--- 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: - 100K10 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+