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
- downstream
- inventory
- tank-farm
- spr
- eia-padd
- seasonal
- xpertsystems
pretty_name: "OIL-033 — Synthetic Crude & Product Inventory Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# 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.

---

## 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.

---

## 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 - |utilization - 72|) — deterministic V-shape around 72% optimal target per API 2350 overfill / EIA mid-fill operational target; expected near-perfect inverse correlation between absolute deviation from 72% and optimization score. |
| 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)

---

## 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.

---

## 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.

---

## Loading

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

Or with pandas:

```python
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'))
```

---

## 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.

---

## 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**:
   ```python
   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.

---

## 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 |

---

## 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

---

## 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**

---

## Citation

```bibtex
@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+