xpertsystems/oil031-sample

OIL-031 — Synthetic Shipping & Logistics Dataset (Sample)

SKU: OIL031-SAMPLE · Vertical: Oil & Gas / Midstream Shipping License: CC-BY-NC-4.0 (sample) · Schema version: oil031.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise shipping & logistics dataset for tanker route optimization, AIS analytics, freight rate forecasting, port congestion ML, demurrage prediction, chokepoint risk modeling, and voyage efficiency classification. The sample covers 2,500 voyages across 250 vessels in 6 tanker classes (VLCC / Suezmax / Aframax / LR2 / MR / Handy) over 500 routes spanning 180 days of operations, with 156,285 rows linked across 12 tables.

OIL-031 has substantial real shipping industry physics — Haversine great-circle distance with maritime routing factor, BIMCO loading/discharge rates, Worldscale freight pricing, 7 real EIA chokepoints with actual traffic shares, feature-coupled delay decomposition, and feature-coupled efficiency grading.

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What's in the box

File	Rows	Cols	Description
vessel_master.csv	250	13	6-class tanker fleet: VLCC / Suezmax / Aframax / LR2 / MR / Handy × 10 flag states × eco_design + reliability + inspection risk
route_master.csv	500	12	Haversine + maritime factor distances × 7 route regions × 4 risk scores (weather / sanctions / piracy / chokepoint_count)
voyage_events.csv	48,765	11	AIS-grade position telemetry at 24-hour intervals: lat/lon/speed/heading/operational_state (anchored/slow_steaming/underway) per IMO Res. A.917
cargo_movements.csv	2,500	13	6 crude grades + 4 products with real API gravity + sulfur per assays (WTI 40/0.24, Brent 38/0.37, Maya 22/3.4, Bonny Light 35/0.14)
port_operations.csv	2,500	12	BIMCO loading/discharge rates + berth wait + customs delay per terminal reliability
port_congestion.csv	5,000	8	Per-port queue + waiting hours + congestion index + berth utilization + weather restriction
shipping_delays.csv	5,978	7	5-class delay taxonomy: weather / port_congestion / chokepoint / mechanical_or_operational / rare_event + financial impact + avoidable flag
freight_rates.csv	78,000	9	Worldscale rates + USD/day + bunker price (VLSFO 0.5% per IMO 2020) + supply/demand ratio + 30d volatility
demurrage_costs.csv	2,500	7	4 charter party types: spot / time_charter / COA / voyage_charter + laycan missed + claim dispute probability
weather_disruptions.csv	2,500	8	Storm severity + Beaufort wind speed + wave height + 4-season classification
chokepoint_events.csv	2,792	7	7 real EIA chokepoints: Suez / Panama / Hormuz / Bab el-Mandeb / Malacca / Turkish Straits / Cape of Good Hope + 3-class risk + reroute flag
logistics_labels.csv	2,500	10	FEATURE-COUPLED ML labels: 4-class efficiency grade (A/B/C/D) + 3-class recommended action (proceed/hold_at_anchor/reroute) + delay/congestion/freight risk scores

Plus optional helper: voyage_summary.csv (2,500 rows, 17 cols) — joins all voyage-level features into a single audit table.

Total: 156,285 rows across 13 CSVs, ~13.7 MB on disk.

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Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: BIMCO (Baltic and International Maritime Council), INTERTANKO (Independent Tanker Owners Association), Worldscale Association freight rate standardization, Baltic Exchange BDTI / BCTI dirty/clean tanker indices, Clarkson Research shipping data, VesselsValue valuations, IMO (International Maritime Organization) regulations, MARPOL Annex VI sulfur emissions (IMO 2020 0.5% sulfur cap), SOLAS Safety of Life at Sea, EIA World Oil Transit Chokepoints (traffic share data: Hormuz 21%, Malacca 16%, Suez 9%, Bab el-Mandeb 12%, Panama Canal 3%, Turkish Straits 3%, Cape of Good Hope 4%), UNCTAD Review of Maritime Transport, IACS classification society standards, Lloyd's List Intelligence, AIS per IMO Res. A.917, Beaufort Wind Scale.

Sample run (seed 42, n_vessels=250, n_routes=500, n_voyages=2500, days=180):

#	Metric	Observed	Target	Tolerance	Status	Source
1	avg tanker speed knots	13.6305	13.5	±1.5	✓ PASS	Clarkson Research + INTERTANKO fleet operational data — mean tanker speed for mixed fleet (11-15 knots typical; slow-steaming reduces by 1-2 knots vs design speed; eco-design tankers trend 12-13 knots)
2	avg loading rate bph	45256.9498	45000.0	±10000.0	✓ PASS	BIMCO + INTERTANKO terminal operations standards — typical VLCC/Suezmax loading rate (35,000-55,000 bph for crude; 25,000-45,000 bph for products; varies by terminal infrastructure)
3	avg discharge rate bph	37924.4911	38000.0	±10000.0	✓ PASS	BIMCO + INTERTANKO terminal operations standards — typical tanker discharge rate (30,000-45,000 bph; discharge slower than loading due to vessel pump limitations vs gravity loading)
4	avg bunker price usd mt	650.3941	650.0	±150.0	✓ PASS	MARPOL Annex VI / IMO 2020 sulfur cap — typical VLSFO 0.5% sulfur bunker fuel price 2023-2024 ($550-800 per mt; IFO 380 cheaper at $400-550; $650 mid-range for mixed fleet)
5	avg planned transit days	16.1006	16.0	±5.0	✓ PASS	Clarkson Research global tanker route data — typical planned transit duration (MEG-Asia ~18 days; USGC-Asia ~30 days; Intra-Asia ~4-8 days; portfolio mean ~16 days)
6	distance planned days correlation	0.9834	0.95	±0.07	✓ PASS	Kinematics d = v·t — expected near-deterministic positive correlation between route distance and planned transit days (planned_days = distance_nm / (speed × 24); cross-vessel speed variance introduces modest noise)
7	delay risk efficiency correlation	-0.9321	-0.85	±0.1	✓ PASS	Generator formula: efficiency = 1 - 0.45·delay_risk - 0.25·congestion_risk - 0.15·weather_severity. Near-deterministic inverse coupling validates feature-coupled efficiency grading per Baltic Exchange BDTI route performance methodology.
8	congestion queue correlation	0.6010	0.55	±0.15	✓ PASS	Port queueing physics — expected positive correlation between port congestion index and queue length (generator: queue = Poisson(3 + congestion × 18); Lloyd's List Intelligence port congestion data shows r ≈ 0.5-0.7 for AIS-tracked port queues).
9	demurrage hours usd correlation	0.8312	0.8	±0.1	✓ PASS	Industry demurrage commercial practice — expected strong positive correlation between demurrage hours and USD cost (demurrage_usd = hours/24 × freight × 1.2-1.8; freight rate variance creates moderate noise vs deterministic 1:1 expectation).
10	tanker class diversity entropy	0.9671	0.96	±0.04	✓ PASS	6-class tanker fleet taxonomy per Clarkson Research / INTERTANKO classifications (VLCC, Suezmax, Aframax, LR2, MR, Handy) — fleet composition diversity benchmark, normalized Shannon entropy

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

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Schema highlights

vessel_master.csv — 6-class tanker fleet per INTERTANKO / Clarkson Research:

Tanker Class	DWT Range	Capacity (bbl)	Speed (knots)	Day Rate (USD)
VLCC	200K-320K	1.8M-2.2M	11-15	$45K-135K
Suezmax	120K-200K	0.9M-1.2M	11.5-15	$35K-105K
Aframax	80K-120K	0.55M-0.8M	11.5-15.5	$28K-85K
LR2	80K-115K	0.6M-0.75M	12-16	$25K-75K
MR	40K-55K	0.28M-0.42M	12-16.5	$18K-52K
Handy	25K-40K	0.18M-0.3M	11.5-16	$14K-42K

10 real flag states: PA (Panama), LR (Liberia), MH (Marshall Islands), SG (Singapore), GR (Greece), JP (Japan), KR (Korea), US, GB, NO (Norway).

route_master.csv — Haversine great-circle + maritime routing factor:

distance_nm = haversine(origin, dest) × 0.539957 (km → nm) maritime_factor = U(1.08, 1.62) (deviation for sea lanes) chokepoint_count = Poisson(distance / 4500)

7 route regions per actual trade flows: USGC-Asia, MEG-Asia, West Africa- Europe, North Sea-Europe, LatAm-USGC, Med-Europe, Intra-Asia.

chokepoint_events.csv — 7 real EIA chokepoints with actual traffic shares:

Chokepoint	EIA Traffic Share	Notes
Strait of Hormuz	21%	Persian Gulf → world (peak risk during Iran tensions)
Malacca Strait	16%	Middle East / Africa → Asia (piracy historical)
Bab el-Mandeb	12%	Red Sea / Suez (Houthi attacks 2023-2024)
Suez Canal	9%	Europe ↔ Asia (Ever Given 2021)
Cape of Good Hope	4%	Suez alternative for VLCC (no canal constraint)
Panama Canal	3%	Atlantic ↔ Pacific (drought 2023-2024 reduced capacity)
Turkish Straits	3%	Black Sea → Mediterranean (Russia oil sanctions 2022)

logistics_labels.csv — feature-coupled ML labels:

delay_risk = clip(total_delay_hours / 120, 0, 1) congestion_risk = (origin_cong + dest_cong) / 2 efficiency = 1 - 0.45·delay_risk - 0.25·congestion_risk - 0.15·weather_severity efficiency_grade = A (≥0.82) / B (≥0.68) / C (≥0.52) / D (<0.52) recommended_action = reroute (rare_event OR delay > 0.65) / hold_at_anchor (congestion > 0.7) / proceed (else)

The sample's delay_risk ↔ efficiency Pearson correlation is r ≈ -0.93 — near-deterministic inverse coupling validates feature-coupled labels.

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Suggested use cases

1. Voyage efficiency classification — 4-class ordinal classifier on route_efficiency_grade from delay + congestion + weather features. Strong feature coupling — models WILL learn meaningful patterns.
2. Delay prediction regression — predict total_delay_hours from route + weather + chokepoint + reliability features per delay decomposition formula.
3. Worldscale freight forecasting — time-series forecasting of worldscale_rate from supply/demand + bunker price + seasonality.
4. Demurrage cost prediction — predict demurrage_usd from delay hours + charter party type + freight rate features. Strong physics: demurrage hours ↔ USD r ≈ +0.83.
5. Port congestion forecasting — predict congestion_index from berth_utilization_pct and queue features per Lloyd's List methodology.
6. 5-class delay type classification — multi-class classifier on delay_type (weather / port / chokepoint / mechanical / rare_event).
7. Chokepoint risk classification — 3-class classifier on risk_level (low / medium / high) from queue + chokepoint features per EIA chokepoint methodology.
8. AIS anomaly detection — anomaly detection on voyage_events.ais_gap_flag per IMO Res. A.917 AIS standards.
9. 6-class tanker class classification — predict tanker_class from DWT + capacity + speed features per INTERTANKO classification.
10. Multi-table relational ML — entity-resolution + graph neural network learning across the 12 joinable tables via vessel_id, route_id, voyage_id, cargo_id, port_id.

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Loading

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil031-sample", data_files="logistics_labels.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
vessels = pd.read_csv("hf://datasets/xpertsystems/oil031-sample/vessel_master.csv")
routes  = pd.read_csv("hf://datasets/xpertsystems/oil031-sample/route_master.csv")
cargo   = pd.read_csv("hf://datasets/xpertsystems/oil031-sample/cargo_movements.csv")
events  = pd.read_csv("hf://datasets/xpertsystems/oil031-sample/voyage_events.csv")
labels  = pd.read_csv("hf://datasets/xpertsystems/oil031-sample/logistics_labels.csv")

# Multi-table voyage feature engineering:
joined = (labels
    .merge(cargo, on="voyage_id")
    .merge(vessels, on="vessel_id")
    .merge(routes, on="route_id"))
# Predict route_efficiency_grade from vessel + cargo + route features

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Reproducibility

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

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Honest disclosure of sample-scale limitations

This is a sample product calibrated for shipping/logistics ML research, not for live voyage planning or chartering decisions. Several notes:

1. Vessel speed ↔ planned days correlation is moderate (r ≈ -0.18 vs expected -0.5). Real markets show stronger inverse coupling for fixed distance, but the sample's distance variance dominates the speed-time relationship because routes are randomly sampled across diverse distances. For speed-time ML, filter to single route_id or single distance bucket to isolate the speed effect.

2. Wave height ↔ wind speed correlation is moderate (r ≈ 0.46). The Beaufort wind scale predicts wave height as a deterministic function of sustained wind, so real-world r is ~0.85-0.95. The sample uses independent N(weather_sev × scale, noise) for both, producing weaker coupling. For Beaufort-grade ML, derive wave height from wind:

   weather['beaufort_wave'] = 0.018 * weather['wind_speed_knots']**2  # ft → m
   

3. Season distribution is skewed (spring 55%, winter 36%, summer 9% for the seed-42 sample). This reflects the 180-day simulation horizon starting January 2024 (covering Jan-Jun → mostly winter/ spring with some summer). For seasonal-balanced ML, use the full product (365+ days) or augment with a 4-season cyclic feature.

4. Tanker class distribution shows mild deviation from declared weights (sample MR 22.8% vs declared 23%, Aframax 20.4% vs 23%, VLCC 17.6% vs 16%). This is sampling noise at n=250 vessels and converges to declared weights at larger fleet sizes. For class-balanced ML, use stratified sampling or filter to specific tanker classes.

5. Cargo grade distribution is roughly uniform 9-11% rather than real-world weighted by trade volume. WTI / Brent / Arab Light / Basrah Medium typically dominate VLCC trade (~75% combined per IEA), while Maya / Bonny Light are smaller shares. For realistic trade-flow ML, filter to specific tanker class × grade combinations that match real trade routes.

6. Recommended action is heavily 'proceed' (93%) because reroute triggers (rare_event OR delay_risk > 0.65) and hold_at_anchor triggers (congestion > 0.7) are rare at sample horizon. For class-balanced recommended_action ML, oversample rare events or use the full product (45,000 voyages) for balanced 3-class distributions.

7. AIS gap flag rate is ~0.6%. Real AIS coverage is 95-98% globally per UNCTAD, so 0.6% gap rate is realistic for normal operations. But for AIS-anomaly ML (detecting sanctions-evasion / "dark fleet" vessels), the sample doesn't generate clusters of correlated AIS gaps. Use the full product or merge with public AIS-spoofing research datasets.

8. Freight volatility 30d is uniform (mean 22%) rather than regime-conditioned. Real Worldscale rates have clustered volatility regimes per BDTI / BCTI index history (calm periods <15%, volatile periods >40%). For vol-regime ML, derive your own regime classification from rolling Worldscale rate statistics.

9. Charter party type distribution is uniform 24-26% across 4 classes rather than realistic spot-dominant (~60% spot, ~25% time charter, ~10% COA, ~5% voyage charter per Clarkson commercial reports). For charter-type ML, filter to specific types or use derived spot-vs-term classification.

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Where physics IS strong (use these for ML)

Seven coupling signals in this sample are physically valid and ML-useful:

Signal	r	Source
Delay hours/24 ↔ actual-planned days	+1.000	Mass balance of voyage duration
Distance ↔ planned transit days	+0.983	Kinematics d=v·t per Haversine + speed
Delay risk ↔ efficiency score	-0.932	Generator's feature-coupled label formula
Total delay ↔ efficiency score	-0.879	Feature-coupled efficiency formula
Demurrage hours ↔ USD	+0.831	Commercial demurrage = hours × rate
Congestion ↔ queue length	+0.601	Port queueing physics (Poisson)
Storm severity ↔ weather delay	+0.534	Weather delay formula

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Cross-references to other XpertSystems OIL SKUs

This SKU is the first midstream-shipping SKU in the catalog — opening a new sub-vertical alongside midstream-pipeline (OIL-015/024/025/027) and storage (OIL-028):

Midstream layer	SKU	Focus
Pipeline flow assurance	OIL-015	Wax / hydrate / asphaltene threshold gating
Pipeline operations	OIL-024	Hydraulics + SCADA + 15 transient events
Pipeline leak detection	OIL-025	Toricelli orifice + acoustic + RBI
Pipeline corrosion	OIL-027	de Waard-Milliams + NACE SP0169 CP
Tank storage	OIL-028	Mass-balance inventory + API 650/653
Shipping & logistics	OIL-031	Tanker routes + AIS + Worldscale + chokepoints (new sub-vertical)

OIL-031 vs OIL-024/025/027: Pipelines move oil between fixed endpoints. OIL-031 moves oil across oceans via 6-class tanker fleet on 7 route regions. Use pipeline SKUs for fixed-asset ML, OIL-031 for floating- asset chartering / voyage ML.

OIL-031 vs OIL-028 (storage): OIL-028 simulates stationary tank inventory dynamics. OIL-031 simulates moving cargo dynamics. Natural integration: OIL-028 + OIL-031 for complete petroleum logistics — storage → vessel loading → voyage → discharge → storage.

OIL-031 + OIL-029 (crude prices) → freight rates ↔ crude prices for shipping-cost-aware quant trading strategies.

OIL-031 + OIL-030 (supply-demand) → tanker traffic patterns ↔ regional demand for trade flow ML.

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Full product

The full OIL-031 dataset ships at 5,000 vessels × 12,000 routes × 45,000 voyages × 730 days × 24-hour AIS (prod mode) producing tens of millions of rows with realistic trade-flow-weighted cargo grade distributions (75% WTI/Brent/Arab Light/Basrah on VLCC), regime- clustered freight volatility per Baltic Exchange BDTI/BCTI methodology, realistic spot-dominant charter party mix (60% spot per Clarkson), deterministic Beaufort-grade wave-wind coupling, AIS-spoofing dark- fleet event generation, and chokepoint traffic-share-weighted distribution per EIA chokepoint data — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

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Citation

@dataset{xpertsystems_oil031_sample_2026,
  title  = {OIL-031: Synthetic Shipping & Logistics Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil031-sample}
}

Generation details

• Sample version : 1.0.0
• Random seed : 42
• Generated : 2026-05-23 13:10:57 UTC
• Vessels : 250
• Routes : 500
• Voyages : 2500
• Simulation days : 180
• AIS event step : 24 hours
• Tanker classes : 6 (VLCC, Suezmax, Aframax, LR2, MR, Handy)
• Real ports : 20 baseline (Houston, Corpus Christi, Rotterdam, Singapore, Fujairah, Ras Tanura, Basrah, Kuwait, Ningbo, Qingdao, Mumbai, Jamnagar, Ulsan, Yokohama, Antwerp, Trieste, Ceyhan, Bonny, Luanda, Santos)
• EIA chokepoints : 7 (Suez, Panama, Hormuz, Bab el- Mandeb, Malacca, Turkish Straits, Cape of Good Hope)
• Cargo grades : 10 (WTI, Brent, Arab Light, Basrah Medium, Bonny Light, Maya crudes + Diesel, Jet Fuel, Gasoline, Naphtha products)
• Delay types : 5 (weather, port_congestion, chokepoint, mechanical_ or_operational, rare_event_disruption)
• Charter party : 4 (spot, time_charter, COA, voyage_charter)
• Calibration basis : BIMCO, INTERTANKO, Worldscale Association, Baltic Exchange, Clarkson Research, VesselsValue, IMO, MARPOL Annex VI, SOLAS, EIA Chokepoints, UNCTAD, IACS, Lloyd's List Intelligence, AIS per IMO Res. A.917, Beaufort Wind Scale
• Overall validation: 100.0/100 — Grade A+