| --- |
| 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. |
|
|
