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Datasets:
xpertsystems
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oil015-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Languages:
English
Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
midstream
flow-assurance
pipeline-integrity
wax-deposition
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Initial release: OIL-015 sample, 300 pipelines / 240K 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
  - 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 

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

Or with pandas:

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 

@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+