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oil014-sample

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Tabular Classification
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100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
artificial-lift
esp
gas-lift
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Initial release: OIL-014 sample, 400 wells / 120K rows, Grade A+ (10/10)
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - oil-and-gas
  - upstream
  - artificial-lift
  - esp
  - gas-lift
  - rod-pump
  - predictive-maintenance
  - failure-prediction
  - nodal-analysis
  - xpertsystems
pretty_name: OIL-014 — Synthetic Artificial Lift Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-014 — Synthetic Artificial Lift Dataset (Sample)

SKU: OIL014-SAMPLE · Vertical: Oil & Gas / Upstream Artificial Lift License: CC-BY-NC-4.0 (sample) · Schema version: oil014.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise artificial lift performance dataset for ESP/Gas Lift/Rod Pump optimization ML, failure prediction, nodal analysis, and intervention prioritization. The sample covers 400 wells across 10 global basins and 5 well classes (unconventional/tight/heavy/offshore/carbonate), simulated over 60 days, with 121,707 rows linked across 12 tables.

What's in the box

File Rows Cols Description
wells_master.csv 400 17 Well spine: basin, well class, lift type, depth, reservoir P/T, PI, water cut, GOR, integrity indices
esp_operations.csv 10,500 14 ESP physics: frequency, intake/discharge pressure, pump head, motor current/voltage, efficiency, gas lock, cavitation
gas_lift_operations.csv 4,980 12 Gas lift physics: injection rate, casing/tubing pressure, valve depth, active valve count, instability score
rod_pump_operations.csv 8,520 12 Rod pump physics: SPM, stroke length, fillage, polished rod load, counterbalance efficiency, fluid pound, pump-off
production_rates.csv 24,000 11 Per-step oil/water/gas rates + water cut + GOR + choke size + allocation quality
equipment_health.csv 24,000 11 Vibration RMS + motor temp + bearing health + corrosion/scale/sand indices + telemetry quality + failure risk
nodal_analysis.csv 24,000 9 FBHP + THP + operating point + IPR slope + VFP pressure loss + nodal balance error
power_consumption.csv 24,000 8 Voltage/amperage/kW/power factor (lift-type stratified: ESP 2400V vs Rod Pump/Gas Lift 480V)
failure_events.csv 24 9 Lift-conditioned failure modes (ESP: 7-class, Gas Lift: 6-class, Rod Pump: 6-class) + severity + downtime
maintenance_history.csv 91 8 9-class interventions (chemical/pump change/VFD tuning/valve change/rod repair/scale removal/hot oil/tubing/controller) + repair costs
optimization_recommendations.csv 792 8 9-class lift-conditioned optimization recommendations + expected gain + recommendation score
artificial_lift_labels.csv 400 8 ML labels: 3-class risk grade (low/medium/high) + intervention flag + remaining useful life days

Total: 121,707 rows across 12 CSVs, ~14.6 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: SPE 174021 (ESP performance benchmarks), API RP-11ER (sucker rod pumping design), API 11L (beam pump design), API 11AX (subsurface pump specs), SPE 14253 (gas lift injection- pressure design), API 670 (machinery protection vibration), IEEE 141 industrial electrical practices, ANSI C50.41 motor voltage standards, IEC 60038 voltage standards, Rystad Energy artificial lift market intelligence, Spears & Associates lift tracker, Schlumberger Reda / Centrilift / Lufkin product design literature.

Sample run (seed 42, n_wells=400, days=60):

# Metric Observed Target Tolerance Status Source
1 avg esp frequency hz 51.7197 52.0 ±8.0 ✓ PASS SPE 174021 + Schlumberger Reda ESP design guide — mean VFD operating frequency for modern ESP systems (typical 45-60 Hz at nameplate, 30-72 Hz operational envelope)
2 avg esp voltage v 2398.4537 2400.0 ±400.0 ✓ PASS ANSI C50.41 + IEEE 141 industrial electrical practices — standard 3-phase ESP motor voltage class (typical 2300-2400V, with 4160V used for deeper/larger units)
3 avg rod pump spm 7.7119 7.5 ±3.0 ✓ PASS API RP-11ER + API 11L (sucker rod pumping system design) — mean strokes-per-minute for beam pump units (typical 4-12 SPM operational range)
4 avg polished rod load lbf 21075.6117 21000.0 ±8000.0 ✓ PASS API 11L + API 11AX — mean polished rod load for moderately-deep onshore wells (typical 10,000-40,000 lbf operational range)
5 avg gas lift active valves 4.0048 4.0 ±2.0 ✓ PASS SPE 14253 (gas lift injection-pressure design) + API RP-19G — typical active valve count for moderately-deep (8-12 kft) gas-lifted wells (1-8 valves per string)
6 avg power factor 0.9099 0.9 ±0.06 ✓ PASS IEEE 141 industrial electrical practices + IEC 60038 — industrial power factor benchmark for mixed motor load portfolio (utility target ≥0.85; modern VFDs deliver 0.90-0.95)
7 nodal balance error pct 1.8458 2.0 ±1.5 ✓ PASS SPE 174021 + Prosper / Pipesim nodal analysis guidelines — mean nodal balance error for IPR-VFP operating-point models (target <5% for production-grade well models)
8 esp frequency load pearson correlation 0.9391 0.85 ±0.2 ✓ PASS Centrilift / Reda ESP design literature — expected positive correlation between VFD frequency and motor load factor (physics: load = f(freq, liquid_rate); validates generator's ESP physics coupling)
9 rod pump fillage fluid pound pearson correlation -0.9686 -0.9 ±0.15 ✓ PASS API RP-11ER + Lufkin sucker rod pumping handbook — expected strong inverse correlation between pump fillage and fluid pound probability (physics: fluid pound is caused by incomplete fillage; validates rod pump physics coupling)
10 lift type diversity entropy 0.9609 0.92 ±0.06 ✓ PASS Rystad Energy + Spears & Associates artificial lift tracker — 3-class lift-type diversity benchmark (ESP, Gas Lift, Rod Pump) — global active-well portfolio splits approximately 40/30/30 (basin-conditioned); normalized Shannon entropy

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

Schema highlights

wells_master.csv — the well spine with basin-class-conditioned lift type selection:

Well class ESP Gas Lift Rod Pump
Offshore 68% 30% 2%
Heavy oil 25% 10% 65%
Unconventional / tight 38% 18% 44%
Carbonate ~42% ~33% ~25%

These conditioning weights match Rystad / Spears artificial-lift market intelligence for global well portfolios.

esp_operations.csv — ESP physics with VFD frequency-driven load factor:

load_factor = (frequency_hz / 60) × (liquid_rate / 1100) efficiency = 76 − 12·|load_factor − 1.0| − 8·gas_lock − 4·scale − 3·sand + noise gas_lock_probability = sigmoid((GOR − 1800) / 550) × 0.65

Voltage centered on 2400 V (3-phase ESP motor class per ANSI C50.41

  • IEEE 141), frequency 35-72 Hz (modern VFD operational envelope). The ESP frequency↔load Pearson correlation is r ≈ 0.94 in the sample — strong physics coupling validates the design.

rod_pump_operations.csv — beam pump physics per API 11L:

SPM = 5.5 + 0.005·liquid_rate − 0.014·water_cut + noise fillage = 88 − 0.018·GOR − 12·sand − 0.09·water_cut + noise fluid_pound_prob = sigmoid((62 − fillage) / 7) + noise polished_rod_load = 3500 + 1.7·depth + 6.5·liquid + noise

Fillage↔fluid pound Pearson correlation is r ≈ −0.97 — near-perfect inverse coupling per API RP-11ER physics (fluid pound is caused by incomplete pump fillage).

gas_lift_operations.csv — gas lift physics per SPE 14253:

casing_pressure = 350 + 1.05·injection_rate + noise tubing_pressure = 120 + 0.55·casing_pressure − 0.04·liquid + noise valve_depth = depth × U(0.42, 0.88) active_valves = round(depth / 2500 + noise) lift_gas_utilization = 0.86 − 0.14·|load_factor − 0.85| − 0.05·scale + noise

Active valve count averages ~4 — matches API RP-19G gas-lift completions for moderately-deep (8-12 kft) wells.

nodal_analysis.csv — IPR-VFP intersection point per SPE 174021 nodal analysis convention. Sample mean nodal balance error is ~1.85% — well below the 5% production-grade target for IPR-VFP equilibrium models.

power_consumption.csv — power-factor-corrected 3-phase electrical metrics:

kW = lift_specific_base + lift_specific_coefficient · liquid_rate + noise amperage = kW × 1000 / (√3 × voltage × power_factor)

Voltage is lift-type stratified per ANSI C50.41: ESP 2400V (HV motor class), Gas Lift 480V (LV instrumentation/controls), Rod Pump 480V (LV prime mover).

Suggested use cases

  1. ESP failure prediction — binary classifier on failure_risk_score > threshold from ESP operations features (frequency / load factor / intake pressure / efficiency / gas lock probability).
  2. Rod pump fluid-pound detection — binary classifier on fluid_pound_probability > 0.5 from fillage / sand / water cut features. Strong physics signal (r ≈ −0.97 fillage↔fluid pound).
  3. Lift type recommendation — multi-class (3-way: ESP/Gas Lift/ Rod Pump) classifier from well characteristics (depth / reservoir P / GOR / water cut / well class).
  4. Pump-off detection — binary classifier on pump_off_probability for rod pump wells, from SPM / stroke / fillage features.
  5. Gas lock detection — binary classifier on gas_lock_probability > 0.5 from GOR / intake pressure / load factor features.
  6. Optimization-priority ranking — regression on recommendation_score from upstream operations features.
  7. Remaining-useful-life regression — predict remaining_useful_life_days from health + operations + well metadata features (standard PHM/RUL benchmark).
  8. 3-class risk-grade classification — ordinal classifier on failure_risk_grade (low/medium/high) from upstream features.
  9. Nodal-analysis fitting — regression on operating_point_bpd from IPR slope + drawdown + VFP pressure loss features. Anchors to SPE 174021 production-grade <5% nodal error.
  10. Multi-table relational ML — entity-resolution and graph neural-network learning across the 12 joinable tables via well_id + timestamp.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil014-sample", data_files="esp_operations.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
wells  = pd.read_csv("hf://datasets/xpertsystems/oil014-sample/wells_master.csv")
esp    = pd.read_csv("hf://datasets/xpertsystems/oil014-sample/esp_operations.csv")
rp     = pd.read_csv("hf://datasets/xpertsystems/oil014-sample/rod_pump_operations.csv")
gl     = pd.read_csv("hf://datasets/xpertsystems/oil014-sample/gas_lift_operations.csv")
labels = pd.read_csv("hf://datasets/xpertsystems/oil014-sample/artificial_lift_labels.csv")

# Filter wells by lift type and join to operations
esp_wells = wells[wells["lift_type"] == "ESP"]
esp_joined = esp.merge(esp_wells, on="well_id")

Reproducibility

All generation is deterministic via the integer seed parameter (driving both random.seed and np.random.default_rng). 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 artificial-lift ML research, not for live lift optimization decisions. A few notes:

  1. Risk-grade distribution skews toward "low" and "medium" — the sample has ~77% low / ~23% medium / 0% high risk-grade labels. The "high" threshold (risk ≥ 0.70) requires a confluence of high corrosion + high scale + high sand + old install age, which is rare in the Beta-distributed sample at this scale. The full product (120K wells × 90 days) generates enough tail wells to populate the "high" class. For ML at sample scale, use failure_risk_score as a continuous regression target rather than the 3-class label.

  2. Failure events are rare (~6% of wells produce a failure event in the 60-day window). This matches real-world artificial-lift reliability (annual ESP failure rates ~10-20%, gas lift ~5-10%, rod pump ~15-25% per SPE 174021 / Rystad data). For class- balanced ML training, oversample positive cases or use the continuous failure_risk_score columns in esp_operations, rod_pump_operations, equipment_health, and artificial_lift_ labels.

  3. Two distinct failure-risk-score scales coexist. Time-series tables (esp / rod_pump / equipment_health) use a sigmoid model centered around ~0.03 mean (per-timestep instantaneous risk). The labels table uses an additive index averaging ~0.34 mean (well-aggregated lifetime risk). These are not the same metric — they're computed differently and serve different ML purposes (instantaneous detection vs lifetime prognosis). Don't mix them in a single regression target.

  4. All voltage in power_consumption.csv is lift-type stratified only by class, not by well-specific motor sizing. Real ESP installations use 2300V / 3300V / 4160V depending on motor HP and depth — the sample uses N(2400, 180) for all ESP wells. For motor-sizing ML, condition the voltage feature on depth and liquid rate.

  5. The optimization_recommendations table is uniformly sampled, not tied to specific underlying conditions. Real production engineering recommends specific interventions based on observed symptoms (high gas lock → reduce frequency, high fluid pound → reduce SPM). For optimization ML, treat this table as label- only and engineer features from the operations tables.

  6. Production decline is linear-on-time (depletion = 1 - 0.22 × t/n_steps), not Arps-driven. For decline-curve ML, use OIL-013 (which implements full Arps hyperbolic decline). OIL-014 focuses on lift-system optimization and instantaneous performance, not long-horizon decline.

  7. Sample-scale ESP class is over-represented vs the global declared 42/33/25 mix. Sample observed 44/21/35 (ESP/Gas Lift/ Rod Pump). The basin-class conditioning produces this because the sample's basin draws favor unconventional/tight/heavy oil classes (which favor rod pump and ESP), giving fewer Gas Lift wells than declared. The full product (120K wells) gives the declared 42/33/25 split with basin-conditioning preserved.

Full product

The full OIL-014 dataset ships at 120,000 wells × 90 days (prod mode) producing several hundred million operation records with substantial failure / maintenance / optimization event populations, full "high" risk- grade class population, and per-motor-sized voltage stratification — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil014_sample_2026,
  title  = {OIL-014: Synthetic Artificial Lift Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil014-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-22 12:49:51 UTC
  • Wells : 400
  • Days simulated : 60
  • Frequency : 24h (60 timesteps per well)
  • Basins : 10 (Permian, Eagle Ford, Bakken, GoM, North Sea, Middle East, Canadian Heavy Oil, Williston, Anadarko, San Joaquin)
  • Well classes : 5 (unconventional oil, tight oil, offshore, carbonate, heavy oil)
  • Lift types : 3 (ESP, Gas Lift, Rod Pump) basin-class-conditioned
  • Failure modes : 19 (ESP: 7, Gas Lift: 6, Rod Pump: 6)
  • Optimization types: 9 (lift-conditioned recommendations)
  • Calibration basis : SPE 174021, API RP-11ER, API 11L, API 11AX, SPE 14253, API 670, IEEE 141, ANSI C50.41, IEC 60038, Rystad, Spears, Schlumberger Reda, Centrilift, Lufkin design literature
  • Overall validation: 100.0/100 — Grade A+