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Datasets:
xpertsystems
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oil027-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Languages:
English
Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
midstream
pipeline
corrosion
cathodic-protection
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Initial release: OIL-027 sample, 1300 pipelines / 245K 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
  - corrosion
  - cathodic-protection
  - nace-sp0169
  - rbi
  - integrity-management
  - xpertsystems
pretty_name: OIL-027 — Synthetic Pipeline Corrosion Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-027 — Synthetic Pipeline Corrosion Dataset (Sample)

SKU: OIL027-SAMPLE · Vertical: Oil & Gas / Midstream Pipeline Integrity License: CC-BY-NC-4.0 (sample) · Schema version: oil027.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise pipeline corrosion dataset for corrosion rate prediction, internal/external corrosion classification, cathodic protection optimization, pitting analysis, and integrity grade ML. The sample covers 1,300 pipelines across 4 environments (Onshore / Offshore / Subsea / Arctic) and 3 API 5L material grades, with 245,045 rows linked across 5 tables spanning 180 days of daily corrosion progression.

OIL-027 specializes in the corrosion-physics layer of pipeline integrity management — implementing NACE SP0169 cathodic protection threshold gating (the -0.85V criterion) and de Waard-Milliams water-cut corrosion coupling in a 5-table relational schema joinable on pipeline_id.

What's in the box

File Rows Cols Description
pipeline_assets.csv 1,300 9 Pipeline catalog: 3 API 5L material grades (X52, X65, X70) × 4 environments × diameter + wall thickness + age + CO2 + H2S + water cut
internal_corrosion.csv 234,000 6 180-day daily corrosion progression: rate (de Waard-Milliams water-cut coupling), accumulated wall loss, temperature, pressure
external_corrosion.csv 1,300 5 NACE SP0169 cathodic protection: soil resistivity per NACE TM0497, CP voltage, coating health, 3-class risk (LOW/MEDIUM/HIGH) physics-gated
pitting_profiles.csv 7,145 5 Per-pipeline pit catalog: depth + width + growth rate (avg 5.5 pits per pipeline per ASTM G46 pit density)
integrity_labels.csv 1,300 4 Per-pipeline 4-class integrity grade (LOW/MEDIUM/HIGH/CRITICAL) + risk score + remaining life days

Total: 245,045 rows across 5 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: NACE SP0169 (External Corrosion Control of Buried Pipelines — Cathodic Protection -0.85V Criterion), NACE MR0175 / ISO 15156 (Sulfide Stress Cracking in H2S Service), NACE TM0497 (Soil Resistivity Measurement), de Waard & Milliams (1991) CO2 Corrosion Prediction Model, API 510 (Pressure Vessel Inspection Code), API 570 (Piping Inspection Code), API 580/581 (Risk-Based Inspection), API 5L (Line Pipe), ASME B31.4 (Liquid Hydrocarbon Pipelines), ASME B31.8 (Gas Transmission Pipelines), PHMSA 49 CFR 195 (Hazardous Liquid Pipeline Safety), NACE SP0502 (Pipeline External Corrosion Direct Assessment), API 1163 (In-Line Inspection Systems), ASTM G1 (Cleaning Corrosion Specimens), ASTM G46 (Pit Density Standard Charts).

Sample run (seed 42, n_pipelines=1300, days=180):

# Metric Observed Target Tolerance Status Source
1 avg diameter in 25.1554 25.0 ±6.0 ✓ PASS API 5L Line Pipe specification + PHMSA pipeline inventory — mean diameter for mixed transmission portfolio (8-42 inch range; 25 inch median for crude/gas mainline operations)
2 avg wall thickness mm 16.5198 16.5 ±4.0 ✓ PASS API 5L + ASME B31.4/B31.8 design wall thickness — typical wall thickness for transmission pipelines (8-25 mm range; 16-17 mm mid-portfolio for 600-1500 psi MAOP)
3 avg corrosion rate mpy 6.3514 6.0 ±3.0 ✓ PASS API 570 (Piping Inspection Code) + NACE TM0274 — mean corrosion rate for mixed pipeline portfolio with moderate water cut (2-10 mpy normal; >15 mpy triggers RBI high-risk classification per API 581)
4 avg cp voltage v -0.9440 -0.95 ±0.2 ✓ PASS NACE SP0169 (External Corrosion Control) — typical cathodic protection potential for buried pipelines (-0.85V vs Cu/CuSO4 minimum protection criterion; -1.0 to -1.2V for fully-protected pipelines)
5 avg soil resistivity ohm cm 2567.9367 2500.0 ±1000.0 ✓ PASS NACE TM0497 (Soil Resistivity Measurement) + NACE SP0169 — typical soil resistivity for mixed onshore/offshore/subsea/arctic portfolio (100-5000 ohm-cm range; <1000 ohm-cm is highly corrosive per NACE classification)
6 avg coating health pct 79.8284 80.0 ±8.0 ✓ PASS API 1163 (In-Line Inspection Systems) + NACE SP0502 (External Corrosion Direct Assessment) — typical coating health for mid-life transmission pipelines (70-90% typical; FBE coating degrades 2-4% per decade)
7 water cut corrosion pearson correlation 0.3275 0.3 ±0.12 ✓ PASS de Waard & Milliams (1991) CO2 corrosion prediction model — expected positive correlation between water cut and corrosion rate (generator formula: rate = base × temp_factor × (1 + water_cut/100); within-pipeline coupling deterministic, cross-pipeline base-rate variation dilutes global correlation). Validates water-conditioned corrosion physics.
8 cp voltage external risk pearson correlation 0.6422 0.55 ±0.15 ✓ PASS NACE SP0169 -0.85V cathodic protection criterion — expected strong positive correlation between CP voltage (less negative) and external corrosion risk numeric (LOW=0/MEDIUM=1/HIGH=2). Validates generator's physics-gated risk classification per NACE.
9 soil resistivity external risk pearson correlation -0.3809 -0.3 ±0.15 ✓ PASS NACE SP0169 + NACE TM0497 soil corrosivity classification — expected inverse correlation between soil resistivity and external corrosion risk numeric (low resistivity soil drives high corrosion). Validates generator's NACE-anchored gating.
10 material grade diversity entropy 0.9998 0.96 ±0.04 ✓ PASS API 5L Line Pipe material grade taxonomy (X52, X65, X70) — 3-class diversity benchmark for mixed transmission portfolio per API 5L specification + PHMSA pipeline inventory, normalized Shannon entropy

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

Schema highlights

pipeline_assets.csv — 3 × 4 portfolio matrix per API 5L + PHMSA:

Material Grade Use Case Specified Min Yield Strength (psi)
API 5L X52 Older transmission / gathering 52,000
API 5L X65 Modern transmission mainline 65,000
API 5L X70 High-pressure transmission 70,000

4 environment classes per NACE SP0169 classification:

Environment Corrosion Drivers
Onshore Soil chemistry, stray current, AC interference
Offshore Splash zone, marine atmosphere, oxygen ingress
Subsea Cathodic protection mandatory, biofouling
Arctic Permafrost cycling, ice gouging, low temperature stress

internal_corrosion.csv — de Waard-Milliams (1991) water-cut coupling:

corrosion_rate_mpy = base_rate × temp_factor × (1 + water_cut/100) wall_loss_pct = corrosion_rate × day/365 × 0.1 (deterministic accumulation)

The sample's water cut ↔ corrosion rate Pearson correlation is r ≈ +0.33 — moderate positive coupling validates de Waard-Milliams water-cut physics (within-pipeline coupling is deterministic; cross-pipeline base- rate variability dilutes the global correlation).

external_corrosion.csv — NACE SP0169 -0.85V cathodic protection threshold gating:

external_corrosion_risk = HIGH if (soil_resistivity < 800 OR cp_voltage > -0.85) external_corrosion_risk = MEDIUM if soil_resistivity < 1500 external_corrosion_risk = LOW otherwise

The sample's CP voltage ↔ external risk Pearson correlation is r ≈ +0.64 — strong positive coupling validates NACE SP0169 -0.85V cathodic protection criterion physics.

Risk class distribution: 49% LOW / 10% MEDIUM / 41% HIGH — meaningful 3-class diversity for ML class-balancing (better than degenerate single-class outcomes).

pitting_profiles.csv — per-pipeline pit catalog per ASTM G46 pit density standard:

n_pits_per_pipeline = U(2, 9) pit_depth_mm = U(0.5, 12) pit_width_mm = U(1, 25) growth_rate_mm_year = U(0.05, 1.5)

Suggested use cases

  1. Internal corrosion rate regression — predict corrosion_rate_mpy from water cut + environment features per de Waard-Milliams (1991). Strong physics signal within-pipeline; moderate global.
  2. External corrosion risk classification — 3-class classifier on external_corrosion_risk from soil resistivity + CP voltage + coating features per NACE SP0169. Strong physics: CP↔risk r ≈ +0.64.
  3. Wall loss time-series forecasting — predict accumulated wall_loss_pct over 180-day horizon per API 510 remaining life calculations.
  4. Pit growth rate regression — predict growth_rate_mm_year from pit depth + width features per ASTM G46.
  5. Cathodic protection optimization — predict CP voltage thresholds from soil resistivity + coating health features per NACE SP0169.
  6. Integrity grade classification — 4-class classifier on integrity_grade (LOW/MEDIUM/HIGH/CRITICAL). Note: integrity labels in this sample are not feature-coupled (see Honest Disclosure §1).
  7. Multi-table relational ML — entity-resolution learning across the 5 tables via pipeline_id. Join asset metadata with corrosion time-series for feature-rich ML pipelines.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil027-sample", data_files="internal_corrosion.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
assets   = pd.read_csv("hf://datasets/xpertsystems/oil027-sample/pipeline_assets.csv")
internal = pd.read_csv("hf://datasets/xpertsystems/oil027-sample/internal_corrosion.csv")
external = pd.read_csv("hf://datasets/xpertsystems/oil027-sample/external_corrosion.csv")
pits     = pd.read_csv("hf://datasets/xpertsystems/oil027-sample/pitting_profiles.csv")
labels   = pd.read_csv("hf://datasets/xpertsystems/oil027-sample/integrity_labels.csv")

# Multi-table corrosion feature engineering:
corr_avg = internal.groupby("pipeline_id")["corrosion_rate_mpy"].mean().reset_index()
joined = (assets
    .merge(corr_avg, on="pipeline_id")
    .merge(external, on="pipeline_id")
    .merge(labels, on="pipeline_id"))
# Now you have water_cut + corrosion_rate + CP voltage + integrity_grade in one frame

Reproducibility

All generation is deterministic via the integer seed parameter (driving np.random.seed and 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 calibrated for pipeline corrosion ML research, not for live operational decisions. Several important limitations should be understood before use:

  1. Integrity grade is NOT feature-coupled. The 4-class integrity_grade label is sampled from np.random.uniform(0, 1) with threshold gates, without any coupling to corrosion_rate, wall_loss, pitting depth, or external_corrosion_risk. The sample's integrity_grade↔corrosion_rate correlation is r ≈ +0.04 — essentially noise. For integrity ML, train on labels you derive yourself from the physics features (e.g., wall_loss_pct > 30% → CRITICAL) rather than the provided integrity_grade. The full product (v1.1) will implement feature-coupled integrity grading per API 580 RBI methodology.

  2. Remaining life is NOT physics-computed. The remaining_life_days field is sampled from np.random.uniform(180, 7200) and is not computed from corrosion rate or wall loss per API 510 RBI. Real remaining life = (current_wall_thickness - retirement_limit) / corrosion_rate. For RUL ML, derive remaining life from internal_ corrosion features rather than using the provided field.

  3. CO2 and H2S do not drive corrosion rate. The pipeline_assets table includes co2_pct and h2s_ppm fields, but neither is used in the corrosion rate calculation. Real CO2 corrosion follows de Waard 1991 rate ∝ partial_pressure_CO2^0.67 × temp^1.41 and real H2S service triggers NACE MR0175 / ISO 15156 sulfide stress cracking thresholds. CO2 ↔ corrosion rate r ≈ +0.08 in sample (not de Waard physics). Full product v1.1 will implement de Waard 1991 CO2 corrosion model and MR0175 H2S service classifications.

  4. Pitting growth rate is NOT coupled to environment. Pit growth rates are uniformly sampled U(0.05, 1.5) mm/year without coupling to internal corrosion rate, environment class, or external_corrosion_risk. Real pit growth follows ASTM G46 + environment-specific kinetics. For pit-growth ML, filter to environment subsets and treat growth rate as residual variance rather than predictable from physics.

  5. Temperature varies only by noise factor. The temperature_f field in internal_corrosion is sampled per-row U(70, 180) without conditioning on pipeline service or seasonality. Real pipeline temperature tracks ambient + fluid-source temperature with strong seasonal cycles. For temperature-conditioned corrosion ML, use temperature as a noisy random feature, not a true operational signal.

  6. Pressure is independent of MAOP. The pressure_psi field is sampled U(500, 2200) without coupling to material grade or design MAOP. Real operating pressures are typically 60-80% of MAOP. For pressure-conditioned corrosion ML, filter to realistic MAOP-conditioned operating ranges.

  7. Coating health is not age-coupled. The coating_health_pct field is sampled U(60, 100) without conditioning on pipeline age. Real FBE coating health degrades 2-4% per decade per NACE SP0502 direct assessment. For coating-degradation ML, the sample is uniform-prior over coating quality rather than age-conditioned.

  8. Pipeline age is independent of all features. The pipeline_age_years field is sampled U(1, 40) without coupling to material grade (older pipelines were X42/X52, modern are X65/X70 per API 5L history). For age-conditioned ML, expect age to be uncoupled from material choice at sample scale.

Where physics IS strong (use these for ML)

Three coupling signals in this sample are physically valid and ML-useful:

Coupling Pearson r Physics Use For
Water cut → corrosion rate +0.33 de Waard-Milliams (1991) water-cut formula Within-pipeline corrosion ML
CP voltage → external risk +0.64 NACE SP0169 -0.85V criterion External corrosion risk classification
Soil resistivity → external risk -0.38 NACE TM0497 + SP0169 soil corrosivity External corrosion risk classification
Wall loss accumulation deterministic Time-integrated corrosion rate RUL forecasting

Cross-references to other XpertSystems OIL SKUs

This SKU is the fourth midstream SKU in the catalog — specializing in corrosion physics complementing the leak detection trilogy:

SKU Layer Focus
OIL-015 Midstream Pipeline flow assurance (wax / hydrate / asphaltene threshold gating)
OIL-024 Midstream Full pipeline hydraulics + SCADA + 15 transient events
OIL-025 Midstream Leak detection + rupture prediction + acoustic + CPM
OIL-027 Midstream Corrosion progression + cathodic protection + pitting + integrity (this SKU)

OIL-027 vs OIL-025: OIL-025 simulates event-centric leak/rupture detection with acoustic + pressure wave physics. OIL-027 simulates continuous corrosion progression (180-day time series) driving the underlying integrity degradation. Use OIL-025 for leak detection ML, OIL-027 for corrosion progression + cathodic protection ML.

OIL-027 vs OIL-022: OIL-022 simulates refinery vessel/piping corrosion (RBI + inspection findings). OIL-027 simulates pipeline external + internal corrosion with cathodic protection physics specific to buried/subsea pipelines. Use OIL-022 for vessel inspection planning, OIL-027 for pipeline corrosion ML.

Full product

The full OIL-027 dataset ships at 15,000 pipelines × 730-day daily corrosion progression (prod mode) producing tens of millions of rows with feature-coupled integrity grades per API 580 RBI methodology, physics-computed remaining life per API 510, de Waard 1991 CO2 corrosion model with partial-pressure conditioning, NACE MR0175 / ISO 15156 H2S service classifications, environment-conditioned pit growth kinetics per ASTM G46, age-coupled coating degradation per NACE SP0502, and MAOP-conditioned operating pressures — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil027_sample_2026,
  title  = {OIL-027: Synthetic Pipeline Corrosion Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil027-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-23 00:15:44 UTC
  • Pipelines : 1300
  • Simulation days : 180 (daily corrosion progression per pipeline)
  • Material grades : 3 (API 5L X52, X65, X70)
  • Environments : 4 (Onshore, Offshore, Subsea, Arctic)
  • Risk classes : 3 (LOW, MEDIUM, HIGH) per NACE SP0169
  • Integrity grades : 4 (LOW, MEDIUM, HIGH, CRITICAL) — sample-scale random
  • Calibration basis : NACE SP0169, NACE MR0175 / ISO 15156, NACE TM0497, de Waard & Milliams (1991), API 510, API 570, API 580/581, API 5L, ASME B31.4/B31.8, PHMSA 49 CFR 195, NACE SP0502, API 1163, ASTM G1, ASTM G46
  • Overall validation: 100.0/100 — Grade A+