEATURE-COUPLED ML labels**: 4-class reliability grade (A/B/C/D) + restart success + cost overrun + completion % |

Total: **165,114 rows** across 15 CSVs, ~12.5 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **API 510** (Pressure Vessel Inspection Code),
**API 570** (Piping Inspection Code), **API 580/581** (Risk-Based
Inspection), **NACE TM0274** (corrosion measurement), **OSHA 29 CFR
1910.119** (Process Safety Management — PSM), **AFPM Reliability and
Maintenance Benchmarking Survey**, **Solomon Associates Refinery
Performance Survey**, **IPA (Independent Project Analysis) Turnaround Cost
Performance Database**, OGCI turnaround safety statistics, **ANSI/AICHE
CCPS** (Center for Chemical Process Safety) guidelines, **EIA-820** Refinery
Capacity Report, **Nelson Complexity Index** (Oil & Gas Journal).

**Sample run** (seed `42`, n_turnarounds=1,200, refineries=15):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg refinery capacity bpd | 217625.3333 | 220000.0 | ±80000.0 | ✓ PASS | EIA-820 Refinery Capacity Report — mean capacity for mixed global refinery portfolio (US median ~135K BPD, largest US refineries 600K+ BPD, Indian/Chinese mega-refineries 400-1200K BPD; portfolio mean ~220K BPD) |
| 2 | avg complexity index | 9.6007 | 9.5 | ±2.0 | ✓ PASS | Nelson Complexity Index (Oil & Gas Journal) + Solomon Associates Refinery Performance Survey — mean Nelson Complexity for global refinery portfolio (simple hydroskimmers 4-6, modern conversion refineries 9-12, deep-conversion / petrochemical 12-16) |
| 3 | avg planned duration days | 23.9475 | 26.0 | ±6.0 | ✓ PASS | AFPM Reliability and Maintenance Benchmarking Survey + Solomon Associates — mean planned turnaround duration for mixed scope portfolio (pitstop 10d, planned TA 26d, major TA 42d; portfolio mean ~26d weighted by frequency) |
| 4 | avg schedule slippage pct | 12.6093 | 11.5 | ±5.0 | ✓ PASS | IPA (Independent Project Analysis) Turnaround Cost Performance Database + AFPM — mean schedule slippage across refinery turnaround portfolio (8-15% typical for well-planned, 20%+ indicates poor planning per IPA benchmarks) |
| 5 | avg corrosion rate mpy | 6.5791 | 5.0 | ±3.0 | ✓ PASS | API 570 (Piping Inspection Code) + NACE TM0274 — mean corrosion rate for refinery piping portfolio (2-8 mpy normal for moderate service; >10 mpy triggers RBI high-risk classification per API 581) |
| 6 | avg work order completion pct | 89.9395 | 90.0 | ±5.0 | ✓ PASS | AFPM Reliability and Maintenance Benchmarking Survey — mean work order completion rate during refinery turnarounds (85-95% typical; >95% indicates either conservative scoping or schedule pressure) |
| 7 | anomaly flag rate | 0.0321 | 0.032 | ±0.015 | ✓ PASS | ANSI/AICHE CCPS process safety management + AFPM operational data — typical anomaly/deviation rate for refinery work order execution (2-5% of WOs exhibit execution anomalies per CCPS safety reporting) |
| 8 | slippage reliability pearson correlation | -0.6386 | -0.55 | ±0.15 | ✓ PASS | IPA Turnaround Cost Performance + AFPM — expected strong inverse correlation between schedule slippage and reliability grade score (generator formula: reliability_score = 100 - slippage*0.7 - findings*0.8 - completion_penalty*1.2). Validates feature-coupled label generation. |
| 9 | corrosion remaining life pearson correlation | -0.5538 | -0.5 | ±0.15 | ✓ PASS | API 510 + API 580/581 (Risk-Based Inspection) — expected inverse correlation between corrosion rate and remaining life (RBI formula: remaining_life = (wall_thickness - retirement_limit) / corrosion_rate). Validates generator's API 510 RBI physics. |
| 10 | equipment class diversity entropy | 0.9986 | 0.97 | ±0.03 | ✓ PASS | API 580 RBI equipment classification + Solomon Associates equipment census — 14-class equipment diversity benchmark covering pressure vessels, heat exchangers, fired heaters, compressors, pumps, piping, tanks, valves, reactors, columns, boilers, cooling towers, instrument loops, normalized Shannon entropy |

**Overall: 100.0/100 — Grade A+**
(10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

---

## Schema highlights

**`refineries_master.csv`** — 10-region global refinery portfolio with
**Nelson Complexity Index**:

| Region | Complexity Notes |
|---|---|
| US Gulf Coast | High complexity (11-13) — deep conversion + petrochemical |
| US West Coast | Moderate (8-10) — fluid catalytic cracking |
| North Sea | Moderate-high (10-12) — sour crude processing |
| Middle East | Mixed (6-14) — both simple export + deep conversion |
| India / China | Mega-refineries (10-15) — petrochemical integrated |
| Brazil | Moderate (8-11) — heavy/sour crude |
| North/West Africa | Lower (5-9) — export-grade simple refineries |
| SE Asia | Moderate (8-12) |
| Western Europe | Higher (10-13) — declining capacity, high specs |

**`shutdown_campaigns.csv`** — 5 shutdown types per **AFPM nomenclature**:

| Type | Weight | Base Duration |
|---|---:|---:|
| Planned Turnaround | 58% | 26d |
| Major Turnaround | 16% | 42d |
| Pitstop | 14% | 10d |
| Emergency Shutdown | 6% | 7d |
| Regulatory Outage | 6% | 18d |

Schedule slippage applied stochastically: `actual_duration = planned × (1 + slippage/100)`
with ~3.5% anomaly rate adding 12-45 extra slippage percentage points.

**`inspection_findings.csv`** — **API 510 RBI physics** implemented:

> wall_thickness = nominal − (age × corrosion_rate × 0.0254/2.0) + noise   [API 570 form]
> remaining_life = (wall_thickness − retirement_limit) / corrosion_rate    [API 580/581]
> anomaly_score = f(metal_loss, criticality) clip(0, 1)
> repair_required_flag = (anomaly_score > 0.68)                            [API 510 trigger]

The sample's corrosion-rate↔remaining-life Pearson correlation is r ≈ −0.55
— **strong inverse coupling validates API 510 RBI physics** (higher
corrosion rate → shorter remaining life).

**`maintenance_work_orders.csv`** — slippage-coupled WO execution:

> est_hours = lognormal(2.65, 0.7)              # typical 14-23 hours
> overrun_factor = N(1 + slippage/180, 0.22)    # slippage drives overrun
> actual_hours = est_hours × overrun_factor

WO status distribution per **AFPM benchmark**: 90% Completed / 5% Deferred /
4% In Progress / 1% Cancelled.

**`permit_to_work.csv`** — **OSHA 29 CFR 1910.119 PSM** permit types:

| Type | Notes |
|---|---|
| Hot Work | Welding/cutting requires gas test |
| Confined Space | Vessel entry requires gas test |
| Line Break | Piping isolation requires gas test |
| Electrical Isolation | LOTO |
| Working at Height | Fall protection |
| Cold Work | Routine maintenance |
| Excavation | Underground services |

**`shutdown_labels.csv`** — **FEATURE-COUPLED ML labels** (unlike OIL-019/020 pure-random labels):

> reliability_score = 100 − slippage × 0.7 − high_risk_findings × 0.8
>                    − max(0, 95 − completion_pct) × 1.2
> reliability_grade = 'A' if score ≥ 90 else 'B' if ≥ 80 else 'C' if ≥ 70 else 'D'
> restart_success = (ready_count ≥ 7) AND (reliability_score > 72) AND (rng > risk × 0.12)

The slippage↔reliability Pearson correlation is r ≈ −0.64 in the sample —
**strong inverse coupling validates feature-coupled labels** per IPA/AFPM
turnaround performance benchmarks.

---

## Suggested use cases

1. **API 510 remaining life regression** — predict `remaining_life_years`
   from corrosion rate + wall thickness + criticality features. **Strong
   physics signal**: corrosion-life inverse r ≈ −0.55.
2. **Reliability grade classification** — 4-class ordinal classifier on
   `reliability_grade` (A/B/C/D) from slippage + findings + completion
   features. **Strong feature coupling** — models WILL learn meaningful
   patterns (unlike OIL-019/020 pure-random labels).
3. **Schedule slippage regression** — predict `schedule_slippage_pct`
   from scope_complexity + shutdown_type + equipment criticality
   features per IPA turnaround benchmark.
4. **Anomaly score regression** — predict `anomaly_score` from corrosion
   rate + wall loss + criticality per API 580/581 RBI.
5. **Restart success binary classification** — predict
   `restart_success_flag` from readiness + reliability + risk features.
6. **Cost overrun prediction** — predict `cost_overrun_flag` from
   slippage + scope complexity features per IPA cost performance.
7. **Permit-to-work hazard classification** — 4-class hazard level
   classifier per OSHA 1910.119 PSM.
8. **Turnaround cost regression** — predict `total_cost` from labor +
   material + delay components per AFPM cost benchmarks.
9. **Safety event classification** — 7-class CCPS event type
   classifier (rare events; see Honest Disclosure §3).
10. **Multi-table relational ML** — entity-resolution and graph neural-
    network learning across the 15 joinable tables via `turnaround_id`,
    `equipment_id`, `refinery_id`, `work_order_id`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil022-sample", data_files="inspection_findings.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
ref     = pd.read_csv("hf://datasets/xpertsystems/oil022-sample/refineries_master.csv")
camps   = pd.read_csv("hf://datasets/xpertsystems/oil022-sample/shutdown_campaigns.csv")
find    = pd.read_csv("hf://datasets/xpertsystems/oil022-sample/inspection_findings.csv")
labels  = pd.read_csv("hf://datasets/xpertsystems/oil022-sample/shutdown_labels.csv")

# Multi-table join for ML feature engineering:
joined = (labels
    .merge(camps, on="turnaround_id")
    .merge(ref, on="refinery_id"))
# Now you have reliability grade alongside slippage + complexity + region + capacity
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
both `random.seed` and `np.random.default_rng`). A seed sweep across
`[42, 7, 123, 2024, 99, 1]` confirms Grade A+ on every seed in this sample.

---

## Honest disclosure of sample-scale limitations

This is a **sample** product calibrated for turnaround/maintenance ML
research, not for live planning decisions. Several notes:

1. **Work order completion is ~90%** vs declared 93% benchmark — generator
   samples status uniformly with `[0.90, 0.05, 0.04, 0.01]` weights, so
   per-WO completion is 90% by construction. Per-campaign aggregate
   completion (used in labels) averages 89.94% in the sample. **For
   90%+ scenarios, this is realistic; for top-decile world-class
   turnarounds (95%+ per Solomon Associates Q1 performers), the
   sample is biased low.** Use the full product or post-process with
   quartile-conditional completion priors.

2. **Cost overrun rate is ~99.75%** because the generator's
   `delay_cost = max(0, actual - planned) × U(450K, 2.5M)` is non-zero
   whenever actual exceeds planned (true for ~99% of sample rows
   given mean 12.6% slippage). Real cost overrun rates depend on
   budget granularity — 99% is realistic for "any delay = cost
   overrun" definition but unrealistic for "≥10% budget overrun"
   definition. Treat `cost_overrun_flag` as "any slippage" indicator
   rather than budget-threshold flag.

3. **Safety events are very sparse** (~2 events per 100 campaigns)
   reflecting realistic OGCI/CCPS rates. At sample scale (1200
   campaigns), this produces only ~25 events — **insufficient for
   class-balanced 7-class safety event ML**. For safety event ML,
   use the full product (45,000+ campaigns generating 500+ events)
   or merge with the alarm_trip_logs from OIL-019 / OIL-021.

4. **Equipment criticality coupling to failure rate is weak** in
   the sample (failed equipment criticality 0.882 vs safe 0.873
   — only 0.009 difference). The generator's
   `fail_prob = failure_rate × (0.55 + criticality) × (1 + slippage/100)`
   formula spreads failure probability across most equipment because
   `failure_rate=0.016` is small. **Strong physics signal requires
   larger samples** — the full product (90K equipment) shows clearer
   criticality-failure coupling.

5. **Restart success rate is ~82%** vs declared target 96% — the
   generator's restart_success formula penalizes for several
   conditions (low readiness count, low reliability score, high
   restart risk). The sample-scale rate is realistic for moderate-
   complexity turnaround portfolios but lower than world-class
   benchmarks. Filter to `reliability_grade in ['A', 'B']` for
   high-performing subset analysis.

6. **Reliability grade distribution is B-dominant** (52% B, 24% A,
   19% C, 5% D) reflecting the slippage-coupled formula. This is a
   **meaningful 4-class distribution unlike degenerate single-class
   outcomes in some other refinery SKUs** — both ordinal classification
   and continuous reliability_score regression are well-supported.

7. **Material family is uniformly carbon-steel-dominant (~63%)**
   per declared weights, reflecting refinery construction reality.
   But material choice is **not coupled to service severity** (sour
   service should drive more Cr-Mo / stainless; high-temp service
   should drive more refractory alloys). For service-conditioned
   material ML, the full product v1.1 will add unit-conditioned
   metallurgy.

8. **Catalyst events are sparse** (~1 per campaign on average) and
   only fire for reactor equipment or specific unit types (FCCU,
   Hydrocracker, Reformer, Hydrotreating). For catalyst lifecycle
   ML, filter to those unit types and use the catalyst_replacement
   table directly.

---

## Cross-references to other XpertSystems OIL SKUs

This SKU completes the **3-SKU downstream refining trilogy**:

| SKU | Layer | Focus |
|---|---|---|
| **OIL-019** | **Downstream — process** | Refinery unit operations (CDU/VDU/FCC reactor + control + HX) |
| **OIL-020** | **Downstream — yield** | Refinery crude-to-product yields + economics + emissions |
| **OIL-021** | **Cross-stream** | Equipment performance + condition monitoring + RUL |
| **OIL-022** | **Downstream — turnaround** | **Shutdown/turnaround planning + RBI + inspection + workforce** *(this SKU)* |

**OIL-022 vs OIL-019**: OIL-019 simulates **steady-state refinery operations**
(when units are running). OIL-022 simulates **transient turnaround operations**
(when units are shut down for inspection/maintenance). Use OIL-019 for
operational ML, OIL-022 for **maintenance planning, scheduling, and
turnaround cost ML**.

**OIL-022 vs OIL-021**: OIL-021 simulates **continuous equipment condition
monitoring** (vibration, lubrication, thermal). OIL-022 simulates
**point-in-time inspection findings** (UT, RT, guided wave thickness
measurements) during scheduled turnarounds. Use OIL-021 for **predictive
maintenance** ML, OIL-022 for **RBI / inspection planning** ML.

---

## Full product

The **full OIL-022 dataset** ships at **45,000 turnarounds × 150 refineries
× 3,000 equipment per refinery** (prod mode) producing several hundred
million rows with **service-conditioned metallurgy**, **quartile-realistic
completion rates** (Q1 95%+ / Q4 75-85%), **richer safety event populations**
(500+ events for class-balanced ML), and **stronger equipment-criticality
failure coupling** (large-sample statistical power) — licensed commercially.
Contact XpertSystems.ai for licensing terms.

📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**

---

## Citation

```bibtex
@dataset{xpertsystems_oil022_sample_2026,
  title  = {OIL-022: Synthetic Shutdown & Turnaround Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil022-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 20:40:58 UTC
- Refineries        : 15
- Equipment per ref : 150 (2250 total)
- Turnaround campaigns: 1200
- Work orders per TA : 40
- Regions           : 10 (US Gulf Coast, US West Coast, North
                      Sea, Middle East, India, Southeast Asia, China,
                      Brazil, North Africa, Western Europe)
- Equipment classes : 14 (Pressure Vessel, Heat Exchanger, Fired Heater,
                      Compressor, Pump, Piping Circuit, Storage Tank,
                      Control Valve, Relief Valve, Reactor, Column,
                      Boiler, Cooling Tower, Instrument Loop)
- Unit types        : 14 (CDU, VDU, FCCU, Hydrocracker, Delayed Coker,
                      Reformer, Alkylation, Hydrotreating, Sulfur
                      Recovery, Hydrogen Plant, Tank Farm, Utilities,
                      Cooling Water, Flare System)
- Shutdown types    : 5 (Planned TA, Major TA, Pitstop, Emergency
                      Shutdown, Regulatory Outage)
- Corrosion mechanisms: 10 (Uniform, Pitting, Sulfidation, Naphthenic
                      Acid, Erosion-Corrosion, CUI, H2S Damage, Amine,
                      Thermal Fatigue, Chloride SCC)
- Calibration basis : API 510, API 570, API 580/581, NACE TM0274,
                      OSHA 29 CFR 1910.119, AFPM RAM Survey, Solomon
                      Associates, IPA Turnaround Cost Database, OGCI,
                      CCPS, EIA-820, Nelson Complexity Index
- Overall validation: 100.0/100 — Grade A+