| --- |
| 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 |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("xpertsystems/oil014-sample", data_files="esp_operations.csv") |
| print(ds["train"][0]) |
| ``` |
|
|
| Or with pandas: |
|
|
| ```python |
| 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 |
| |
| ```bibtex |
| @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+ |
| |
