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
- multiphase-flow
- beggs-brill
- flow-regime-classification
- slugging-prediction
- pvt-properties
- separator-performance
- xpertsystems
pretty_name: "OIL-018 — Synthetic Multi-Phase Flow Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-018 — Synthetic Multi-Phase Flow Dataset (Sample)

**SKU:** `OIL018-SAMPLE` · **Vertical:** Oil & Gas / Upstream Production Multiphase Flow
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil018.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise multiphase
flow dataset for flow regime classification, slugging prediction, PVT property
estimation, separator optimization, and flow assurance ML. The sample covers
**250 wells** across **12 global basins**, **7
formation types**, **5 lift types**, simulated over **60 days**
at **240-minute resolution**, with **110,150 rows** linked
across **12 tables**.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `01_wells_master.csv` | 250 | 17 | Well spine: basin, formation, lift, depth, PI, API gravity, salinity, integrity flags |
| `02_pipeline_segments.csv` | 630 | 11 | 35 pipelines × 18 segments: length, diameter, material, roughness, inclination |
| `03_multiphase_flow_timeseries.csv` | 90,000 | 13 | Per-well timeseries: oil/gas/water rate, water cut, GOR, WHP, BHP, temp, holdup, slug flag |
| `04_pressure_temperature_profiles.csv` | 4,000 | 7 | 16-depth-point P/T profile per well + phase envelope region |
| `05_flow_regimes.csv` | 3,000 | 10 | Beggs & Brill regime classification: bubble/slug/churn/annular/stratified/mist + vsl/vsg/holdup |
| `06_slugging_events.csv` | 300 | 8 | Severe slugging events: length, frequency, severity, pressure oscillation, mitigation action |
| `07_separator_performance.csv` | 3,000 | 9 | Separator P/T + oil recovery + gas efficiency + water/liquid carryover + instability per API 12J |
| `08_pvt_properties.csv` | 2,000 | 10 | Bubble point, gas Z-factor, oil/water FVF, solution GOR, oil viscosity per Vasquez-Beggs |
| `09_hydrate_wax_risk.csv` | 3,000 | 9 | P/T-conditioned hydrate risk + WAT-conditioned wax risk + scale risk + emulsion stability |
| `10_artificial_lift_behavior.csv` | 1,770 | 9 | Intake/discharge pressure + gas interference + pump efficiency/fillage + stability score |
| `11_flow_assurance_anomalies.csv` | 200 | 9 | 10-class anomaly events: severe slugging, hydrate, wax, separator, ESP gas lock, sand erosion etc. |
| `12_production_labels.csv` | 2,000 | 8 | ML labels: 4-class stability grade A/B/C/D + slugging + liquid loading + optimization flags |

Total: **110,150 rows** across 12 CSVs, ~13.2 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **Beggs & Brill (1973)** "A Study of Two-Phase
Flow in Inclined Pipes", **Mukherjee & Brill (1985)** inclined pipe regime
maps, **Hagedorn & Brown (1965)** vertical well multiphase flow gradient,
**Turner et al. (1969)** liquid loading criterion, **Standing-Katz (1942)**
gas Z-factor compressibility chart, **Lasater (1958)** bubble point
correlation, **Vasquez & Beggs (1980)** PVT correlations, **API 12J**
(Specification for Oil and Gas Separators), **API RP-14E** (pipeline
erosional velocity), **Sloan & Koh (2008)** "Clathrate Hydrates of Natural
Gases", **NACE TM0274** wax appearance temperature measurement, GPSA
Engineering Data Book, Rystad Energy + IHS Markit + EIA global production
tracker.

**Sample run** (seed `42`, n_wells=250, days=60, interval=240min):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg initial water cut pct | 35.6197 | 36.0 | ±8.0 | ✓ PASS | SPE PEH Vol V + IHS Markit global production tracker — mean initial water cut for mixed onshore/offshore portfolio (5-25% greenfield, 40-70% mature) |
| 2 | avg initial gor scf per bbl | 873.1204 | 850.0 | ±250.0 | ✓ PASS | SPE PEH Vol V + Vasquez & Beggs (1980) — mean initial gas-oil ratio for mixed oil/condensate portfolio (300-1500 scf/bbl typical, 5000+ for condensates) |
| 3 | avg pressure gradient psi per ft | 0.4199 | 0.42 | ±0.1 | ✓ PASS | Hagedorn & Brown (1965) vertical multiphase flow + Beggs & Brill (1973) — mean pressure gradient for mixed water/oil/gas column (oil column 0.30-0.38, water column 0.43-0.46 psi/ft, mixed multiphase 0.35-0.50) |
| 4 | bhp whp physical consistency | 1.0000 | 1.0 | ±0.005 | ✓ PASS | Hagedorn & Brown (1965) — BHP must exceed WHP for all producing wells (well column hydrostatic + frictional pressure loss). Validates generator's pressure model produces no physically-impossible WHP > BHP states. |
| 5 | avg separator oil recovery pct | 96.0900 | 95.0 | ±3.0 | ✓ PASS | API 12J (Specification for Oil and Gas Separators) + GPSA Engineering Data Book — typical oil recovery for production separators (93-98% for properly-sized vessels with good retention time) |
| 6 | avg separator gas efficiency pct | 93.4835 | 93.0 | ±3.0 | ✓ PASS | API 12J + GPSA Engineering Data Book — typical gas separation efficiency (90-96% for vertical/horizontal separators with mist extractor) |
| 7 | avg gas z factor | 0.8609 | 0.86 | ±0.08 | ✓ PASS | Standing-Katz (1942) gas compressibility chart — typical Z-factor for natural gas at reservoir P/T conditions (0.75-0.95 for most production scenarios; 0.86 is the median for moderate-pressure portfolios) |
| 8 | hydrate prone risk coupling | 0.1554 | 0.1 | ±0.05 | ✓ PASS | Sloan & Koh (2008) 'Clathrate Hydrates of Natural Gases' — expected positive difference in hydrate risk score between hydrate-prone wells and non-hydrate-prone wells (validates flag-conditioned risk physics; generator coefficient is 0.22 prone-flag boost) |
| 9 | wax prone risk coupling | 0.1796 | 0.15 | ±0.05 | ✓ PASS | NACE TM0274 (Wax Appearance Temperature Measurement) + Pedersen et al. (1991) — expected positive difference in wax risk score between wax-prone wells and non-wax-prone wells (validates flag-conditioned risk physics; generator coefficient is 0.18 prone-flag boost) |
| 10 | basin diversity entropy | 0.9812 | 0.95 | ±0.05 | ✓ PASS | Rystad Energy + IHS Markit + EIA global production tracker — 12-class basin diversity benchmark (Permian Delaware/Midland, Eagle Ford, Bakken, Marcellus, GoM Deepwater, North Sea, Brazil Pre-Salt, Middle East Carbonate, West Africa, Canadian Heavy Oil, North African Carbonate), normalized Shannon entropy |

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

---

## Schema highlights

**`03_multiphase_flow_timeseries.csv`** — the production spine with
**Hagedorn & Brown (1965) vertical multiphase flow physics**:

> WHP        = P_res − 0.42·depth − 0.00095·liquid_bpd − 0.018·gas_mscfd + noise
> BHP        = WHP + 0.38·depth + 0.0006·liquid_bpd
> vsl (ft/s) = liquid_bpd × 5.615 / 86400 / area_ft2
> vsg (ft/s) = gas_mscfd × 1000 / 86400 / area_ft2 × (520/T_R) × (14.7/WHP_psia)

The vsg formula applies **gas density correction at standard conditions**
(14.7 psia / 520 R = 60°F) per **GPSA Engineering Data Book** convention.
**BHP > WHP is enforced for 100% of timesteps** — physical consistency
validates the pressure model.

**`05_flow_regimes.csv`** — **Beggs & Brill (1973) inclined pipe regime
classification**:

| Regime | Trigger |
|---|---|
| `bubble` | vsg/vsl < 0.18 AND vsl ≥ 0.20 |
| `slug` | 0.18 ≤ vsg/vsl < 0.38 AND vsl ≥ 0.30 |
| `churn` | 0.38 ≤ vsg/vsl < 0.70 |
| `annular` | vsg/vsl ≥ 0.70 AND vsl ≥ 0.25 |
| `stratified` | vsl < 0.75 AND vsg < 0.75 AND \|inclination\| < 6° |
| `mist` | vsg/vsl ≥ 0.85 AND vsl < 0.50 |
| `transition` | (fallback) |

**`08_pvt_properties.csv`** — **Vasquez & Beggs (1980)** PVT correlations:

> bubble_point  = 2.2 × GOR + noise               (Lasater 1958 form)
> oil_viscosity = 14.5 / API × (1 + 0.00035 × max(P_bp − P, 0))
> Z_factor      = N(0.86, 0.06) clip(0.62, 1.08)  (Standing-Katz envelope)
> oil_FVF       = 1 + GOR/6500 + noise            (Vasquez-Beggs form)

**`07_separator_performance.csv`** — **API 12J** separator design metrics:

> oil_recovery   = 0.965 − 0.0012·max(water_cut − 40, 0) + noise
> gas_efficiency = 0.955 − 0.000045·sep_pressure + noise

Sample mean oil recovery 96.1%, gas efficiency 93.5% — **within API 12J
production-grade specifications** (93-98% oil, 90-96% gas).

**`09_hydrate_wax_risk.csv`** — **Sloan & Koh (2008)** hydrate physics + **NACE
TM0274** wax thermodynamics with **flag-conditioned risk amplification**:

> hydrate_risk = 0.18 + 8e-5·P − 4e-3·(T − 60) + 0.22·hydrate_prone_flag + noise
> wax_risk     = 0.12 + 0.45·(T < WAT) + 0.18·wax_prone_flag + noise

Hydrate risk **increases with pressure and decreases with temperature** per
hydrate stability zone physics. Wax risk uses a hard threshold below WAT
(Wax Appearance Temperature). Both have **flag-conditioned amplification**
that ML models should learn.

**`12_production_labels.csv`** — 4-class stability grade per multiphase flow
operability convention:

| Grade | Stability score |
|---|---|
| `A` | ≥ 0.80 |
| `B` | 0.60 ≤ score < 0.80 |
| `C` | 0.35 ≤ score < 0.60 |
| `D` | < 0.35 |

---

## Suggested use cases

1. **Flow regime multiclass classification** — predict
   `flow_regime` (5-7 classes) from vsl/vsg/inclination features. **Strong
   physics signal**: classification follows **Beggs & Brill (1973)**
   deterministic regime boundaries.
2. **Slugging detection** — binary classifier on `slugging_flag` from
   regime + holdup + pressure features.
3. **Liquid loading prediction** — binary classifier on
   `liquid_loading_flag` per **Turner et al. (1969)** criterion (vsg <
   2.5 ft/s AND water_cut > 0.45).
4. **PVT property regression** — predict bubble point / FVF / Z-factor
   from upstream features (depth, API gravity, GOR). Strong physical
   signal per Vasquez-Beggs (1980).
5. **Separator efficiency optimization** — regression on
   `oil_recovery_pct` and `gas_efficiency_pct` from upstream feed
   conditions. Anchors to API 12J production-grade targets.
6. **Hydrate / wax risk scoring** — regression on
   `hydrate_risk_score` / `wax_risk_score` from operating P/T +
   integrity flags. **Strong flag-conditioned coupling**: prone-flag
   risk amplification is 0.15-0.22 in the sample.
7. **10-class anomaly type classification** — multi-class classifier
   on `anomaly_type` from upstream features.
8. **4-class production stability classification** — ordinal
   classifier on `production_stability_grade` (A/B/C/D); see Honest
   Disclosure §3 for the class-imbalance caveat.
9. **Pressure gradient prediction** — regression on
   `pressure_gradient_psi_per_ft` from depth + fluid composition
   features per Hagedorn & Brown (1965).
10. **Multi-table relational ML** — entity-resolution and graph
    neural-network learning across the 12 joinable tables via
    `well_id` + `pipeline_id` + `timestamp`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil018-sample", data_files="03_multiphase_flow_timeseries.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
wells     = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/01_wells_master.csv")
ts        = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/03_multiphase_flow_timeseries.csv")
regimes   = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/05_flow_regimes.csv")
labels    = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/12_production_labels.csv")

# Join timeseries with well metadata for ML feature engineering
joined = ts.merge(wells, on="well_id")

# Flow regime classification training set:
X = regimes[["superficial_velocity_liquid_ft_s",
             "superficial_velocity_gas_ft_s",
             "inclination_deg"]]
y = regimes["flow_regime"]
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
`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 for multiphase flow / production engineering
ML research, not for live operational decisions. Several important notes:

1. **Flow regime is heavily slug-dominant (~57%) and churn-dominant (~26%).**
   The Beggs & Brill (1973) regime classifier uses superficial velocity
   ratios that naturally produce lots of slug+churn at typical wellhead
   conditions (moderate vsl, moderate vsg). The `slugging_share_estimate`
   in the generator's metrics.json reports 0.67 — physically correct for
   moderate-rate wells but means **annular and mist regimes are
   underrepresented (~0.1% and ~0% respectively)**. For class-balanced
   flow regime ML, oversample annular/mist or filter for high-velocity
   wells.

2. **Liquid loading flag fires at ~38%** because **Turner et al. (1969)**
   criterion `vsg < 2.5 ft/s AND water_cut > 0.45` triggers for many
   mature water-cut wells. This is **realistic for late-life production**
   but means liquid loading ML on this sample is biased toward mature
   wells. For greenfield liquid-loading ML, filter the timeseries to
   early-life timesteps (`day < 30`).

3. **Production stability grade is ~93% A** because the stability
   formula `1 − 0.55·slug_flag − 0.25·(water_cut > 0.75)` rarely drops
   below 0.80 in 60-day simulations. **4-class stability classification
   is heavily imbalanced at sample scale.** Use
   `optimization_candidate_score` as a continuous regression target
   instead.

4. **Reservoir pressure ranges from 1500 to 18000 psi** in the wells
   table because the generator adds `0.42·depth_ft` to the base, so
   deep wells (28000 ft cap) produce 13000+ psi reservoir P. **The
   `mean_pressure_psi: 4200` config parameter is the *intercept*, not
   the actual mean.** Observed mean is ~8450 psi due to the depth
   amplification — this is realistic for a portfolio with deep
   deepwater wells.

5. **Oil rate compound-lognormal-skew effects.** The
   `lognormal(log(1800), 0.65)` baseline + decline + season + noise
   produces observed mean ~2050 bopd vs declared 1800 — a 14%
   positive bias from compound noise sources. Realistic for production
   distributions (positive skew) but disclosed.

6. **The `04_pressure_temperature_profiles.csv` table is a snapshot**,
   not a timeseries — one profile per well at fixed depth points. For
   pressure-traverse-over-time ML, use `03_multiphase_flow_timeseries.csv`
   WHP/BHP columns instead.

7. **PVT properties are sampled per well, not per pressure step.** Each
   well gets 8 PVT samples at randomized pressures (25-115% of reservoir
   P), not a full PVT envelope. For full bubble-point-vs-pressure curve
   modeling, use the field-development simulation tools downstream of
   this dataset.

8. **Anomaly types are uniformly sampled** (~10% each) across 10
   classes, not feature-conditioned. Real anomaly distributions are
   heavily skewed (sensor drift dominates, severe slugging rarer).
   Treat anomaly_type as label-only at sample scale; full product will
   add feature-conditioned anomaly priors.

---

## Cross-references to other XpertSystems OIL SKUs

This SKU specializes in **multiphase flow / pipeline dynamics**.
Related SKUs cover complementary aspects:

| SKU | Focus | Use Case |
|---|---|---|
| **OIL-013** | Production engineering | Daily production with downtime/anomaly events at single-well scale |
| **OIL-014** | Artificial lift performance | ESP / Gas Lift / Rod Pump operations per-period |
| **OIL-015** | Flow assurance | Pipeline-only wax/hydrate/asphaltene threshold-gated deposition (midstream) |
| **OIL-018** | Multi-phase flow | Beggs-Brill regime classification + PVT + separator + per-well timeseries (this SKU) |

**OIL-018 vs OIL-015**: OIL-015 is **midstream pipeline-only flow assurance**
(wax / hydrate / asphaltene threshold gating). OIL-018 is **upstream
wellbore + facility multiphase flow** (regime classification, PVT, separator,
lift behavior). Use OIL-015 for pipeline integrity ML, OIL-018 for well-to-
separator flow modeling ML.

---

## Full product

The **full OIL-018 dataset** ships at **40,000 wells × 3,650 days × 15-min
resolution** (prod mode) producing several hundred million timeseries rows
with **feature-conditioned anomaly priors**, **proper class-balanced
stability grades** (mixed simulation durations), **full annular/mist regime
representation** (high-velocity well subset), and **per-timestep PVT envelope
modeling** — licensed commercially. Contact XpertSystems.ai for licensing
terms.

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

---

## Citation

```bibtex
@dataset{xpertsystems_oil018_sample_2026,
  title  = {OIL-018: Synthetic Multi-Phase Flow Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil018-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 13:48:46 UTC
- Wells             : 250
- Days simulated    : 60
- Time interval     : 240 min (4h)
- Pipelines         : 35 × 18 segments each
- Basins            : 12 (Permian Delaware/Midland, Eagle Ford,
                      Bakken, Marcellus, GoM Deepwater, North Sea, Brazil
                      Pre-Salt, Middle East Carbonate, West Africa,
                      Canadian Heavy Oil, North African Carbonate)
- Formation types   : 7 (carbonate, sandstone, shale, tight sand,
                      turbidite, heavy oil sand, gas condensate)
- Lift types        : 5 (natural flow, ESP, gas lift, rod pump, plunger)
- Flow regimes      : 7 (bubble, slug, churn, annular, stratified, mist,
                      transition) per Beggs & Brill (1973)
- Anomaly types     : 10 (severe slugging, hydrate plugging, wax
                      restriction, separator instability, sensor drift,
                      choke instability, liquid loading, pipeline leak,
                      ESP gas lock, sand erosion)
- Calibration basis : Beggs & Brill (1973), Mukherjee & Brill (1985),
                      Hagedorn & Brown (1965), Turner et al. (1969),
                      Standing-Katz (1942), Lasater (1958), Vasquez &
                      Beggs (1980), API 12J, API RP-14E, Sloan & Koh
                      (2008), NACE TM0274, GPSA, Rystad, IHS Markit
- Overall validation: 100.0/100 — Grade A+