README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- upstream
- production-engineering
- decline-curve-analysis
- arps-decline
- artificial-lift
- reservoir-engineering
- workover-prediction
- water-breakthrough
- xpertsystems
pretty_name: "OIL-013 — Synthetic Production Time-Series Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-013 — Synthetic Production Time-Series Dataset (Sample)

**SKU:** `OIL013-SAMPLE` · **Vertical:** Oil & Gas / Upstream Production Engineering
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil013.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise production
time-series dataset for decline curve ML, artificial-lift optimization,
workover-candidate prediction, and water-breakthrough forecasting. The
sample covers **250 wells** across **10 global basins** and
**8 asset types**, simulated over **365 days**, with
**116,154 rows** including **91,250 daily production
records** linked across **11 tables**.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `wells_master.csv` | 250 | 24 | Well spine: basin, formation, completion, lift type, Arps decline params (qi, di, b) |
| `daily_production.csv` | 91,250 | 17 | Per-well-per-day oil/gas/water/water-cut/GOR/pressures/uptime/anomaly |
| `reservoir_pressure.csv` | 6,750 | 7 | Biweekly pressure tests: reservoir P + BHFP + drawdown + test quality |
| `artificial_lift.csv` | 13,250 | 9 | Weekly lift performance: ESP frequency/pump efficiency/motor temp/vibration/runtime |
| `downtime_events.csv` | 491 | 7 | 8-class downtime (planned/unplanned/facility/weather/flow assurance/lift/integrity/power) |
| `stimulation_events.csv` | 1 | 8 | Workover/refrac/acidizing/cleanout/lift change with expected/actual uplift |
| `injection_support.csv` | 40 | 9 | Producer-injector pairings with response-lag correlation scores |
| `production_allocations.csv` | 625 | 6 | 1-4 producing zones per well, Dirichlet-sampled (sums to 100%) |
| `facility_constraints.csv` | 200 | 8 | Per-field throughput/gas/water handling limits + constraint severity |
| `flow_assurance_events.csv` | 47 | 9 | 6-class flow assurance (scale/paraffin/hydrate/sand/emulsion/corrosion) |
| `production_labels.csv` | 3,250 | 9 | Monthly ML labels: 6-class forecast + 4 binary flags (workover/water breakthrough/steep decline/lift limited) |

Total: **116,154 rows** across 11 CSVs, ~14.8 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **Arps (1945) JPT** "Analysis of Decline
Curves" (canonical hyperbolic decline equation), SPE Petroleum Engineering
Handbook Vol V, SPE 152596 (Unconventional Reservoir Decline Curve
Analysis), SPE 167242 (Arps b-factor calibration for unconventional wells),
SPE 174021 (ESP performance benchmarks), API RP-11ER (sucker rod pumping
system design), EIA Annual Energy Outlook, Rystad ShaleWellCube
(unconventional well economics), IHS Markit global production tracker,
IOGP allocation standards.

**Sample run** (seed `42`, n_wells=250, simulation_days=365):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg initial oil rate bopd | 1232.3387 | 1100.0 | ±400.0 | ✓ PASS | EIA AEO + Rystad ShaleWellCube — mean initial oil rate for mixed US unconventional + international portfolio (Permian/Eagle Ford ~1500 BOPD IP, deepwater ~2000, heavy oil ~300, shale gas ~200 BOPD condensate) |
| 2 | avg initial water cut pct | 32.7699 | 34.0 | ±10.0 | ✓ PASS | SPE Petroleum Engineering Handbook Vol V + Rystad — mean initial water cut for mixed onshore/offshore production portfolio (greenfield wells typically 5-25%, mature fields 40-70%) |
| 3 | avg initial gor scf bbl | 1977.7810 | 1800.0 | ±600.0 | ✓ PASS | SPE PEH Vol V + EIA — mean initial gas-oil ratio across mixed oil/condensate/wet-gas portfolio (Permian ~1500, Marcellus 5000+ condensate, Bakken 1200-2500, heavy oil 200-500 scf/bbl) |
| 4 | avg nominal decline rate | 0.2300 | 0.23 | ±0.08 | ✓ PASS | SPE 152596 (Unconventional Reservoir Decline Curve Analysis) + SPE 167242 — mean first-year nominal annual decline rate for mixed shale/conventional portfolio (shale 0.30-0.65 yr1, conventional 0.05-0.20, deepwater 0.08-0.25) |
| 5 | avg arps b factor | 0.9574 | 1.0 | ±0.3 | ✓ PASS | Arps (1945) JPT + SPE 167242 — mean hyperbolic exponent b-factor for unconventional/conventional mix (shale typically 1.0-1.8 transitioning to exponential at terminal decline, conventional 0.3-1.0) |
| 6 | arps decline fidelity score | 0.9501 | 0.9 | ±0.06 | ✓ PASS | Arps (1945) JPT canonical decline equation — fidelity of generated daily production rates to the Arps prediction (computed as 1 - mean absolute relative error on anomaly-free days across 50 sample wells, target ≥0.85 indicates strong Arps physics) |
| 7 | production mass balance score | 1.0000 | 0.99 | ±0.01 | ✓ PASS | Material balance principle — cumulative production should equal sum of daily rates (verifies generator's cumulative_oil_bbl column is internally consistent, target ≥0.98 indicates proper integration) |
| 8 | allocation completeness score | 1.0000 | 1.0 | ±0.02 | ✓ PASS | SPE production allocation guidelines + IOGP allocation standards — per-well allocation percentages across producing zones must sum to 100% (validates Dirichlet sampling produces complete allocations) |
| 9 | basin diversity entropy | 0.9964 | 0.95 | ±0.05 | ✓ PASS | Rystad Energy + EIA + IHS Markit global production tracker — 10-class basin diversity benchmark (Permian, Eagle Ford, Bakken, Marcellus, Haynesville, GoM, North Sea, Middle East, Western Canada, Brazil Pre-Salt), normalized Shannon entropy |
| 10 | lift type diversity entropy | 0.9078 | 0.85 | ±0.1 | ✓ PASS | API RP-11ER + SPE 174021 + Spears & Associates lift market intelligence — 6-class artificial lift diversity benchmark (natural flow, ESP, rod pump, gas lift, PCP, plunger lift), normalized Shannon entropy (ESP-dominant per industry default weights [0.18, 0.31, 0.22, 0.18, 0.07, 0.04]) |

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

---

## Schema highlights

**`daily_production.csv`** — the production spine, one row per well per day.
The oil-rate model is **Arps (1945) hyperbolic decline** modulated by
operational factors:

> q(t) = qi / (1 + b·di·t)^(1/b)                  — Arps hyperbolic
> oil_rate = q(t) × seasonal × noise × uptime × lift_factor

The **decline-curve fidelity is high**: at sample scale, the mean absolute
relative error between actual rates and pure-Arps predictions on anomaly-
free days is ~6%, with the residual driven by lift degradation (built-in)
and operational noise (1.5% std). The full Arps physics is preserved
well-by-well — see the scorecard for the explicit fidelity check.

**`reservoir_pressure.csv`** — biweekly pressure tests with realistic
drawdown modeling:

> reservoir_pressure(d) = p0 × (1 − 0.22 × d/sim_days) + N(0, 45)
> bhfp = reservoir_pressure − U(250, 1700)
> drawdown = reservoir_pressure − bhfp

Pressure-test quality follows the **A/B/C grading convention** common in
production engineering (40% A, 40% B, 20% C).

**`artificial_lift.csv`** — weekly performance per lift type. ESP wells
get full instrumentation (ESP frequency Hz, pump efficiency, motor
temperature F, vibration index); rod pump / PCP / gas lift / plunger
get pump efficiency + vibration only. **ESP frequency centered at 52 Hz**
per API/SPE 174021 ESP operating-range guidance.

**`production_labels.csv`** — monthly ML labels with **6-class forecast
classification**:

| Class | Trigger |
|---|---|
| `stable` | oil_rate ≥ 0.60 × qi AND wc ≤ 62% |
| `moderate_decline` | oil_rate < 0.60 × qi |
| `workover_candidate` | oil_rate < 0.35 × qi OR wc > 62% |
| `water_breakthrough` | wc > 75% |
| `lift_limited` | non-natural-flow AND uptime < 78% |
| `steep_decline` | oil_rate < 0.20 × qi at early time |

Plus four binary flags: `workover_candidate_flag`, `water_breakthrough_flag`,
`steep_decline_flag`, `lift_limited_flag`.

**`production_allocations.csv`** — per-well multi-zone allocation using
**Dirichlet sampling** over 1-4 zones; per-well percentages sum to exactly
100%. Allocation methods follow standard production engineering practice:
test separator / production logging / model based / commingled estimate
(weighted equally).

**`flow_assurance_events.csv`** — 6-class flow assurance taxonomy aligned
with **NACE corrosion standards + SPE flow assurance literature**:
scale / paraffin / hydrate / sand / emulsion / corrosion. Per-event
domain-specific risk indices.

---

## Suggested use cases

1. **Arps decline curve regression** — fit hyperbolic Arps parameters
   (qi, di, b) from the first 60-180 days of daily production for
   each well; benchmark against the ground-truth params in
   `wells_master.csv`. Strong physics signal — sample mean Arps
   fidelity is ~94%.
2. **6-class forecast class classification** — multi-class classifier
   on `forecast_class` from daily production + lift + pressure
   features.
3. **Workover candidate prediction** — binary classifier on
   `workover_candidate_flag` from upstream features. Highly
   class-imbalanced (~3% positives), realistic for production
   engineering operations.
4. **Water breakthrough prediction** — binary or time-to-event
   modeling on `water_breakthrough_flag` from water-cut trajectory
   features.
5. **ESP failure prediction** — train RUL or binary failure
   classifier on `artificial_lift.csv` ESP-only rows using vibration,
   motor temperature, pump efficiency degradation as features.
6. **Multi-zone allocation regression** — predict per-zone allocation
   percentages from well characteristics and zone metadata.
7. **Flow assurance type classification** — 6-class classifier on
   `flow_assurance_type` from well characteristics and production
   conditions.
8. **Production rate forecasting** — N-day-ahead time-series
   forecasting of oil/gas/water rates from historical features
   (LSTM / Transformer / TFT benchmark target).
9. **Downtime root-cause classification** — 6-class classifier on
   `root_cause_category` (surface/subsurface/facility/weather/power/
   unknown) from production anomaly patterns.
10. **Multi-table relational ML** — entity-resolution and graph
    neural-network learning across the 11 joinable tables via
    `well_id` + `production_date`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil013-sample", data_files="daily_production.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
wells   = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/wells_master.csv")
daily   = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/daily_production.csv")
lift    = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/artificial_lift.csv")
labels  = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/production_labels.csv")

# Join daily production to wells master for asset-type / completion-type features
joined = daily.merge(wells, on="well_id")

# Join labels to daily production (monthly labels propagated to all days in month)
labels["label_date"] = pd.to_datetime(labels["label_date"])
daily["production_date"] = pd.to_datetime(daily["production_date"])
```

---

## 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 production-engineering and
decline-curve ML research, not for live production-allocation decisions.
A few notes:

1. **Initial rates run higher than declared base parameter.** The
   generator's `--mean-initial-oil-rate-bopd` parameter is 950 BOPD,
   but the actual observed mean is ~1100 BOPD because **two stacked
   lognormal multipliers** (basin profile `qi_mult` × per-well
   `lognormal(0, 0.25)`) compound to a multiplier mean > 1. Same
   pattern for GOR (declared 1450, observed ~1800). This is realistic
   (real production distributions have positive skew), but if you
   need a pure declared-target match, scale `--mean-initial-oil-rate-
   bopd` down by ~13% to compensate for the lognormal-compound bias.

2. **Anomaly and downtime injection rates are very low.** The
   generator divides `anomaly_injection_rate / 365.25` and
   `downtime_event_rate / 365.25` to convert per-year rates to
   per-day probabilities. At sample defaults (3% per year, 2.2% per
   year), this gives ~0.0001 daily probability — essentially zero
   anomalies in the daily timeseries (~0.01% rate observed). The
   `downtime_events.csv` table is separately generated via a Poisson
   model and *is* populated (~2 events/well), so downtime ML training
   uses that table, not the per-day anomaly flag.

3. **Forecast class distribution is heavily skewed toward "stable"
   (~97%)** because the simulation runs only 365 days — Arps
   hyperbolic decline with mean b=1.0 and di=0.23 produces only
   ~20-27% rate decline in year 1, which keeps most wells in the
   "stable" class (oil_rate ≥ 0.60 × qi). For long-horizon forecast
   ML, use the full product with `--simulation-days 1800+` to see
   meaningful class diversity (steep_decline, lift_limited,
   water_breakthrough all populate substantially over 3-5 years).

4. **Stimulation events are extremely sparse** (~1 event in 250 wells
   at sample scale) because the generator uses a one-time Bernoulli
   draw per well with combined probability ~2.4%. Full product
   (120K wells × 3650 days) gives ~3000 stimulation events with full
   event-type diversity. For workover ML at sample scale, use the
   `workover_candidate_flag` in `production_labels.csv` (synthesized
   from production patterns) rather than the literal
   stimulation_events table.

5. **Mass balance is exact (>99.99%)** because the generator's
   `cumulative_oil_bbl` column is computed as a running sum of
   `oil_rate_bopd`. This is a property of the simulation, not a
   physics test — but it does confirm proper integration. Use it as
   sanity check, not as evidence of advanced reservoir physics.

6. **Pressure decline is linear**, not exponential. The generator
   uses `p(d) = p0 × (1 - 0.22 × d/sim_days)`, which is a simple
   linear depletion model. Real reservoirs follow material-balance-
   driven decline with B-factor and aquifer support — for
   reservoir-engineering-grade decline modeling, use SPE-PEH-Vol-V
   compliant tools rather than the OIL-013 pressure column.

7. **Allocation methods are uniformly weighted**, not conditioned on
   well type or facility. Real production allocations heavily favor
   test-separator for low-rate wells, model-based for commingled
   pads, and production-logging for problem wells. Future generator
   v1.1 will introduce conditioning.

---

## Full product

The **full OIL-013 dataset** ships at **120,000 wells × 3,650 days**
(prod mode) producing several hundred million daily production rows with
substantial populated stimulation/workover events, full multi-year
decline curves enabling meaningful forecast-class diversity, and
basin-conditioned operator behavior priors — licensed commercially.
Contact XpertSystems.ai for licensing terms.

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

---

## Citation

```bibtex
@dataset{xpertsystems_oil013_sample_2026,
  title  = {OIL-013: Synthetic Production Time-Series Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil013-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 12:39:33 UTC
- Wells             : 250
- Simulation days   : 365
- Basins            : 10 (Permian, Eagle Ford, Bakken, Marcellus,
                      Haynesville, GoM, North Sea, Middle East, W Canada,
                      Brazil Pre-Salt)
- Asset types       : 8 (unconventional shale oil, tight oil,
                      shale gas, deepwater, offshore sandstone, carbonate,
                      heavy oil, deepwater carbonate)
- Completion types  : 6 (horizontal multistage frac, vertical, deviated,
                      multilateral, open hole, cased hole)
- Lift types        : 6 (natural flow, ESP, rod pump, gas lift, PCP,
                      plunger lift)
- Downtime types    : 8 (planned, unplanned, facility, weather, flow
                      assurance, lift, integrity, power)
- Flow assurance    : 6 (scale, paraffin, hydrate, sand, emulsion, corrosion)
- Forecast classes  : 6 (stable, moderate decline, steep decline, water
                      breakthrough, lift limited, workover candidate)
- Calibration basis : Arps (1945), SPE PEH Vol V, SPE 152596, SPE 167242,
                      SPE 174021, API RP-11ER, EIA AEO, Rystad
                      ShaleWellCube, IHS Markit, NACE, IOGP allocation
- Overall validation: 100.0/100 — Grade A+