---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- synthetic-data
- oil-and-gas
- upstream
- production
- decline-curve
- arps
- arps-decline
- type-curve
- well-economics
- reserves
- eur
- spe-prms
- api-production-reporting
- epa-subpart-w
- methane-emissions
- ghg-emissions
- pvt
- standing-correlation
- vasquez-beggs
- reservoir-engineering
- petroleum-engineering
- permian
- eagle-ford
- bakken
- appalachian
- marcellus
- gulf-of-mexico
- shale
- unconventional
- horizontal-drilling
- hydraulic-fracturing
- esp
- rod-pump
- gas-lift
- wti
- henry-hub
- commodity-prices
- ornstein-uhlenbeck
- royalty
- working-interest
- net-revenue-interest
- nri
- gor
- water-cut
- bsw
- bottomhole-pressure
pretty_name: ENR004 — Synthetic Upstream Oil & Gas Production Dataset (Sample)
size_categories:
- 10K<n<100K
configs:
- config_name: production
  data_files: enr004_production_data.parquet
- config_name: wells
  data_files: enr004_wells_metadata.parquet
- config_name: fields
  data_files: enr004_fields_metadata.parquet
- config_name: facilities
  data_files: enr004_facilities.parquet
---

# ENR004 — Synthetic Upstream Oil & Gas Production Dataset (Sample Preview)

**XpertSystems.ai | Synthetic Data Factory | Energy & Climate Vertical**

A four-table, **physics-calibrated** upstream oil & gas production dataset
spanning 5 US basins (Permian, Eagle Ford, Bakken, Appalachian/Marcellus,
GOM Offshore) with **daily-resolution well-level production timeseries**,
Arps decline curve physics, Standing (1947) and Vasquez-Beggs (1980) PVT
correlations, Ornstein-Uhlenbeck commodity prices, and EPA Subpart W
methane intensity. Calibrated benchmark-first against **SPE Petroleum
Resources Management System (PRMS)**, **API production reporting**, **EPA
Subpart W**, and **EIA Drilling Productivity Reports**.

This is the **sample preview** — 50 wells × 8 fields × 2 years × daily
(~15K production records). The full product covers 5,000 wells × 50 fields
× 5 years (~9M rows) with full geologic heterogeneity, complete
basin/fluid/lift coverage, and 500 surface facilities.

---

## Dataset summary

| Table | Rows (sample) | What it contains |
|---|---:|---|
| `production_data` | ~15,699 | Daily well-level production: oil/gas/water rates, GOR, WOR, BSW, cumulative volumes, reservoir/wellhead/bottomhole pressures, ESP/rod-pump/gas-lift parameters, WTI/Henry Hub prices, oil/gas revenues, LOE, netback, well status, methane/CO2/H2S/flare emissions, spill flags |
| `wells_metadata` | 50 | Per-well metadata: API number, basin, fluid type (Black Oil / Volatile Oil / Gas Condensate / Dry Gas / CBM), lift type (ESP / Rod Pump / Gas Lift / Plunger / Natural Flow), completion type (Horizontal / Deviated / Vertical), lateral length, perforation stages, proppant/fluid volumes, skin, PI, IP30/IP90, Arps decline parameters, EUR, working interest, royalty rate, NRI |
| `fields_metadata` | 8 | Reservoir block metadata: basin, formation, initial pressure, reservoir temperature, permeability, porosity, net pay, API gravity, sulfur content, bubble point, oil viscosity, FVF, OOIP/OGIP, drive type, aquifer strength |
| `facilities` | 15 | Surface facilities: GPF, separator trains, tank batteries, compressor stations, LACT units, saltwater disposal — with treating capacities, separator T/P, uptime, throughput utilization, meter factors |

All four tables are provided in both **CSV** and **Parquet**. They join via
`field_id` (production ↔ wells ↔ fields ↔ facilities).

---

## Calibration sources

All ten validation metrics target named industry sources, not generator
self-metrics:

- **SPE PRMS (Petroleum Resources Management System)** — economic
  identities (NRI = WI × (1 - royalty)), reserves classification
- **Arps (1945) Decline Curve Analysis** — hyperbolic/exponential/harmonic
  decline equations
- **Standing (1947) PVT correlation** — solution GOR vs. pressure
- **Vasquez-Beggs (1980) FVF correlation** — oil formation volume factor
- **API Production Reporting** — well numbering (14-digit API),
  status codes, GOR/WOR bounds
- **EPA Subpart W** — upstream methane emissions intensity
- **EIA Drilling Productivity Report** — basin-level lateral lengths,
  completion statistics, fluid mix
- **EIA Spot WTI 2019-2024** — commodity price ranges for OU calibration
- **Ornstein-Uhlenbeck process** — mean-reverting commodity price model

---

## Validation scorecard (seed = 42)

10/10 PASS · **Grade A+ (100%)** across all six canonical seeds (42, 7, 123, 2024, 99, 1).

| # | Metric | Observed | Target | Tol | Type | Source |
|---|---|---:|---:|---:|---|---|
| 1 | `nri_structural_match_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | SPE PRMS |
| 2 | `pressure_structural_fbhp_le_reservoir` | 1.000 | 0.99 | ±0.01 | FLOOR | Reservoir engineering |
| 3 | `cumulative_oil_monotonic_per_well_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Mass balance |
| 4 | `arps_decline_observed_per_well_rate` | 1.000 | 0.95 | ±0.05 | FLOOR | Arps (1945) |
| 5 | `api_gravity_mean_degrees` | 38.81 | 38.0 | ±4.0 | two-sided | EIA crude grades |
| 6 | `horizontal_lateral_length_mean_ft` | 8,160 | 8,500 | ±2,000 | two-sided | EIA DPR |
| 7 | `methane_oil_intensity_pct` | 0.835 | 0.85 | ±0.30 | two-sided | EPA Subpart W |
| 8 | `wti_price_mean_usd_per_bbl` | 75.90 | 68.0 | ±20.0 | two-sided | EIA WTI 2019-2024 |
| 9 | `oil_revenue_non_negative_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Economics integrity |
| 10 | `gor_in_industry_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | SPE GOR bounds |

---

## Schema highlights

### `production_data` (~15,699 rows × 36 columns)

**Identifiers (4):** `well_id`, `api_number`, `field_id`, `production_date`.

**Rates & ratios (8):** `oil_rate_bopd`, `gas_rate_mcfd`, `water_rate_bwpd`,
`gross_rate_blpd`, `gor_scf_per_bbl`, `wor`, `bsw_pct`, plus cumulative
counterparts.

**Cumulative production (3):** `cumulative_oil_bbl`, `cumulative_gas_mscf`,
`cumulative_water_bbl` — monotonically increasing per well.

**Pressures (4):** `reservoir_pressure_psia` (depletion-aware),
`flowing_wellhead_pressure_psia`, `flowing_bottomhole_pressure_psia`,
`drawdown_psia`.

**Artificial lift (4):** `esp_frequency_hz`, `rod_pump_spm`,
`rod_pump_fillage_pct`, `gas_lift_rate_mmscfd` — populated per `lift_type`.

**Economics (7):** `wti_price_usd_per_bbl`, `realized_oil_price_usd_per_bbl`,
`henry_hub_usd_per_mmbtu`, `oil_revenue_usd`, `gas_revenue_usd`,
`loe_total_usd_per_day`, `net_operating_cash_flow_usd`, `netback_usd_per_boe`.

**Well status (1):** `well_status` ∈ {PRODUCING, SHUT_IN, P_AND_A}.

**HSE / Environmental (5):** `methane_emissions_mcfd`,
`co2_emissions_tons_per_day`, `flare_volume_mmscfd`,
`h2s_concentration_ppm`, `spill_event_flag`.

### `wells_metadata` (50 rows × 30 columns)

`well_id`, `api_number`, `field_id`, `basin`, `fluid_type`, `lift_type`,
`completion_type`, `lateral_length_ft`, `n_perforation_stages`,
`proppant_volume_mlb`, `fluid_volume_bbl`, `skin_factor`,
`productivity_index_bopd_per_psi`, `spud_date`, `completion_date`,
`first_production_date`, `ip_30_bopd`, `ip_90_bopd`, `decline_type`
(Hyperbolic / Exponential / Harmonic), `initial_decline_rate_pct_yr`,
`hyperbolic_b_factor`, `terminal_decline_rate_pct_yr`, `eur_oil_mbo`,
`eur_gas_mmscf`, `working_interest_pct`, `royalty_rate_pct`,
`net_revenue_interest_pct`, `workover_count`, `recompletion_flag`.

### `fields_metadata` (8 rows × 19 columns)

`field_id`, `reservoir_id`, `basin`, `formation_name`,
`initial_reservoir_pressure_psia`, `reservoir_temperature_degF`,
`permeability_md`, `porosity_pct`, `net_pay_ft`, `area_acres`,
`api_gravity`, `sulfur_content_pct`, `bubble_point_pressure_psia`,
`oil_viscosity_cp`, `formation_volume_factor_bo`,
`original_oil_in_place_mmbo`, `original_gas_in_place_bcf`,
`reservoir_drive_type` (Solution Gas / Water Drive / Gas Cap / Compaction /
Combination), `aquifer_strength_index`.

### `facilities` (15 rows × 12 columns)

`facility_id`, `field_id`, `basin`, `facility_type` (GPF / Separator Train /
Tank Battery / Compressor Station / LACT Unit / Saltwater Disposal),
`oil_treating_capacity_bopd`, `gas_compression_hp`,
`separator_inlet_pressure_psia`, `separator_temperature_degF`,
`facility_uptime_pct`, `throughput_utilization_pct`, `lact_meter_factor`,
`saltwater_disposal_bwpd`.

---

## Suggested use cases

- **Type curve / EUR forecasting** — train Arps decline parameter estimators
  from early-time IP30/IP90 + completion features (lateral length, n_stages,
  proppant); predict EUR per well
- **Production rate forecasting** — multi-step time-series prediction of
  `oil_rate_bopd`, `gas_rate_mcfd`, `water_rate_bwpd` conditioned on
  reservoir pressure, drawdown, and lift parameters
- **Well economics ML** — net cash flow regression from WTI, oil rate,
  GOR, royalty, and LOE; build break-even price predictors
- **Reservoir pressure decline modeling** — train ML to predict
  `reservoir_pressure_psia` from cumulative production and drive type
- **GOR / WOR progression modeling** — classifier for fluid evolution
  phases (above bubble point, gas breakthrough, water breakthrough)
- **ESP / rod pump anomaly detection** — unsupervised models on
  `esp_frequency_hz` and `rod_pump_fillage_pct` for downhole equipment
  failure prediction
- **Methane emissions intensity benchmarking** — train regressors for
  `methane_emissions_mcfd` per BOE; useful for EPA Subpart W reporting
  ML augmentation
- **Basin classification** — predict basin from completion params, GOR,
  API gravity, and decline parameters
- **Lift type selection** — classifier for `lift_type` given reservoir
  conditions, fluid properties, and depth proxies
- **Commodity price stress testing** — replay WTI/Henry Hub OU paths
  against well economics; quantile-based break-even analysis
- **Plug-and-abandon (P&A) prediction** — survival model for time-to-P&A
  from IP30, decline rate, and price exposure
- **Multi-table relational ML** — join production × wells × fields ×
  facilities for full-stack value-chain optimization

---

## Loading examples

```python
from datasets import load_dataset

# Load the primary production table
prod = load_dataset("xpertsystems/enr004-sample", "production", split="train")
print(prod.shape)
```

```python
import pandas as pd
from huggingface_hub import hf_hub_download

# Load all four tables and join
prod = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr004-sample", "enr004_production_data.parquet",
    repo_type="dataset",
))
wells = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr004-sample", "enr004_wells_metadata.parquet",
    repo_type="dataset",
))
fields = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr004-sample", "enr004_fields_metadata.parquet",
    repo_type="dataset",
))

# Per-well decline ratio (last 30 days / first 30 days)
for wid, g in prod.groupby("well_id"):
    g = g.sort_values("production_date")
    producing = g[g["oil_rate_bopd"] > 1]
    if len(producing) >= 60:
        ratio = producing.tail(30)["oil_rate_bopd"].mean() / producing.head(30)["oil_rate_bopd"].mean()
        wells_meta = wells[wells["well_id"] == wid].iloc[0]
        print(f"{wid[:8]}  {wells_meta['basin']:<14} {wells_meta['fluid_type']:<14} "
              f"decline ratio = {ratio:.3f}")
```

```python
# Arps decline curve fit per well
import numpy as np
import pandas as pd

def fit_exponential_di(g):
    """Estimate exponential decline rate per year from log-linear fit."""
    g = g.sort_values("production_date")
    producing = g[g["oil_rate_bopd"] > 1].reset_index(drop=True)
    if len(producing) < 30:
        return np.nan
    days = np.arange(len(producing))
    log_oil = np.log(producing["oil_rate_bopd"].values)
    m, _ = np.polyfit(days, log_oil, 1)
    return -m * 365  # fraction per year

fit_di = prod.groupby("well_id").apply(fit_exponential_di)
print(f"Fitted di mean: {fit_di.mean():.3f}/yr  (target: see wells.initial_decline_rate_pct_yr)")
```

---

## Limitations and honest disclosures

This sample is calibrated for **structural fidelity, not bit-exact reproduction
of any specific basin's archive.** Specifically:

- **The production table is NOT a uniform daily series for every well.**
  Generator line 543-545 skips consecutive inactive days as a memory
  optimization. For long-shut-in or P_AND_A wells, only the transition
  rows are recorded. Use `well_status` and `production_date` explicitly
  when building daily-frequency time series.
- **`unit_status` (PRODUCING / SHUT_IN / P_AND_A) is computed from
  `oil_rate_bopd` vs. economic limit** — there is no separate operational
  state model for unplanned shutdowns vs. mechanical issues vs. economic
  shut-ins. Use status as a coarse activity proxy, not an event log.
- **Decline curve log-linear R² at daily resolution is low (~0.02-0.05)**
  even though the underlying Arps process is correctly implemented. The
  ±5% lognormal noise + 2% seasonal modulation + 3% downtime mask the
  smooth Arps trend at daily cadence. For decline-curve ML, aggregate
  to monthly first (the industry-standard cadence). The structural decline
  property (last 30 days < first 30 days) holds at 100% of qualifying wells.
- **The Ornstein-Uhlenbeck WTI process** mean-reverts to $68 at θ=0.15/yr
  but the 2-year sample mean wanders $60-$87 across seeds. For volatility-
  sensitive backtests, replay with deliberate price shocks; for level
  exposure, use the realized prices per row directly.
- **`base_gor` is derived from `bubble_point_pressure × 0.5`** (generator
  line 466), then escalated by `1 + t × uniform(0.05, 0.25)/yr` per well.
  This produces realistic GOR rise for depleting reservoirs above bubble
  point, but does NOT model the gas-cap-driven GOR collapse that occurs
  in solution-gas-drive reservoirs after they drop below bubble point.
- **`h2s_concentration_ppm` is derived from `sulfur_content_pct × 5000`**
  — a coarse proxy. Real H2S concentrations depend on bacterial sulfate
  reduction, formation chemistry, and souring history. Use as a hazard
  flag, not for materials selection or treating chemistry design.
- **`spill_event_flag`** fires at 0.001/day — at sample scale that's
  ~15 events across the full table; per-basin spill rate analysis needs
  the full product scale (5K wells × 5 years).
- **`recompletion_flag` is per-well static** — generator does not model
  the date/timing/effect of a recompletion on subsequent production.
- **Surface facilities are not linked 1:1 to specific wells** — the
  `facilities.field_id` joins to the field, but wells-to-facility
  assignment is not modeled. Treat facilities as field-level summary.
- **Commodity price differentials are per-well constants** drawn at
  well-creation time, not time-varying — the full product models
  basin-specific differential dynamics (e.g., Midland-Cushing,
  Appalachian-NYMEX).

The full ENR004 product addresses these by per-event SCADA shutdowns,
monthly-aggregated decline analytics, basin-specific GOR/WOR phase
modeling, well-facility linkage, and time-varying differentials —
contact us for the licensed commercial release.

---

## Companion datasets in the Energy & Climate vertical

- **ENR-001** — Synthetic Power Grid Operations Dataset (bus telemetry,
  line flows, dispatch, frequency, contingency)
- **ENR-002** — Synthetic Renewable Energy Generation Dataset (solar/wind/
  hybrid SCADA, weather, forecast, PCC, BESS)
- **ENR-003** — Synthetic Electricity Demand & Load Forecasting Dataset
  (zone-level demand, multi-horizon forecasts, peak events, EV/DER, TOU,
  LMP)
- **ENR-004** — Synthetic Upstream Oil & Gas Production Dataset (you are
  here) — well-level production, decline curves, PVT, commodity prices,
  Subpart W methane

Use **ENR-001 + ENR-002 + ENR-003 + ENR-004** together for a complete
energy value-chain ML workflow: upstream production economics (ENR-004) →
generation supply mix and renewables (ENR-002) → grid dispatch and
delivery (ENR-001) → demand-side load and pricing (ENR-003).

For subsurface companion data (seismic, well logs, reservoir simulation,
geological formations), see the **OIL series** (OIL-001 through OIL-004)
in our [Oil & Gas vertical](https://huggingface.co/xpertsystems).

For the broader catalog:
- [Materials & Energy](https://huggingface.co/xpertsystems) — MAT-001
- [Insurance & Risk](https://huggingface.co/xpertsystems) — 10 SKUs
- [Cybersecurity](https://huggingface.co/xpertsystems) — 11 SKUs

---

## Citation

```bibtex
@dataset{xpertsystems_enr004_sample_2026,
  author       = {XpertSystems.ai},
  title        = {ENR004 Synthetic Upstream Oil and Gas Production Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/enr004-sample}
}
```

---

## Contact

- **Web:** https://xpertsystems.ai
- **Email:** pradeep@xpertsystems.ai
- **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy,
  Oil & Gas, Energy & Climate, and more

**Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0)
**Full product License:** Commercial — please contact for pricing.