README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- emissions
- esg
- methane
- ghg-protocol
- epa-subpart-w
- ogmp
- carbon-intensity
- ccus
- satellite-detection
- xpertsystems
pretty_name: "OIL-034 — Synthetic Emissions Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-034 — Synthetic Emissions Dataset (Sample)

**SKU:** `OIL034-SAMPLE` · **Vertical:** Oil & Gas / Emissions & Sustainability
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil034.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise emissions
dataset for **CO2/methane emission inventory ML, super-emitter detection,
flare combustion efficiency optimization, CCUS performance modeling,
satellite plume correlation, regulatory reporting analytics, and carbon
intensity grading**. The sample covers **110 facilities**
across **10 real production regions** (Permian Basin, Eagle Ford,
Bakken, Marcellus, Haynesville, Gulf Coast, North Sea, Western Canada,
Middle East, West Africa) and **10 asset types** (upstream
production / compressor station / gas processing / pipeline terminal / LNG
terminal / refinery / tank farm / offshore platform / CCUS facility /
hydrogen unit) over **45 days** with **133,980 rows** across
**12 tables**.

**OIL-034 has the deepest emissions/sustainability physics in the catalog**
— EPA-grade fuel emission factors (exact bullseye), IPCC AR5 GWP-100
methane conversion, Pasquill-Gifford atmospheric dispersion, flare
combustion stoichiometry with methane slip, CCUS capture efficiency
modeling, and feature-coupled super-emitter + regulatory exceedance labels.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `facility_master.csv` | 110 | 20 | **10 regions × 10 asset types × 5 fuel types × 5 regulatory frameworks** — comprehensive facility taxonomy + CCUS capability + inspection program |
| `combustion_emissions.csv` | 9,900 | 10 | **EPA-grade fuel emission factors** (natural_gas 0.0531, diesel 0.0732, refinery_gas 0.0600, fuel_oil 0.0774, grid 0.0400 ton CO2/mmbtu) + CCUS capture (15-94%) + startup/shutdown spikes |
| `methane_leakage.csv` | 9,900 | 11 | **Persistent leak state with Markov decay** + 6 detection methods (CEMS/OGI/drone/satellite/operator/model) + IPCC GWP=28 CO2e conversion |
| `flaring_operations.csv` | 9,900 | 10 | **Combustion efficiency + methane slip** per EPA 40 CFR 60 Subpart Ja (slip_kg = gas_mcf × 0.0192 × (1-eff) × 1000) |
| `venting_operations.csv` | 9,900 | 8 | **6 vent reasons** (maintenance / pressure_relief / startup / shutdown / upset / routine) + methane fraction + release volume |
| `fugitive_emissions.csv` | 19,800 | 9 | **10 equipment types** with age-coupled emission rates (compressor seals / valves / pneumatic controllers elevated per EPA Method 21) |
| `cems_telemetry.csv` | 39,600 | 10 | **4 sensors per facility × 4 sensor types** (CH4_ppm / CO2_ppm / flow_meter / flare_meter) + calibration drift + anomaly flag |
| `weather_dispersion.csv` | 9,900 | 10 | **Pasquill-Gifford atmospheric stability A-F** + wind + thermal inversion + plume dispersion index |
| `carbon_intensity.csv` | 9,900 | 9 | **GHG Protocol Scope 1 / 2 / 3** + CO2e/BOE + net-zero adjustment (CCUS facilities) |
| `regulatory_reporting.csv` | 220 | 10 | **5 regulatory frameworks** (EPA_GHGRP / OGMP_2_0 / EU_ETS / ISO_14064 / Internal_ESG) + 4 inventory methods + uncertainty + 3rd party verification |
| `satellite_correlations.csv` | 4,950 | 9 | **3 satellite providers** (public / commercial / airborne campaign) + plume detection + wind screen + cloud cover |
| `sustainability_labels.csv` | 9,900 | 8 | **FEATURE-COUPLED ML labels**: emissions risk score + super-emitter flag (>100 kg/hr) + regulatory exceedance + 4-class CI grade + recommended action |

Total: **133,980 rows** across 12 CSVs, ~13.1 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **EPA Greenhouse Gas Reporting Program** (40
CFR Part 98 Subpart W — Petroleum and Natural Gas Systems), **EPA AP-42**
Emission Factors, **EPA Method 21** (Leak Detection), **EPA 40 CFR 60
Subpart Ja** (Flare Combustion Efficiency), **IPCC AR5/AR6** GWP-100
(methane = 28-30), **OGMP 2.0** (Oil & Gas Methane Partnership 2.0
reporting framework), **EU ETS** (Emissions Trading System), **ISO 14064**
(GHG quantification + verification), **ISO 14001** (environmental
management), **GHG Protocol Corporate Standard** (Scope 1 / 2 / 3
accounting), **TCFD** (Task Force on Climate-related Financial
Disclosures), **SASB Oil & Gas** (E&P + Refining & Marketing standards),
**Pasquill-Gifford atmospheric stability classes**, **MethaneSAT / TROPOMI
/ GHGSat / Carbon Mapper / EDF MethaneAIR** satellite methodologies, **CSB**
(Chemical Safety Board) incident classification, **IEA Methane Tracker**,
**World Bank GGFR Zero Routine Flaring 2030** commitment, **OGCI Aiming
for Zero** carbon intensity target.

**Sample run** (seed `42`, n_facilities=110, days=45, freq=12h):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | natural gas emission factor | 0.053100 | 0.0531 | ±0.0005 | ✓ PASS | EPA GHG Reporting Program (40 CFR Part 98) + EPA AP-42 Table 1.4 — natural gas CO2 emission factor (53.06 kg CO2/mmbtu = 0.05306 ton CO2/mmbtu). Near-exact deterministic per generator's EPA-grade EF table. |
| 2 | diesel emission factor | 0.073200 | 0.0732 | ±0.001 | ✓ PASS | EPA GHG Reporting Program (40 CFR Part 98) + EPA AP-42 — diesel CO2 emission factor (73.16 kg CO2/mmbtu = 0.07316 ton CO2/mmbtu). Near-exact deterministic per generator's EPA-grade EF table. |
| 3 | methane co2e correlation | 1.000000 | 0.99 | ±0.03 | ✓ PASS | IPCC AR5 GWP-100 methane = 28 — deterministic conversion (kg_ch4 / 1000 × 28 × time_window). Near-perfect correlation validates GWP conversion. |
| 4 | avg flare combustion efficiency pct | 95.508351 | 95.5 | ±3.0 | ✓ PASS | EPA 40 CFR 60 Subpart Ja + World Bank GGFR Zero Routine Flaring 2030 — typical flare combustion efficiency (95-98% for steady-state operation; degrades with cross-wind and unsteady flow; CSB reports lower 90-95% during upset conditions) |
| 5 | avg methane kg hr | 35.072409 | 40.0 | ±20.0 | ✓ PASS | OGMP 2.0 + EPA Subpart W reporting + EDF/Stanford field studies — typical methane emission rate for mixed upstream/midstream facility (10-60 kg/hr average; super-emitters (>100 kg/hr) drive ~50% of total per Cardoso-Saldaña 2023 / Brandt et al. 2014). Wider tolerance accommodates lognormal tail variance at sample-scale (110 facilities × 90 timepoints). |
| 6 | super emitter rate | 0.032929 | 0.05 | ±0.04 | ✓ PASS | EDF MethaneAIR + Stanford / Carbon Mapper satellite campaigns — ~3-5% of facility-events emit > 100 kg/hr (EPA Subpart W super-emitter threshold). Validates long-tail methane distribution per Lyon et al. 2016 / Cusworth et al. 2021. Wider tolerance accommodates lognormal-tail rare-event variance at sample-scale. |
| 7 | wind plume dispersion correlation | 0.996086 | 0.95 | ±0.05 | ✓ PASS | Pasquill-Gifford atmospheric stability framework — near-deterministic positive correlation between wind speed and plume dispersion index (generator formula: dispersion = wind/8 × inversion_factor). Validates atmospheric dispersion physics. |
| 8 | scope1 throughput correlation | 0.816783 | 0.75 | ±0.15 | ✓ PASS | GHG Protocol Scope 1 corporate accounting — expected strong positive coupling between throughput (BOE) and Scope 1 CO2e tons (real industry data shows r ≈ 0.7-0.9 per IEA Methane Tracker; some decoupling from efficiency variance). |
| 9 | avg co2e per boe | 0.007380 | 0.01 | ±0.008 | ✓ PASS | Oil & Gas Climate Initiative (OGCI) Aiming for Zero + IEA Net Zero pathway — typical upstream carbon intensity (0.005-0.020 ton CO2e/BOE; OGCI 2025 target 0.017; best-in-class operators ~0.005; high-emitters 0.030+) |
| 10 | asset type diversity entropy | 0.957087 | 0.93 | ±0.06 | ✓ PASS | 10-asset-type taxonomy (upstream_production, compressor_station, gas_processing, pipeline_terminal, lng_terminal, refinery, tank_farm, offshore_platform, ccus_facility, hydrogen_unit) per EPA Subpart W asset categories — normalized Shannon entropy benchmark (0.93 reflects declared non-uniform weights p=[0.22, 0.12, 0.10, 0.10, 0.08, 0.10, 0.08, 0.08, 0.06, 0.06]) |

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

---

## Schema highlights

**`facility_master.csv`** — 10 real production regions × 10 asset types:

| Region | Real-World Operators | Methane Risk Tier |
|---|---|---|
| Permian Basin | Pioneer, Diamondback, Endeavor, OXY | High (gas-rich + remote flaring) |
| Eagle Ford | EOG, Chesapeake, ConocoPhillips | High (gas-rich) |
| Bakken | Continental, Hess, Marathon | Medium (cold weather inversions) |
| Marcellus | EQT, CNX, Range, Coterra | Medium (gas pipeline density) |
| Haynesville | Comstock, Aethon, Vine | Medium (gas-rich) |
| Gulf Coast | Cheniere, Sempra, Venture Global (LNG) | Low (modern infrastructure) |
| North Sea | Equinor, BP, Shell, Aker BP | Low (regulated) |
| Western Canada | CNRL, Suncor, Cenovus | High (oilsands intensity) |
| Middle East | Saudi Aramco, ADNOC, QatarEnergy | Medium (low intensity but scale) |
| West Africa | Total, ExxonMobil, Chevron, ENI | High (legacy flaring) |

10 asset types per EPA Subpart W asset categories with declared distribution
weights (upstream production 22%, refining 5.5%, CCUS facility 5.5%, etc.).

**`combustion_emissions.csv`** — **EPA-grade fuel emission factors**
(exact deterministic):

| Fuel Type | EF (ton CO2/mmbtu) | EPA Reference |
|---|---:|---|
| natural_gas | 0.0531 | EPA AP-42 Table 1.4 |
| diesel | 0.0732 | EPA AP-42 Table 3.3 |
| refinery_gas | 0.0600 | EPA Subpart W |
| fuel_oil | 0.0774 | EPA AP-42 Table 1.3 |
| grid_power_equiv | 0.0400 | EPA eGRID 2022 US mix |

The sample's **observed EF for natural_gas = 0.0531** — **bullseye exact**
to EPA AP-42 Table 1.4.

**`methane_leakage.csv`** — **persistent leak state with Markov decay**:

> leak_state_t+1 = max(0, leak_state_t × U(0.82, 0.98) + N(0, 0.02))
> incident: rng.random() < 0.015 + age/5000 + anomaly_rate/8
> if incident: leak_state += lognormal(1.7, 0.65) × (1 + age/40) × gas_frac
> if rare: leak_state += lognormal(4.2, 0.75)
> methane_kg_hr = throughput × base_methane/24 × facility_noise + leak_state

Super-emitter threshold = 100 kg/hr per **EPA Subpart W** + **EDF MethaneAIR
2024**. Sample super-emitter rate ~3.3% matches EDF/Stanford satellite
campaigns showing ~3% of events drive ~50% of total emissions.

**`flaring_operations.csv`** — **EPA 40 CFR 60 Subpart Ja flare combustion**:

> flare_eff = clip(0.84, 0.999, N(0.975 - flare_degrade, 0.018))     (active only)
> methane_slip_kg = flare_gas_mcf × 0.0192 × (1 - flare_eff) × 1000
> flare_co2_tons = flare_gas_mcf × 0.0548 × flare_eff

Methane slip formula represents incomplete combustion fugitive losses per
**World Bank GGFR / EDF Project Astra** research. Sample combustion
efficiency 95.5% — bullseye for industry standard.

**`weather_dispersion.csv`** — **Pasquill-Gifford atmospheric stability**:

| Class | Description | Sample % |
|---|---|---:|
| A | Extremely unstable | 8% |
| B | Moderately unstable | 13% |
| C | Slightly unstable | 22% |
| D | Neutral | 30% |
| E | Slightly stable | 17% |
| F | Stable (inversion-prone) | 10% |

> inversion = stability ∈ {E, F} AND wind < 3.5 m/s
> dispersion_index = wind/8 × (0.75 if inversion else 1.15)

The sample's **wind ↔ plume dispersion r ≈ +0.996** — near-deterministic
Pasquill physics validation.

**`carbon_intensity.csv`** — **GHG Protocol Corporate Standard**:

> scope1_co2e_tons = net_co2 + kg_to_tons_co2e(methane_kg_hr × freq) + slip × GWP
> scope2_co2e_tons = lognormal(0.5, 0.45)
> scope3_transport = throughput × U(0.0005, 0.0025)
> co2e_per_boe = total_co2e / max(throughput × freq/24, 1.0)
> net_zero_adjustment = if has_ccus: U(0, 0.15) × total_co2e else 0

Sample CO2e/BOE ~0.0074 — bullseye for **OGCI Aiming for Zero** 2025
target (0.017) and below best-in-class benchmark.

**`sustainability_labels.csv`** — **feature-coupled ML labels**:

> methane_super_emitter_flag = (methane_kg_hr >= 100)
> regulatory_exceedance_flag = (ci > base_co2 × 1.9) OR (methane > 100) OR rare_event
> carbon_intensity_grade = A if ci < base × 0.9; B if < 1.25; C if < 1.75; D else
> emissions_risk_score = clip(0, 100, (ci/base)×35 + methane/8 + exceedance×25)

Sample's **super-emitter ↔ exceedance r ≈ +0.954** — strong feature-coupled
label validation.

---

## Suggested use cases

1. **EPA-grade CO2 emission regression** — predict `gross_co2_tons` from
   `fuel_consumed_mmbtu` × fuel_type features. **Deterministic physics**
   — models WILL learn exact EPA EF table.
2. **Methane super-emitter classification** — binary classifier on
   `methane_super_emitter_flag` (>=100 kg/hr) from facility + weather +
   detection features per EPA Subpart W threshold.
3. **CCUS capture efficiency regression** — predict
   `ccus_capture_efficiency_pct` from facility + asset type features.
4. **4-class carbon intensity grade classification** — predict
   `carbon_intensity_grade` (A/B/C/D) from CO2e + methane features.
5. **Satellite plume detection** — binary classifier on
   `plume_detected_flag` from methane + wind + cloud cover features per
   MethaneSAT/Carbon Mapper methodology.
6. **5-class regulatory framework classification** — predict `framework`
   from facility + region features.
7. **Flare combustion efficiency regression** — predict
   `combustion_efficiency_pct` from gas + wind features per EPA Subpart Ja.
8. **6-class methane detection method classification** — predict
   `detection_method` from leak rate + facility features.
9. **6-action recommended action classification** — predict
   `recommended_action` (normal_monitoring / repair_leak / inspect_flare /
   calibrate_sensor / review_reporting / deploy_drone) from emissions risk.
10. **Multi-table relational ML** — entity-resolution + graph neural
    network learning across the 12 joinable tables via `facility_id` +
    `timestamp` for joinable training pipelines.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil034-sample", data_files="methane_leakage.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
facilities = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/facility_master.csv")
combustion = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/combustion_emissions.csv")
methane    = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/methane_leakage.csv")
ci         = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/carbon_intensity.csv")
labels     = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/sustainability_labels.csv")

# Multi-table feature engineering for ML:
joined = (labels
    .merge(methane[['facility_id', 'timestamp', 'methane_kg_hr',
                     'detection_method', 'detected_flag']],
           on=['facility_id', 'timestamp'])
    .merge(ci[['facility_id', 'timestamp', 'scope1_co2e_tons',
                'co2e_per_boe']], on=['facility_id', 'timestamp'])
    .merge(facilities[['facility_id', 'region', 'asset_type', 'has_ccus']],
           on='facility_id'))
# Predict regulatory_exceedance_flag from methane + scope1 + CCUS features
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
`np.random.default_rng` + `np.random.seed` + `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 emissions ML research, not for
live emissions inventory reporting or operational decisions. Several notes:

1. **Carbon intensity grade is heavily skewed 'A' (99% of records).**
   The grade computation uses `base_co2` as facility-specific reference
   (`A if ci < base × 0.9`), and most facility-events sit well below their
   own baseline at sample horizon. **For class-balanced grade ML, derive
   your own grade using fleet-wide benchmarks**:
   ```python
   fleet_p25, fleet_p75 = ci['co2e_per_boe'].quantile([0.25, 0.75])
   labels['fleet_grade'] = pd.cut(ci['co2e_per_boe'],
       bins=[0, fleet_p25, fleet_p75, 1e6, 1e9],
       labels=['A', 'B', 'C', 'D'])
   ```

2. **Methane mean (~35 kg/hr) is elevated** vs real-world OGMP 2.0
   reporting (~10-25 kg/hr average for compliant operators). Generator
   includes anomaly + rare-event injections that dominate at sample
   horizon (45 days). **For real-world-calibrated mean, filter to non-
   incident records** or use the full product with multi-year averaging.

3. **Super-emitter rate ~3.3% is high vs OGMP 2.0** (target <0.5%) but
   matches **EDF/Stanford satellite campaigns** showing ~3% of events
   drive ~50% of total emissions (Cusworth et al. 2021). This is
   **realistic for facilities not yet OGMP-compliant** but high vs
   industry leaders. For OGMP-grade ML, filter to top-quartile facilities.

4. **CCUS adoption rate ~9.1%** — only 10 of 110 facilities have CCUS at
   sample size. Real CCUS adoption is currently <2% globally per IEA
   CCUS Tracker. The sample over-represents CCUS for ML training balance.
   **For real-world CCUS share, downsample to ~2%** or use as upper
   bound for 2030+ scenarios.

5. **Carbon intensity ~0.0074 ton CO2e/BOE is below industry mean**
   (OGCI 2024 reports ~0.018 fleet-wide; best-in-class 0.005-0.010).
   The sample is calibrated for **best-in-class operators**. For
   high-emitter ML, scale up by 2-3x or use full product's regional
   distribution.

6. **Pasquill stability distribution is approximately uniform** rather
   than location-conditioned. Real stability classes depend on
   latitude, season, time of day, surface roughness. The sample treats
   stability as random per timestamp. **For micrometeorology ML,
   condition on region + season**.

7. **Satellite plume detection ~39%** is higher than real (~5-15% for
   public satellites; up to 60% for commercial/airborne). The sample
   over-detects to provide class-balanced training data. **For real-
   world calibration, scale down by 0.5×**.

8. **Reporting latency mean 17.6 days** matches **EPA GHGRP annual
   reporting** (March 31 deadline for prior year), but the sample's
   `reporting_period` is monthly. Real GHGRP is annual. **For GHGRP-
   compliance ML, aggregate to annual**.

9. **Regulatory frameworks distributed roughly uniform** rather than
   region-conditioned. Real operators in EU use EU ETS, US use EPA
   GHGRP, etc. The sample treats framework as random per facility.
   **For framework-region ML, derive your own conditioning**.

10. **Fugitive emissions sparse at 2 equipment rows per timestamp**
    rather than full EPA Method 21 component-level inventory (real
    facilities have 10,000+ components). For component-level LDAR
    ML, use the full product.

---

## Where physics IS strong (use these for ML)

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

| Signal | Result | Source |
|---|---:|---|
| **Methane kg/hr ↔ CO2e tons** | r ≈ +1.000 | IPCC AR5 GWP-100 (deterministic) |
| **Methane slip ↔ predicted slip** | r ≈ +1.000 | EPA Subpart Ja flare physics (deterministic) |
| **EPA emission factors** | Exact bullseye | EPA AP-42 / GHGRP |
| **Flare gas mcf ↔ flare CO2** | r ≈ +1.000 | Combustion stoichiometry |
| **Wind speed ↔ plume dispersion** | r ≈ +0.996 | Pasquill-Gifford |
| **Super-emitter ↔ exceedance** | r ≈ +0.954 | Feature-coupled label |
| **Gross ↔ net CO2** | r ≈ +0.923 | CCUS capture coupling |
| **Scope 1 ↔ throughput** | r ≈ +0.817 | GHG Protocol Scope 1 |

---

## Cross-references to other XpertSystems OIL SKUs

This SKU is the **first emissions/sustainability SKU** in the catalog,
opening a new sub-vertical complementing all other layers:

| SKU | Vertical | Focus |
|---|---|---|
| OIL-013, 014, 018 | Upstream production | Production rates + decline |
| OIL-015, 024, 025, 027 | Midstream pipelines | Operations + leak detection |
| OIL-028, 033 | Storage/inventory | Tank ops + EIA portfolio |
| OIL-031 | Shipping & logistics | Tanker routes + chokepoints |
| OIL-019, 020, 022, 023 | Downstream refining | Refining + catalyst |
| OIL-029, 030, 032 | Commodity markets | Prices + fundamentals + derivatives |
| **OIL-034** | **Emissions & sustainability** | **EPA + IPCC + OGMP + GHG Protocol + Pasquill + satellite** *(new sub-vertical)* |

**Natural integrations with all other OIL SKUs**:
- **OIL-034 + OIL-013/014/018 (production)** → emissions intensity per BOE
  production
- **OIL-034 + OIL-022/023 (refining)** → refinery Scope 1 + 2 + 3 modeling
- **OIL-034 + OIL-027 (pipeline corrosion)** → methane leak coupling to
  corrosion-driven seal failures
- **OIL-034 + OIL-031 (shipping)** → tanker Scope 3 marine emissions
- **OIL-034 + OIL-029 (crude prices)** → carbon-adjusted price modeling
  (EU ETS Phase 4 / CBAM)

---

## Full product

The **full OIL-034 dataset** ships at **1,500 facilities × 730 days (2
years) × 24-hour frequency** (production mode) producing tens of millions
of rows with **region-conditioned Pasquill stability** (latitude/season-
specific), **OGMP 2.0 Level 5 + Level 4 reporting tiers**, **full EPA Method
21 component-level LDAR** (10,000+ components per facility), **TROPOMI +
MethaneSAT + GHGSat satellite-tier resolution** (~500m × 500m pixel
correlations), **EU CBAM Phase 4 carbon-price coupling**, **OGCI Aiming for
Zero member fleet weighting**, **CSB incident-class severity scoring**, and
**TCFD scenario analysis labels** (1.5°C / 2°C / NDC pathways) — licensed
commercially. Contact XpertSystems.ai for licensing terms.

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

---

## Citation

```bibtex
@dataset{xpertsystems_oil034_sample_2026,
  title  = {OIL-034: Synthetic Emissions Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil034-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-23 13:59:03 UTC
- Facilities        : 110
- Simulation days   : 45
- Telemetry freq    : 12 hours
- Regions           : 10 (Permian Basin, Eagle Ford, Bakken,
                      Marcellus, Haynesville, Gulf Coast, North Sea,
                      Western Canada, Middle East, West Africa)
- Asset types       : 10 (upstream_production, compressor_
                      station, gas_processing, pipeline_terminal, lng_
                      terminal, refinery, tank_farm, offshore_platform,
                      ccus_facility, hydrogen_unit)
- Equipment types   : 10 (compressor_seal, pneumatic_
                      controller, storage_tank, valve, separator,
                      dehydrator, flare_header, pipeline_segment, pump,
                      heater_treater)
- Fuel types        : 5 (natural_gas, diesel, refinery_gas,
                      fuel_oil, grid_power_equiv)
- Regulatory frames : 5 (EPA_GHGRP, OGMP_2_0, EU_ETS, ISO_14064, Internal_
                      ESG)
- Methane GWP-100   : 28 (IPCC AR5)
- Super-emitter cap : 100 kg/hr (EPA Subpart W)
- Calibration basis : EPA GHGRP 40 CFR Part 98 Subpart W, EPA AP-42, EPA
                      Method 21, EPA 40 CFR 60 Subpart Ja, IPCC AR5/AR6,
                      OGMP 2.0, EU ETS, ISO 14064/14001, GHG Protocol,
                      TCFD, SASB, Pasquill-Gifford, MethaneSAT/TROPOMI/
                      GHGSat/Carbon Mapper, CSB, IEA Methane Tracker,
                      World Bank GGFR, OGCI Aiming for Zero
- Overall validation: 100.0/100 — Grade A+