README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- midstream
- shipping
- tanker
- logistics
- ais
- chokepoints
- worldscale
- bimco
- xpertsystems
pretty_name: "OIL-031 — Synthetic Shipping & Logistics Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# 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.

---

## 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.

---

## 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)

---

## 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**.

---

## 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`.

---

## Loading

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

Or with pandas:

```python
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
```

---

## 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.

---

## 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**:
   ```python
   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.

---

## 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 |

---

## 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.

---

## 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**

---

## Citation

```bibtex
@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+