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README.md

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+---
+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.

enr004_facilities.csv

@@ -0,0 +1,16 @@
+facility_id,field_id,basin,facility_type,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
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+31f86e4d-43ae-45b2-15f9-1f8cb40d7db6,fd03d0d4-e46a-488c-fa66-6d525011cbb7,Permian,Compressor_Station,2237.0,453.0,553.3,115.5,95.13,61.49,0.99521,0.0
+37cda358-21dc-4df4-9624-1022925f080a,f62dab14-cd6c-46e4-3aa2-d3c0ccca7b4c,Eagle_Ford,Tank_Battery,2766.0,0.0,290.4,130.9,96.76,83.88,0.99914,0.0
+34504f36-b163-461e-d25a-dfef72da52d3,acc35770-82ba-4b5f-65e5-17c372a7539a,GOM_Offshore,GPF,16455.0,0.0,170.2,106.9,99.54,61.87,1.00848,0.0
+80ea41d6-99e2-4911-b7bf-49097d95e110,f62dab14-cd6c-46e4-3aa2-d3c0ccca7b4c,Eagle_Ford,GPF,2085.0,0.0,313.1,155.9,98.19,60.59,1.00177,0.0
+062a5d7b-2b65-4990-6fdf-d10541e82279,4c4bc7b4-117e-47b3-b4c9-827d0c7ba64a,Eagle_Ford,Separator_Train,14067.0,0.0,402.0,108.7,98.34,41.62,0.99818,0.0
+807518c2-71ed-485a-37fd-3564d618b334,291aa1c6-c027-45a2-a129-c24ef9759940,Eagle_Ford,Separator_Train,4459.0,0.0,367.4,136.1,95.29,69.38,1.00108,0.0
+98124054-7a1d-4927-54b2-e150922c70a0,b2e8f27b-7726-44aa-95f1-515f5e7b74cf,Bakken,Tank_Battery,6284.0,0.0,488.3,154.2,95.56,68.13,1.00221,0.0
+5bae0c29-5690-4c3e-0834-995531eb3231,b2e8f27b-7726-44aa-95f1-515f5e7b74cf,Bakken,Saltwater_Disposal,302.0,0.0,247.1,147.8,99.34,91.75,1.00027,4451.0

enr004_facilities.parquet

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enr004_fields_metadata.csv

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+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,aquifer_strength_index
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enr004_fields_metadata.parquet

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enr004_production_data.parquet

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enr004_wells_metadata.csv

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enr004_wells_metadata.parquet

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