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
- 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

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

Or with pandas:

```python
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

```bibtex
@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+