README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- midstream
- flow-assurance
- pipeline-integrity
- wax-deposition
- hydrate-formation
- asphaltene-precipitation
- multiphase-flow
- thermal-management
- xpertsystems
pretty_name: "OIL-015 — Synthetic Flow Assurance Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-015 — Synthetic Flow Assurance Dataset (Sample)

**SKU:** `OIL015-SAMPLE` · **Vertical:** Oil & Gas / Midstream Flow Assurance
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil015.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise flow
assurance dataset for wax/hydrate/asphaltene deposition ML, multiphase flow
regime classification, chemical injection optimization, and pipeline
integrity risk modeling. The sample covers **300 pipelines**
across **6 pipeline types**, with **240,300 rows**
including **30,000 hourly operating-condition records** linked
across **9 tables**.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `pipelines_master.csv` | 300 | 7 | Pipeline spine: type, length, diameter, insulation, water depth, design pressure |
| `operating_conditions.csv` | 30,000 | 8 | Per-pipeline hourly conditions: pressure, temperature, flow rate, water cut, GOR |
| `wax_deposition.csv` | 30,000 | 5 | Wax Appearance Temperature (WAT) + threshold-gated deposition rate + cumulative thickness |
| `hydrate_events.csv` | 30,000 | 5 | Hydrate risk score + volume fraction + 3-class inhibition state (active/partial/failed) |
| `asphaltene_precipitation.csv` | 30,000 | 5 | Asphaltene Onset Pressure (AOP) + threshold-gated precipitation rate + deposition index |
| `multiphase_flow.csv` | 30,000 | 6 | 5-class flow regime (slug/annular/bubble/stratified/churn) + gas/liquid fractions + slug frequency |
| `chemical_injection.csv` | 30,000 | 5 | 4-class inhibitor (MEG/methanol/wax inhibitor/asphaltene dispersant) + dosage + effectiveness |
| `thermal_profiles.csv` | 30,000 | 5 | Fluid temperature → seabed temperature with delta-T-gated heat loss |
| `integrity_risk_labels.csv` | 30,000 | 5 | Blockage probability + shutdown risk + 3-class integrity grade (LOW/MEDIUM/HIGH) |

Total: **240,300 rows** across 9 CSVs, ~13.0 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **NACE TM0274** (Wax Appearance Temperature
Measurement), **NACE SP0775** (paraffin/wax control), **Sloan & Koh (2008)**
"Clathrate Hydrates of Natural Gases" (canonical hydrate reference),
**SPE 28994 (Hammami & Raines, 1997)** asphaltene precipitation
thermodynamics, **Mandhane et al. (1974)** / **Beggs & Brill (1973)**
multiphase flow regime maps, **API RP-14E** (pipeline erosional velocity),
**ISO 13703** (offshore pipeline design), **DNV-RP-F101** (corroded
pipeline integrity), Pedersen et al. (1991) crude oil WAT studies, Rystad
Energy + IHS Markit pipeline tracker.

**Sample run** (seed `42`, n_pipelines=300, rows_per_pipeline=100):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg operating pressure psi | 4200.0231 | 4200.0 | ±800.0 | ✓ PASS | API RP-14E + ISO 13703 — typical pipeline operating pressure for mixed deepwater/offshore export/onshore gathering portfolio (typical 2000-6000 psi envelope) |
| 2 | avg operating temperature f | 145.1683 | 145.0 | ±30.0 | ✓ PASS | ISO 13703 + API RP-14E — typical pipeline operating temperature for mixed deepwater/onshore portfolio (120-180°F typical, with HPHT to 250°F+) |
| 3 | avg wax appearance temp f | 117.9561 | 118.0 | ±20.0 | ✓ PASS | NACE TM0274 (Wax Appearance Temperature Measurement) + Pedersen et al. (1991) crude oil WAT studies — typical WAT for paraffinic crude portfolio (90-150°F envelope) |
| 4 | avg asphaltene onset pressure psi | 3501.5829 | 3500.0 | ±1000.0 | ✓ PASS | SPE 28994 (Hammami & Raines, 1997) asphaltene precipitation thermodynamics — typical AOP for asphaltic crude oil portfolio (2000-5000 psi envelope) |
| 5 | wax threshold gating fidelity | 1.0000 | 0.99 | ±0.02 | ✓ PASS | NACE TM0274 wax physics — fraction of rows where deposition_rate_mm_day is correctly zero when T_fluid >= WAT (physics: no wax deposition above WAT). Validates the generator's threshold-gating logic. |
| 6 | asphaltene threshold gating fidelity | 1.0000 | 0.99 | ±0.02 | ✓ PASS | SPE 28994 (Hammami & Raines) asphaltene precipitation thermodynamics — fraction of rows where precipitation_rate is correctly zero when P_fluid >= AOP (physics: asphaltenes stay in solution above AOP). Validates the generator's threshold gating. |
| 7 | wax deposition pearson correlation | 0.7894 | 0.7 | ±0.2 | ✓ PASS | Pedersen et al. (1991) + Hammami & Raines (1997) — expected positive correlation between (WAT − T_fluid) and deposition rate when delta-T > 0 (physics: greater subcooling drives faster crystallization). Validates wax deposition rate scales with thermodynamic driving force. |
| 8 | asphaltene precipitation pearson correlation | 0.7534 | 0.65 | ±0.2 | ✓ PASS | SPE 28994 (Hammami & Raines, 1997) — expected positive correlation between (AOP − P_fluid) and precipitation rate when delta-P > 0 (physics: greater pressure deficit drives faster asphaltene flocculation). Validates precipitation rate scales with thermodynamic driving force. |
| 9 | flow regime diversity entropy | 0.9999 | 0.99 | ±0.03 | ✓ PASS | Mandhane et al. (1974) + Beggs & Brill (1973) multiphase flow regime classification — 5-class flow regime diversity benchmark (slug, annular, bubble, stratified, churn), normalized Shannon entropy. ML training portfolios typically use uniform sampling across regimes. |
| 10 | pipeline type diversity entropy | 0.9959 | 0.97 | ±0.04 | ✓ PASS | Rystad Energy + IHS Markit global pipeline tracker — 6-class pipeline-type diversity benchmark (deepwater subsea, heavy oil gathering, gas condensate, LNG feed, offshore export, shale multiphase), normalized Shannon entropy. |

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

---

## Schema highlights

**`wax_deposition.csv`** — implements **NACE TM0274** wax-thermodynamic
threshold gating:

> wat = N(118, 8)  °F
> deposition_rate = max(0, WAT − T_fluid) × U(0.01, 0.15)   mm/day

This means **wax deposition is exactly zero when T_fluid ≥ WAT** (per Pedersen
et al. 1991 thermodynamics — wax stays in solution above WAT) and scales
positively with subcooling below WAT. The sample observes ~10% of rows in
the deposition zone, matching the realistic fraction of pipeline operations
that drop below WAT.

**`asphaltene_precipitation.csv`** — implements **SPE 28994 (Hammami &
Raines)** asphaltene-thermodynamic threshold gating:

> aop = N(3500, 600)  psi
> precipitation_rate = max(0, AOP − P_fluid) × U(0.0001, 0.005)

Asphaltenes precipitate **only when P_fluid < AOP** (per asphaltene
solubility thermodynamics — asphaltenes stay in solution above onset
pressure). The sample observes ~18% of rows in the precipitation zone,
matching the realistic fraction of pipeline operations that drop below AOP.

**`thermal_profiles.csv`** — heat loss conditioned on delta-T per **ISO
13703** subsea pipeline thermal design:

> seabed_temp = N(40, 10)  °F
> heat_loss = max(0, T_fluid − T_seabed) × U(1, 10)   BTU

Heat loss only occurs when fluid is warmer than seabed (always true in this
sample given fluid temp ~145°F vs seabed ~40°F).

**`multiphase_flow.csv`** — 5-class flow regime classification per
**Mandhane et al. (1974) / Beggs & Brill (1973)** flow-regime maps:
slug / annular / bubble / stratified / churn. Sample distribution is
near-uniform (~20% each) for ML-balanced classification training.

**`integrity_risk_labels.csv`** — 3-class integrity grade derived from
blockage probability:

| Grade | Trigger |
|---|---|
| `LOW` | risk ≤ 0.45 |
| `MEDIUM` | 0.45 < risk ≤ 0.75 |
| `HIGH` | risk > 0.75 |

---

## Suggested use cases

1. **Wax deposition rate regression** — predict
   `deposition_rate_mm_day` from operating conditions
   (temperature/pressure/flow_rate) and WAT. **Strong physics signal**:
   threshold gating + delta-T correlation r ≈ 0.79.
2. **Asphaltene precipitation regression** — predict
   `precipitation_rate` from pressure conditions and AOP. **Strong
   physics signal**: threshold gating + delta-P correlation r ≈ 0.75.
3. **Flow regime classification** — multi-class (5-way) classifier on
   `flow_regime` from gas/liquid fractions + slug frequency features.
4. **Hydrate inhibition state classification** — 3-class (active/
   partial/failed) classifier for inhibition effectiveness ML.
5. **Pipeline integrity grading** — 3-class ordinal classifier on
   `integrity_grade` (LOW/MEDIUM/HIGH) — useful as label-only
   reference; see Honest Disclosure §3 for feature-engineering
   caveats.
6. **Chemical injection optimization** — regression on
   `effectiveness_pct` from inhibitor type + dosage features for
   chemical program tuning.
7. **Heat loss prediction** — regression on `heat_loss_btu` from
   fluid/seabed temperature + pipeline characteristics. Anchors to
   ISO 13703 thermal design.
8. **Multi-table relational ML** — entity-resolution and graph
   neural-network learning across the 9 joinable tables via
   `pipeline_id` + `condition_id`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil015-sample", data_files="wax_deposition.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
pipelines  = pd.read_csv("hf://datasets/xpertsystems/oil015-sample/pipelines_master.csv")
conditions = pd.read_csv("hf://datasets/xpertsystems/oil015-sample/operating_conditions.csv")
wax        = pd.read_csv("hf://datasets/xpertsystems/oil015-sample/wax_deposition.csv")
asp        = pd.read_csv("hf://datasets/xpertsystems/oil015-sample/asphaltene_precipitation.csv")

# Wax deposition is keyed by condition_id (embedded in wax_id):
wax["condition_id"] = wax["wax_id"].str.replace("WAX-", "", regex=False)
wax_joined = wax.merge(conditions, on="condition_id")
# Now you have WAT + T_fluid + P_fluid features ready for ML
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
both `random.seed` and `np.random.seed`). 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 flow assurance ML research, not for live
pipeline operations decisions. Several important notes:

1. **Operating conditions are independent of pipeline characteristics.**
   The generator samples pressure/temperature/flow_rate from fixed
   Gaussians, *not* conditioned on pipeline diameter, water depth, design
   pressure, or insulation type. This is a deliberate simplification for
   ML-balanced training but is not realistic — real deepwater pipelines
   run colder than onshore (better heat transfer to seabed), and design
   pressure limits operating pressure. For physics-realistic conditioning
   ML, treat operating conditions as features and pipeline characteristics
   as separate features rather than expecting cross-table coupling.

2. **Hydrate events have no thermodynamic gating.** Real hydrate
   formation occurs **inside the hydrate envelope** (low T, high P region
   per Sloan & Koh 2008). The generator samples `hydrate_risk_score`
   from `N(0.35, 0.15)` independent of T/P, so hydrate risk is not
   physically coupled to operating conditions. **This is a significant
   simplification.** For hydrate ML that requires T-P-conditioned
   risk, use a Sloan & Koh CSMHYD-style envelope calculation on top
   of operating conditions, or wait for the full product v1.1 which
   will introduce envelope-aware hydrate gating.

3. **`integrity_risk_labels.csv` is feature-decoupled.** The blockage
   probability is sampled from `N(0.25, 0.15)` independent of wax
   deposition rate, asphaltene precipitation, hydrate risk, or other
   upstream features. **Models trained to predict
   `integrity_grade` from upstream features will not learn meaningful
   patterns** because the label is not a function of the features.
   This is a generator design choice (likely placeholder for future
   coupling). For integrity-grade ML, build a derived label from
   weighted combinations of `wax_thickness_mm`, `hydrate_risk_score`,
   `precipitation_rate`, and `heat_loss_btu` rather than using the
   sampled label directly. The integrity_risk_labels table is best
   used as a **reference distribution** for production label
   calibration, not as a supervised ML target.

4. **Chemical injection effectiveness is uniform-sampled.** The
   `effectiveness_pct` column from `U(40, 99)` is not tied to
   inhibitor type, dosage, or operating conditions — so an "MEG @
   100 ppm in a wax-deposition scenario" might show 95% effectiveness
   in the sample, which is physically wrong (MEG is a hydrate
   inhibitor, not a wax inhibitor). **Inhibitor-effectiveness ML on
   this sample will learn marginals, not couplings.** For
   physically-correct inhibitor-effectiveness ML, post-process the
   data to align inhibitor type with deposition type, or wait for
   the full product v1.1.

5. **Wax/asphaltene rates are dimensionless multipliers**, not
   physically calibrated. The wax `U(0.01, 0.15)` multiplier and
   asphaltene `U(0.0001, 0.005)` multiplier produce rates in the
   correct order of magnitude (mm/day for wax, dimensionless flux
   for asphaltene) but are not calibrated to specific crude
   compositions. For absolute-rate prediction, the labels need
   recalibration against the user's crude assay; for relative
   ranking ML (e.g., "which pipeline is most at risk"), the relative
   ordering is preserved.

6. **No time-series autocorrelation across hourly steps.** Each row
   in `operating_conditions.csv` is sampled independently — there's
   no Markov / AR / drift modeling across consecutive hours. **Time-
   series ML that relies on temporal smoothness will not get
   realistic signal from this data.** Treat the sample as a panel
   of independent observations, not as time-series.

7. **Pipeline length / diameter are uniformly distributed**, not
   conditioned on pipeline type. Real LNG feed lines run 36"+ for
   high-volume gas transport; real heavy-oil gathering lines are
   typically 6-12". The sample uses U(4, 36) inches across all
   types. For type-conditional ML, post-process the data with
   industry-standard pipeline-sizing priors.

---

## Full product

The **full OIL-015 dataset** (in development) will ship at 5,000+ pipelines
× 8,760 hourly records (1 full year) with **physics-conditioned hydrate
envelope gating** (Sloan & Koh CSMHYD-style), **type-conditional
pipeline sizing**, **coupled integrity-risk labels** derived from upstream
deposition features, and **inhibitor-specific effectiveness coupling** —
licensed commercially. Contact XpertSystems.ai for licensing terms.

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

---

## Citation

```bibtex
@dataset{xpertsystems_oil015_sample_2026,
  title  = {OIL-015: Synthetic Flow Assurance Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil015-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 13:17:09 UTC
- Pipelines         : 300
- Rows per pipeline : 100 (~hourly for ~4 days)
- Pipeline types    : 6 (deepwater subsea, heavy oil
                      gathering, gas condensate, LNG feed, offshore
                      export, shale multiphase)
- Insulation types  : 5 (wet insulation, pipe-in-pipe, foam, none,
                      vacuum insulated)
- Flow regimes      : 5 (slug, annular, bubble, stratified, churn)
- Inhibitor types   : 4 (MEG, methanol, wax inhibitor, asphaltene
                      dispersant)
- Calibration basis : NACE TM0274, NACE SP0775, Sloan & Koh (2008),
                      SPE 28994 (Hammami & Raines), Mandhane (1974),
                      Beggs & Brill (1973), API RP-14E, ISO 13703,
                      DNV-RP-F101, Pedersen (1991), Rystad, IHS
- Overall validation: 100.0/100 — Grade A+