xpertsystems/oil033-sample

OIL-033 — Synthetic Crude & Product Inventory Dataset (Sample)

SKU: OIL033-SAMPLE · Vertical: Oil & Gas / Storage & Inventory License: CC-BY-NC-4.0 (sample) · Schema version: oil033.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise crude oil and refined product inventory dataset for EIA-style weekly inventory forecasting, tank farm utilization optimization, SPR operations modeling, shortage risk classification, seasonal demand pattern ML, disruption event prediction, and PADD-regional inventory analytics. The sample covers 235 storage sites across 5 EIA PADD-aligned regions (USGC, Midwest, East Coast, West Coast, Cushing) and 5 storage types (Tank Farm, SPR, Refinery, Terminal, Floating) over 365 days of daily operations, with 257,936 rows across 6 tables.

OIL-033's distinctive features: (1) mass-balance-coupled daily inventory with EIA-grade dynamics; (2) seasonal inflow/outflow with proper sinusoidal modulation; (3) 4 real DOE SPR sites (Bryan Mound, Big Hill, West Hackberry, Bayou Choctaw); (4) feature-coupled labels with both binary shortage_risk (util < 35%) and V-shape optimization_score around 72% optimal target.

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What's in the box

File	Rows	Cols	Description
inventory_master.csv	235	5	Storage site catalog: 5 EIA PADD regions × 5 types × capacity (0.5-10M bbl) + working_capacity_pct
crude_inventory_levels.csv	85,775	6	DAILY MASS-BALANCE-COUPLED inventory with seasonal inflow/outflow + utilization%
refined_product_inventory.csv	85,775	5	Per-site daily gasoline + diesel + jet fuel inventory levels
spr_operations.csv	365	4	4 REAL DOE SPR sites + release events + reserve level (post-2022 ~648M bbl)
disruption_events.csv	11	3	5-class disruption taxonomy: Hurricane / Pipeline Outage / Refinery Fire / Import Disruption / Tank Failure
inventory_labels.csv	85,775	4	FEATURE-COUPLED ML labels: binary shortage_risk (util<35%) + V-shape optimization_score

Total: 257,936 rows across 6 CSVs, ~13.6 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: EIA Weekly Petroleum Status Report (US crude + product weekly inventory baselines), EIA Petroleum Supply Annual (annual tank farm utilization stats), EIA Storage Capacity Report (regional PADD-level working storage capacity), DOE Strategic Petroleum Reserve operations data (4 actual Gulf Coast sites), API 650 (Welded Tanks for Oil Storage), API 653 (Tank Inspection / Repair), API 575 (Tank Inspection), API 2350 (Overfill Protection), PADD classifications (EIA's PADD I-V regional taxonomy: East Coast, Midwest, USGC, Rocky Mountain, West Coast), OECD Oil Stocks (IEA OECD commercial stocks coverage), JODI (Joint Organisations Data Initiative World Database), EPA AP-42 (vapor emissions from storage), NFPA 30 (Flammable and Combustible Liquids Code).

Sample run (seed 42, n_sites=235, days=365):

#	Metric	Observed	Target	Tolerance	Status	Source
1	avg utilization pct	78.6737	75.0	±12.0	✓ PASS	EIA Petroleum Supply Annual + EIA Weekly Petroleum Status Report — typical US tank farm utilization (65-90% normal operating range; 50% under-utilized; 95%+ overfill risk per API 2350; sample reflects mid-fill operational target)
2	avg capacity million bbl	4.9001	5.0	±1.5	✓ PASS	API 650 + EIA Storage Capacity Report (PADD-level) — typical mixed-portfolio tank capacity (0.5-10M bbl range; ~5M mean for mixed Terminal/Refinery/Tank Farm operations; Cushing OK individual tanks ~15-20M)
3	avg working capacity pct	0.7936	0.8	±0.06	✓ PASS	API 653 + API 575 + EIA Storage Capacity Report — working capacity is the usable fraction of total capacity (65-95% range; ~80% mean accounting for inactive shell heel + vapor space + sludge layer per API 575 inspection methodology)
4	avg inflow bpd	250097.4841	250000.0	±30000.0	✓ PASS	EIA Weekly Petroleum Status Report receipts data — typical tank receipts ~250K bpd reflecting transmission pipeline + crude-by-rail + waterborne imports; varies by terminal size (50K-500K bpd operational range)
5	avg outflow bpd	244878.2809	245000.0	±30000.0	✓ PASS	EIA Weekly Petroleum Status Report disposition data — typical tank disposition slightly below receipts in steady-state operations (~245K bpd; difference reflects small net build during sample period)
6	avg spr reserve million bbl	648.2198	650.0	±80.0	✓ PASS	DOE Strategic Petroleum Reserve historical inventory — ~648M mean reflects post-2022 SPR drawdown era (peaked ~727M in 2009; reduced to ~350M after 2022 exchange; rebuilding 2024+; 4 Gulf Coast salt domes)
7	disruption event rate per day	0.0301	0.03	±0.02	✓ PASS	EIA + DOE supply disruption tracking — typical daily disruption event rate (~3% of days have meaningful supply-affecting events including hurricanes, pipeline outages, refinery fires per US oil infrastructure incident history). Wider tolerance accommodates binomial sampling variance at 365-day horizon: with p=0.03 and n=365, expected events ~11 with σ ~3.3 (rate σ ~0.009).
8	utilization shortage risk correlation	-0.5216	-0.45	±0.15	✓ PASS	Generator formula: shortage_risk = (utilization < 35) — expected strong inverse correlation between utilization and binary shortage risk. Validates feature-coupled label per EIA tight-inventory tracking methodology.
9	deviation from optimal optimization correlation	-1.0000	-1.0	±0.05	✓ PASS	Generator formula: optimization_score = clip(0, 100, 100 -
10	region diversity entropy	0.9959	0.95	±0.06	✓ PASS	5-region taxonomy per EIA PADD classifications (USGC=PADD III, Midwest=PADD II, East Coast=PADD I, West Coast=PADD V, Cushing=pricing hub) — 5-class 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

inventory_master.csv — 5-region × 5-type matrix per EIA PADD:

Region	EIA PADD	Real-World Anchor
USGC	PADD III	Gulf Coast — largest US refining/export hub
Midwest	PADD II	Cushing + refinery cluster
East Coast	PADD I	New England + Mid-Atlantic
West Coast	PADD V	California + Pacific Northwest
Cushing	(sub-PADD II)	NYMEX delivery hub, ~90M bbl capacity

5 storage types per industry taxonomy:

Storage Type	Use Case	API Code
Tank Farm	Crude oil storage clusters	API 650
SPR	DOE Strategic Petroleum Reserve	DOE
Refinery	Refinery feedstock + product storage	API 650/620
Terminal	Pipeline/marine terminal	API 650
Floating	Floating roof crude tanks (vapor min)	API 650

crude_inventory_levels.csv — mass-balance-coupled daily inventory (the real physics in this SKU):

inventory_t+1 = clip(0, capacity, inventory_t + inflow_t − outflow_t + disruption_t) seasonal(d) = 1 + 0.15 · sin(2π · day_of_year / 365) inflow_t = N(250000, 50000) × seasonal(d) bpd outflow_t = N(245000, 45000) × seasonal(d) bpd disruption_t = U(-400000, 400000) with prob 0.005

The sample's seasonal coupling (day_of_year ↔ inflow r ≈ -0.36, expected seasonal ↔ inflow r ≈ +0.47) validates the sinusoidal modulation.

spr_operations.csv — 4 real DOE Strategic Petroleum Reserve sites:

Site	State	Real Capacity	Notes
Bryan Mound	Texas	~245M bbl	Largest SPR site, near Freeport
Big Hill	Texas	~160M bbl	Beaumont area
West Hackberry	Louisiana	~227M bbl	Near Lake Charles
Bayou Choctaw	Louisiana	~76M bbl	Baton Rouge area

Sample reserve level mean ~648M bbl matches post-2022 SPR drawdown era (peaked ~727M in 2009; reduced to ~350M after 2022 sales; rebuilding 2024+).

inventory_labels.csv — feature-coupled ML labels:

shortage_risk = 1 if utilization_pct < 35 else 0 optimization_score = clip(0, 100, 100 - |utilization_pct - 72|)

The sample's deviation from 72% optimal ↔ optimization_score r = -1.000000 (deterministic V-shape coupling per generator formula) — near-perfect feature-coupled label validation. The shortage_risk binary classifier shows r ≈ -0.52 with utilization, validating EIA tight-inventory threshold.

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Suggested use cases

1. Inventory time-series forecasting — predict inventory_bbl from inflow/outflow features per mass balance accumulation. Strong physics signal — within-site dynamics deterministic.
2. Binary shortage risk classification — predict shortage_risk (util<35%) from inventory + region + storage_type features per EIA tight-inventory tracking methodology. Strong physics coupling.
3. V-shape optimization regression — predict optimization_score from |utilization - 72| per API 2350 / EIA mid-fill target. Near-deterministic — models can learn exact V-shape.
4. Seasonal demand pattern ML — predict seasonal inflow/outflow patterns from day_of_year features per EIA Weekly Petroleum.
5. 5-class disruption event classification — multi-class classifier on event_type (Hurricane / Pipeline Outage / Refinery Fire / Import Disruption / Tank Failure).
6. SPR operations forecasting — predict SPR release events from reserve_level + global market features (extend with OIL-029 prices).
7. Regional PADD inventory analytics — aggregate inventory by EIA PADD region per EIA Weekly Petroleum Status methodology.
8. 5-class storage type classification — predict storage_type from capacity + working_capacity features.
9. Daily inflow/outflow regression — predict inflow_bpd / outflow_bpd from seasonal + site features.
10. Multi-table relational ML — entity-resolution across the 6 tables via inventory_id + timestamp for joinable training pipelines.

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Loading

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil033-sample", data_files="crude_inventory_levels.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
master  = pd.read_csv("hf://datasets/xpertsystems/oil033-sample/inventory_master.csv")
crude   = pd.read_csv("hf://datasets/xpertsystems/oil033-sample/crude_inventory_levels.csv")
refined = pd.read_csv("hf://datasets/xpertsystems/oil033-sample/refined_product_inventory.csv")
spr     = pd.read_csv("hf://datasets/xpertsystems/oil033-sample/spr_operations.csv")
labels  = pd.read_csv("hf://datasets/xpertsystems/oil033-sample/inventory_labels.csv")

# Multi-table feature engineering for ML:
crude_agg = crude.groupby('inventory_id').agg(
    avg_inventory=('inventory_bbl', 'mean'),
    avg_utilization=('utilization_pct', 'mean'),
    net_flow_std=('inflow_bpd', lambda x: x.std() - 0)  # placeholder
).reset_index()

joined = (master
    .merge(crude_agg, on='inventory_id')
    .merge(labels.groupby('inventory_id').agg(
        avg_shortage=('shortage_risk', 'mean'),
        avg_opt=('optimization_score', 'mean')
    ).reset_index(), on='inventory_id'))

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Reproducibility

All generation is deterministic via the integer seed parameter (driving np.random.seed). 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 inventory ML research, not for live tank farm operations or EIA forecasting. Several notes:

1. No region/storage-type conditioning on capacity. All sites use randint(500K, 10M) bbl regardless of being SPR (real ~150M bbl), Cushing terminal (real ~15-20M), or floating tank (typically 500K-2M). For type-conditioned ML, normalize by storage type scale:

   type_scales = {'SPR': 150e6, 'Cushing': 18e6, 'Refinery': 8e6,
                   'Terminal': 5e6, 'Floating': 1e6, 'Tank Farm': 6e6}
   

2. No region/storage-type conditioning on inflow/outflow. All sites use ~250K bpd inflow/outflow regardless of being SPR (typically very low daily flux) or refinery (250K-500K bpd realistic). For flux- conditioned ML, filter to single storage_type before training.

3. Inflow ≈ outflow nearly balanced (250K vs 245K mean). Net flow std 67K bpd dominates the 5K mean drift, so inventory exhibits slow random walk with capacity bounds. For mass-balance ML, focus on near-term dynamics rather than expecting trend-following behavior.

4. Refined products are NOT joined to crude utilization. Gasoline, diesel, jet fuel are independently sampled from N(120K, 25K), N(95K, 20K), N(60K, 15K) per site/day without coupling to crude inflow or refinery throughput. Real refined product inventory tracks refinery utilization with ~1-2 week lag. For product-yield ML, derive your own coupling or use the full product.

5. SPR site distribution is per-day random rather than per-event. The generator samples spr_site independently each day, so the 4 SPR sites appear roughly uniform (24-27% each) over the 365-day period even though release events are rare. For SPR-site-specific ML, filter to release events only (release_rate_bpd > 0).

6. SPR reserve level changes very little (647-650M range across 365 days) because only ~1% of days trigger releases. Real SPR inventory changes more dramatically (120M bbl reduction in 2022). For SPR drawdown ML, use the full product or augment with historical 2022 release events.

7. Disruption magnitude includes positive values U(-400K, +400K), which is physically odd (disruptions should typically reduce supply). The sample treats positive values as "anti-disruptions" (e.g., emergency receipts). For supply-shock ML, filter to negative disruption values or use abs(disruption) as severity.

8. Capacity ↔ utilization is uncoupled (r ≈ 0.04). Real markets show smaller tanks have more variable utilization (higher turnover cycles relative to capacity). For capacity-conditioned ML, use normalized utilization (e.g., daily change / capacity).

9. Working capacity % is uniform U(0.65, 0.95) without conditioning on storage type. Real SPR working capacity is ~95%+ (low heel), while floating roof tanks are ~80% (shell heel + sludge). For type-specific ML, derive type-conditioned working capacity.

10. Inventory mean 78.67% is elevated vs EIA optimal 72% target. The generator's random walk drifts upward over 365 days due to inflow - outflow = 5K bpd net positive. For optimal-target ML, filter to days near 72% or augment with historical EIA reference levels.

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Where physics IS strong (use these for ML)

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

Signal	Result	Source
Deviation from 72% ↔ optimization score	r = -1.000	Generator V-shape formula (deterministic)
Utilization ↔ shortage risk	r = -0.522	Generator binary threshold
Expected seasonal ↔ inflow	r = +0.466	sin(2π·day/365) modulation
Mass-balance inventory accumulation	Deterministic per site	Tank conservation law
Day of year ↔ inflow	r = -0.363	Seasonal phasing
SPR reserve mean	~648M bbl	DOE post-2022 drawdown

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Cross-references to other XpertSystems OIL SKUs

This SKU is the second storage/inventory SKU in the catalog — complementing OIL-028 (tank operations) with multi-site portfolio + SPR + seasonal dynamics:

Storage layer	SKU	Focus
Tank operations	OIL-028	API 650 mass-balance inventory + 6 product types × 3 tank types (single-site granularity)
Portfolio inventory	OIL-033	EIA PADD regions + 4 DOE SPR sites + seasonal dynamics + feature-coupled labels (this SKU)

OIL-033 vs OIL-028: OIL-028 simulates individual tank operations (per-tank hourly mass balance, product types, integrity). OIL-033 simulates portfolio-level inventory across multiple PADD regions + SPR sites with daily granularity + seasonal patterns. Use OIL-028 for single-tank ML, OIL-033 for regional/national inventory analytics.

Natural integrations:

• OIL-033 + OIL-029 → EIA inventory levels ↔ WTI prices for fundamentals- driven trading
• OIL-033 + OIL-030 → portfolio inventory ↔ global supply/demand
• OIL-033 + OIL-028 → portfolio rollup ↔ individual tank operations
• OIL-033 + OIL-031 → inventory levels ↔ tanker arrivals at terminals

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Full product

The full OIL-033 dataset ships at 5,000 storage sites × 730 days (prod mode) producing tens of millions of rows with EIA PADD-tier-weighted capacity (SPR sites ~150M bbl, Cushing ~18M, refineries ~8M), type- conditioned inflow/outflow rates (SPR ~50K bpd vs refinery ~400K bpd), realistic SPR drawdown events (2008/2011/2022 historical scenarios), crude-refined product coupling via refinery throughput ML linkages, signed disruption events (negative for outages only), multi-year seasonal cycles with weather-driven anomalies, and PADD-aggregated EIA weekly inventory reports matching real EIA Friday release schedule — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

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Citation

@dataset{xpertsystems_oil033_sample_2026,
  title  = {OIL-033: Synthetic Crude & Product Inventory Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil033-sample}
}

Generation details

• Sample version : 1.0.0
• Random seed : 42
• Generated : 2026-05-23 13:46:01 UTC
• Storage sites : 235
• Simulation days : 365 (1 year)
• Regions : 5 (USGC, Midwest, East Coast, West Coast, Cushing per EIA PADD)
• Storage types : 5 (Tank Farm, SPR, Refinery, Terminal, Floating)
• SPR sites : 4 (Bryan Mound, Big Hill, West Hackberry, Bayou Choctaw — real DOE Gulf Coast salt domes)
• Disruption types : 5 (Hurricane, Pipeline Outage, Refinery Fire, Import Disruption, Tank Failure)
• Capacity range : 500K - 10M bbl (API 650 mixed portfolio)
• Calibration basis : EIA Weekly Petroleum Status, EIA Petroleum Supply Annual, EIA Storage Capacity Report, DOE SPR, API 650, API 653, API 575, API 2350, PADD classifications, OECD Oil Stocks, JODI, EPA AP-42, NFPA 30
• Overall validation: 100.0/100 — Grade A+