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oil024-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
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100K<n<1M
Tags:
Synthetic
oil-and-gas
midstream
pipeline
hydraulics
darcy-weisbach
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Initial release: OIL-024 sample, 55 pipelines / 369 segments / 170K 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
  - pipeline
  - hydraulics
  - darcy-weisbach
  - swamee-jain
  - leak-detection
  - scada
  - flow-assurance
  - xpertsystems
pretty_name: OIL-024 — Synthetic Pipeline Flow Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-024 — Synthetic Pipeline Flow Dataset (Sample)

SKU: OIL024-SAMPLE · Vertical: Oil & Gas / Midstream Pipeline Operations License: CC-BY-NC-4.0 (sample) · Schema version: oil024.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise pipeline flow dataset for hydraulic modeling, leak detection, flow assurance, SCADA telemetry analytics, pump/compressor optimization, and pipeline reliability ML. The sample covers 55 pipelines with 369 segments across 10 pipeline types, 10 fluid families, 10 global regions, and 7 terrain types, with 150,137 rows linked across 12 tables spanning 7 days of 180-minute time- series.

OIL-024 has the strongest hydraulic-physics anchoring of any OIL SKU — full Swamee-Jain (1976) friction factor + Darcy-Weisbach (1857) pressure drop + Newton's cooling thermal model + Sloan-Koh hydrate physics implemented end-to-end through 369-segment hydraulic profiles.

What's in the box

File Rows Cols Description
pipeline_master.csv 369 21 Pipeline + segment catalog: 10 fluid types × 10 regions × 7 terrains × 5 API 5L grades × 6 coatings × MAOP + design flow + pigging/SCADA flags
hydraulic_profiles.csv 20,664 14 Darcy-Weisbach pressure drop + Swamee-Jain friction + Reynolds + flow regime classification (laminar/transition/turbulent) + mass balance error
thermal_profiles.csv 20,664 9 Temperature + ambient + Newton's cooling heat loss + thermal gradient + Joule-Thomson cooling
pump_station_operations.csv 3,752 9 API 610 pump performance: RPM + efficiency + suction/discharge pressure + power + cavitation risk
compressor_operations.csv 1,344 8 API 617 compressor performance: compression ratio + efficiency + fuel gas consumption (gas pipelines only)
valve_operations.csv 20,664 7 Per-segment valve operations: position + throttling state + control response latency
transient_events.csv 21 8 15-class event taxonomy: pump trip, compressor trip, surge, water hammer, slugging, hydrate risk, leak, pigging run, etc.
flow_assurance.csv 20,664 8 Wax / hydrate / slugging risk scores + wax deposition thickness per Sloan-Koh (2008)
leak_detection.csv 3 9 API 1130 + API RP 1175 CPM leak detection: leak rate + pressure signature + detection delay
corrosion_erosion.csv 20,664 8 NACE SP0169 corrosion rate + erosion index + wall loss + leak probability
scada_telemetry.csv 20,664 10 SCADA sensor telemetry: signal value + telemetry latency + quality + drift flag
optimization_labels.csv 20,664 8 FEATURE-COUPLED ML labels: optimization score + failure risk + 4-class efficiency grade (A/B/C/D) + recommended action

Total: 150,137 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: Swamee & Jain (1976) "Explicit equations for pipe-flow problems", Colebrook-White (1939) turbulent friction factor, Darcy-Weisbach (1857) pressure drop equation, Moody (1944) Moody chart, Reynolds (1883) laminar-turbulent transition, Hagen- Poiseuille (1839) laminar friction (64/Re), API 5L (Line Pipe), ASME B31.4 (Liquid Hydrocarbon Pipelines), ASME B31.8 (Gas Transmission Pipelines), API 1130 (Computational Pipeline Monitoring), API RP 1175 (Pipeline Leak Detection), NACE SP0169 (External Corrosion Control), Sloan & Koh (2008) hydrate thermodynamics, PHMSA pipeline safety statistics, CSA Z662 (Canadian Oil/Gas Pipeline Systems), API 610 (Centrifugal Pumps), API 617 (Axial and Centrifugal Compressors), Newton's law of cooling.

Sample run (seed 42, n_pipelines=55, days=7, interval=180min):

# Metric Observed Target Tolerance Status Source
1 avg diameter in 21.9704 22.0 ±6.0 ✓ PASS API 5L Line Pipe specification + ASME B31.4/B31.8 — mean nominal pipe diameter for mixed transmission portfolio (8-48 inch standard sizes; 22 inch median for mixed crude/gas/refined products mix per PHMSA pipeline inventory)
2 avg maop psi 1501.7934 1480.0 ±250.0 ✓ PASS ASME B31.4 (Liquid Hydrocarbon Pipelines) + ASME B31.8 (Gas Transmission) — typical MAOP for transmission pipelines (1000-2000 psi normal range per PHMSA; 1480 psi reflects mixed portfolio mean)
3 avg reynolds number 1069625.4263 1000000.0 ±600000.0 ✓ PASS Reynolds (1883) laminar-turbulent transition + Moody (1944) — typical operating Reynolds number for transmission pipelines (200K-2M typical for crude/gas; 1M reflects high-flow turbulent regime per Darcy-Weisbach analysis)
4 avg friction factor 0.0156 0.016 ±0.008 ✓ PASS Swamee-Jain (1976) friction factor + Moody (1944) Moody chart — typical Darcy friction factor for turbulent transmission pipeline operation (0.010-0.025 range per ε/D ratios of 0.0001-0.001)
5 avg mass balance error pct 0.1719 0.2 ±0.15 ✓ PASS API 1130 Computational Pipeline Monitoring + API RP 1175 — typical mass balance error for SCADA-instrumented pipelines (0.05-0.5% normal; >1% triggers leak alarm per API standards)
6 avg pump efficiency pct 72.7872 73.0 ±6.0 ✓ PASS API 610 (Centrifugal Pumps for Petroleum) — typical pump efficiency for transmission pipeline pump stations (68-82% at BEP per Hydraulic Institute; demand-modulated operation reduces from peak)
7 thermal heat loss pearson correlation 0.8272 0.75 ±0.15 ✓ PASS Newton's law of cooling (heat_loss ∝ ΔT) — expected strong positive correlation between (pipe_temp - ambient_temp) and heat_loss_btu_hr_ft per fundamental heat transfer physics. Validates generator's thermal model.
8 optimization failure pearson correlation -0.9944 -0.95 ±0.1 ✓ PASS Generator's deterministic formula: failure_risk = (100 - optimization_score) × 0.7 + leak_prob × 0.3. Near-deterministic inverse coupling validates feature-coupled label generation for pipeline reliability ML.
9 corrosion leak pearson correlation 0.9708 0.85 ±0.1 ✓ PASS NACE SP0169 External Corrosion Control + API 1130 + PHMSA pipeline incident data — expected strong positive correlation between corrosion rate and leak probability (generator formula: leak_prob = corrosion/25 × 50 + erosion × 0.12). Validates integrity-leak physics.
10 pipeline type diversity entropy 0.9596 0.92 ±0.04 ✓ PASS 10-class pipeline type taxonomy per ASME B31.4/B31.8 + PHMSA classification (crude oil transmission, natural gas transmission, refined products, offshore subsea flowline, multiphase gathering, water injection, CO2 transport, heavy oil diluent, LNG transfer, hydrogen-ready), normalized Shannon entropy

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

Schema highlights

pipeline_master.csv — 10-class pipeline type taxonomy per PHMSA / ASME B31.4 / B31.8:

Type Weight Typical Fluid
crude_oil_transmission 20% crude (light/medium/heavy)
natural_gas_transmission 18% dry/wet gas
refined_products 11% gasoline / diesel / jet
offshore_subsea_flowline 10% multiphase
multiphase_gathering 12% mixed wellhead fluids
water_injection 7% brine / produced water
co2_transport 6% CO2 (dense phase)
heavy_oil_diluent 6% bitumen + diluent
lng_transfer 5% LNG (cryogenic)
hydrogen_ready 5% H2 / H2 blends

Material grades: API 5L X42 / X52 / X60 / X65 / X70 — standard transmission- grade pipe per API 5L specification.

hydraulic_profiles.csv — Swamee-Jain (1976) friction + Darcy-Weisbach (1857) pressure drop implementation:

Re = ρ × v × D / μ (Reynolds 1883) f_lam = 64 / Re (Hagen-Poiseuille, Re < 2300) f_turb = 0.25 / [log10(ε/(3.7D) + 5.74/Re^0.9)]² (Swamee-Jain) dp_friction = f × (L/D) × (ρ × v² / 2) (Darcy-Weisbach) dp_elev = ρ × g × Δh (Bernoulli) dp_total = (dp_friction + dp_elev) × wax_factor

PVT-aware fluid properties for 10 fluid families with temperature and pressure dependence:

Fluid Density (kg/m³) Viscosity (Pa·s)
Light crude ~790 × (1 - 0.00038·ΔT) 0.004 × exp(-0.022·(T-100))
Medium crude ~850 0.018
Heavy crude ~930 0.12
Dry gas 48 × (P/1000) × (520/T_R) 1.2e-5
LNG 430 × (1 - 0.0006·(T+260)) 1.2e-4
CO2 700 × (P/1500)^0.15 7e-5
Hydrogen blend 18 × (P/1000) × (520/T_R) 9e-6

thermal_profiles.csv — Newton's law of cooling:

heat_loss_btu_hr_ft ∝ (pipe_temp - ambient_temp) joule_thomson_cooling ∝ pressure_drop (gas pipelines, ~0.002 multiplier)

The sample's (temp-ambient) ↔ heat_loss Pearson correlation is r ≈ +0.83 — strong positive coupling validates Newton's cooling physics.

flow_assurance.csv — Sloan-Koh (2008) hydrate physics:

wax_risk = (118 - temp_f) × 1.8 + viscosity × 0.35 + current_wax × 600 hydrate_risk = (95 - temp_f) × 2.2 + pressure/50 + 20·is_gas slugging_risk = multiphase × N(45, 15) + 20 × |sin(t/7)|

leak_detection.csv — API 1130 + API RP 1175 computational pipeline monitoring leak detection:

leak_rate_bpd = lognormal(2.8, 0.9) clip 2-850 pressure_signature = 50 + leak_rate/8 + noise detection_delay_sec = lognormal(7.0, 0.8) clip 60-86400

optimization_labels.csv — deterministic feature-coupled labels:

opt_score = 100 - |demand - 0.92| × 60 - max(wax, hydrate) × 0.18 - leak_prob × 0.22 - anomaly × 8 failure_risk = (100 - opt_score) × 0.7 + leak_prob × 0.3 grade = 'A' if opt_score ≥ 85 else 'B' if ≥ 70 else 'C' if ≥ 55 else 'D'

The sample's optimization↔failure Pearson correlation is r ≈ −0.994 — near-deterministic inverse coupling validates formula-level label generation.

Suggested use cases

  1. Pressure drop prediction — regression on pressure_drop_psi from flow + diameter + roughness + temp features. Strong physics: Darcy-Weisbach signal validated.
  2. Friction factor prediction — predict friction_factor from Reynolds + roughness ratio per Swamee-Jain / Moody chart.
  3. Leak probability scoring — regression on leak_probability_score per NACE SP0169 + API 1130. Strong physics: corrosion-leak r ≈ +0.97.
  4. Flow assurance binary classification — predict flow_assurance_state == 'critical' from temperature + viscosity + pressure features per Sloan-Koh.
  5. Wax deposition forecasting — time-series forecasting of wax_deposition_thickness_mm per coupled accumulation physics.
  6. Pipeline efficiency grade classification — 4-class ordinal classifier on efficiency_grade (A/B/C/D). Strong feature coupling — models WILL learn meaningful patterns.
  7. Pump cavitation prediction — regression on cavitation_risk_score from RPM + demand features per API 610 + Hydraulic Institute.
  8. 15-class transient event classification — multi-class classifier on event_type (rare events at sample scale; see Honest Disclosure §1).
  9. Mass balance anomaly detection — anomaly detection on mass_balance_error_pct per API 1130 CPM leak detection.
  10. Multi-table relational ML — entity-resolution + graph neural- network learning across the 12 joinable tables via pipeline_id, segment_id, timestamp.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil024-sample", data_files="hydraulic_profiles.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
master = pd.read_csv("hf://datasets/xpertsystems/oil024-sample/pipeline_master.csv")
hyd    = pd.read_csv("hf://datasets/xpertsystems/oil024-sample/hydraulic_profiles.csv")
thm    = pd.read_csv("hf://datasets/xpertsystems/oil024-sample/thermal_profiles.csv")
fa     = pd.read_csv("hf://datasets/xpertsystems/oil024-sample/flow_assurance.csv")
labels = pd.read_csv("hf://datasets/xpertsystems/oil024-sample/optimization_labels.csv")

# Full multi-table feature engineering:
joined = (hyd
    .merge(thm, on=["timestamp", "pipeline_id", "segment_id"])
    .merge(fa,  on=["timestamp", "pipeline_id", "segment_id"])
    .merge(labels, on=["timestamp", "pipeline_id", "segment_id"])
    .merge(master, on=["pipeline_id", "segment_id"]))
# Predict efficiency_grade from hydraulics + thermal + flow assurance features

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 calibrated for pipeline ML research, not for live operational decisions. Several notes:

  1. Flow regime is ~100% turbulent because transmission pipeline Reynolds numbers naturally exceed 4000 (sample mean Re ≈ 1.1M). Laminar regime is essentially absent at sample scale — only pipelines with very viscous fluids (heavy crude) at very low flow rates would produce laminar flow. For laminar-regime ML, filter to primary_fluid == 'heavy_crude' AND flow_bpd < 5000 or wait for the full product which adds explicit low-flow scenarios.

  2. Pump cavitation risk is ~0 because the generator's formula (rpm - 2700)/12 + N(0, 5) clips at 0 for typical 1750 rpm operation. Real cavitation risk requires NPSH-margin physics (OIL-021 implements this correctly). For pump cavitation ML, use OIL-021 instead; OIL-024 cavitation is a placeholder.

  3. Integrity state is 100% 'normal' at 7-day sample horizon because leak_probability > 40 threshold (for 'watch' classification) requires longer-horizon corrosion accumulation. The full product (30-day prod mode) and full-scale simulations show meaningful 'watch' and 'high_risk' populations. For integrity-state classification ML, use the full product or augment with external corrosion-progression simulations.

  4. Transient events are sparse (~20 events per 55 pipelines at sample scale). For 15-class event-type classification ML, use the full product (12000+ pipelines for 30 days) or filter to pipeline-day aggregated event flags rather than per-timestep events.

  5. Leak events are very rare (~3 leak entries across 55 pipelines). Leak ML requires the full product. For leak detection ML at sample scale, use leak_probability_score regression rather than the binary leak flag.

  6. Sensor drift flag rate is ~0.04% because the generator only sets drift_flag = True when event_type == 'sensor_drift', which is one of 15 event types at 0.6% rare event rate. For sensor drift ML, use full product or rely on signal-quality quality_flag instead.

  7. Flow ↔ pressure drop correlation is weak (r ≈ +0.06) because the relationship is dominated by between-segment variance in diameter, length, and fluid type rather than within-segment flow-pressure scaling. For Darcy-Weisbach ML, normalize pressure drop per-segment (dp/L or use friction factor) before fitting. Within a single segment, the relationship is strong (r > 0.85 typically).

  8. Flow assurance critical rate is ~38% — higher than realistic field operation (5-15% typical). The generator's wax_risk formula (118-temp) × 1.8 triggers easily at ambient temperatures; real field operation includes insulation and heat-tracing that reduces wax onset. For realistic flow assurance ML, filter critical classifications by insulation type (insulation_type in ['high', 'subsea_wet_insulated'] for realistic thermal-protected operation).

Cross-references to other XpertSystems OIL SKUs

This SKU is the second midstream SKU in the catalog (after OIL-015 flow assurance):

SKU Layer Focus
OIL-015 Midstream Pipeline flow assurance (wax / hydrate / asphaltene threshold gating)
OIL-024 Midstream Full pipeline hydraulics + SCADA + leak detection + transient events (this SKU)
OIL-018 Upstream Wellbore-to-separator multiphase flow (Beggs-Brill regime classification)

OIL-024 vs OIL-015: OIL-015 specializes in flow-assurance-only threshold-gated wax/hydrate/asphaltene deposition. OIL-024 is the comprehensive midstream pipeline operations dataset — full Swamee-Jain hydraulics + Darcy-Weisbach pressure drop + thermal profiles + SCADA telemetry + leak detection + 15-class transient event taxonomy + 10-fluid- type PVT-aware physics. Use OIL-015 for flow-assurance ML specifically, OIL-024 for general pipeline operations / SCADA / hydraulic ML.

OIL-024 vs OIL-018: OIL-018 simulates wellbore-to-separator multiphase flow with Beggs-Brill regime classification (upstream). OIL-024 simulates midstream transmission pipelines with single-phase or multiphase flow assurance (downstream of separator). Use OIL-018 for upstream production multiphase ML, OIL-024 for midstream transmission pipeline ML.

Full product

The full OIL-024 dataset ships at 12,000 pipelines × 30 days × 60-min interval (prod mode) producing tens of millions of rows with richer event populations (1000+ events for class-balanced 15-class ML), full NPSH-conditioned pump cavitation physics, multi-day leak event populations, realistic flow assurance critical rates (insulation- conditioned), and explicit laminar-flow scenarios (heavy-crude low-flow regimes) — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil024_sample_2026,
  title  = {OIL-024: Synthetic Pipeline Flow Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil024-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-22 21:05:15 UTC
  • Pipelines : 55
  • Segments : 369 (avg 6.7 per pipeline)
  • Simulation days : 7
  • Time-step interval: 180 minutes
  • Fluid families : 10 (light/medium/heavy crude, dry/wet gas, refined product, water, CO2, LNG, hydrogen blend)
  • Pipeline types : 10 (crude oil / natural gas / refined products / offshore subsea / multiphase gathering / water injection / CO2 transport / heavy oil diluent / LNG / hydrogen-ready)
  • Regions : 10 (Permian Basin, Gulf Coast, North Sea, Middle East, Canadian Oil Sands, Offshore Brazil, West Africa, Alaska Arctic, Rocky Mountains, Appalachia)
  • Terrains : 7 (flat, rolling, mountainous, offshore, subsea, arctic, desert)
  • Event types : 15 (pump trip, compressor trip, valve throttle, emergency shutdown, restart, surge, water hammer, slugging, hydrate risk, wax deposition, SCADA outage, sensor drift, leak, corrosion alarm, pigging run)
  • Calibration basis : Swamee-Jain (1976), Darcy-Weisbach (1857), Moody (1944), Reynolds (1883), Hagen-Poiseuille (1839), Colebrook-White (1939), API 5L, ASME B31.4/B31.8, API 1130, API RP 1175, NACE SP0169, Sloan-Koh (2008), PHMSA, CSA Z662, API 610, API 617
  • Overall validation: 100.0/100 — Grade A+