README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- upstream
- mud-logging
- formation-evaluation
- gas-chromatography
- pixler-ratios
- pore-pressure
- kick-detection
- lithology-id
- xpertsystems
pretty_name: "OIL-009 — Synthetic Mud Logging Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-009 — Synthetic Mud Logging Dataset (Sample)

**SKU:** `OIL009-SAMPLE` · **Vertical:** Oil & Gas / Upstream Formation Evaluation
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil009.v1`
**Generator version:** `1.0-file1-simulation-engine` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise mud-logging
dataset for real-time formation evaluation, gas chromatography ML,
pore-pressure detection, and kick-risk monitoring. The sample covers
**150 wells** across **10 global basins** with
**217,275 depth-resolved mud-log records** linked across
**12 tables**.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `wells_master.csv` | 150 | 12 | Well spine: basin, formation, rig, HPHT/sour/offshore flags, planned MW |
| `formation_tops.csv` | 735 | 5 | 3-7 formation tops per well with picker confidence score |
| `mud_log_timeseries.csv` | 21,639 | 9 | Depth-resolved drilling mechanics: ROP, WOB, RPM, torque, SPP, flow |
| `gas_readings.csv` | 21,639 | 10 | Total gas units + C1-C5 chromatograph composition + H2S |
| `lithology_intervals.csv` | 21,639 | 8 | 9-class lithology + carbonate/shale/sand fraction % |
| `cuttings_analysis.csv` | 21,639 | 8 | Grain size, sorting, fluorescence color & intensity, oil stain flag |
| `drilling_events.csv` | 21,639 | 6 | 10-class event log (drilling break, kick precursor, lost circulation, etc.) |
| `pore_pressure_indicators.csv` | 21,639 | 7 | d-exponent, shale density, overpressure flag, pore pressure ppg equiv |
| `mud_properties.csv` | 21,639 | 7 | Mud weight, viscosity, chlorides, gas-cut mud flag |
| `gas_chromatography.csv` | 21,639 | 7 | Pixler ratios: wetness, balance, character + gas quality flag |
| `cavings_analysis.csv` | 21,639 | 6 | Cavings type (5-class) + wellbore instability score |
| `drilling_labels.csv` | 21,639 | 7 | ML labels: hydrocarbon show, kick risk, reservoir quality, lithology |

Total: **217,275 rows** across 12 CSVs, ~16.8 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: Pixler (1969) AAPG seminal hydrocarbon-ratio
classification paper, Jorden & Shirley (1966) JPT d-exponent overpressure
detection, IADC Mud Logging Standards, IADC Well Control Statistics, API
RP-13B-1 drilling fluids, SPE 142884 (pore pressure detection methods),
Schlumberger Mud Logging Field Manual, Halliburton Mud Logging guide,
IHS Markit / Rystad Energy global wildcat database.

**Sample run** (seed `42`, n_wells=150, depth_step=100 ft):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg total gas units | 225.6205 | 200.0 | ±80.0 | ✓ PASS | IADC Mud Logging Standards + Schlumberger Mud Logging Field Manual — global mean background total gas units, mixed unconventional/conventional basin portfolio |
| 2 | avg methane pct | 71.3605 | 72.0 | ±8.0 | ✓ PASS | Pixler (1969) AAPG — C1 fraction in mixed oil/gas/condensate global wildcat portfolio, dry-to-wet-gas transition zone |
| 3 | avg wetness ratio | 11.8237 | 12.0 | ±4.0 | ✓ PASS | Pixler (1969) AAPG — wetness ratio (ΣC2-C5/ΣC1-C5×100), wet-gas / gas-condensate Pixler classification zone |
| 4 | avg balance ratio | 9.9006 | 10.0 | ±5.0 | ✓ PASS | Pixler (1969) AAPG + Halliburton Mud Logging guide — balance ratio C1/(C2+C3) light-oil-to-wet-gas envelope |
| 5 | avg mud weight ppg | 12.0585 | 11.5 | ±2.0 | ✓ PASS | API RP-13B-1 + SPE drilling fluids literature — global mean mud weight, mixed conventional/HPHT/deepwater portfolio |
| 6 | avg d exponent | 1.3000 | 1.3 | ±0.3 | ✓ PASS | Wyllie + Jorden & Shirley (1966) JPT — corrected d-exponent normal-compaction shale baseline value (typically 1.0-1.5; decreasing trend indicates overpressure) |
| 7 | hydrocarbon show rate | 0.0987 | 0.1 | ±0.05 | ✓ PASS | IHS Markit + Schlumberger wildcat database — fraction of drilled depth intervals exhibiting hydrocarbon shows (gas + fluorescence + reservoir lithology), global mixed exploration portfolio |
| 8 | kick risk rate | 0.0102 | 0.012 | ±0.01 | ✓ PASS | IADC Well Control Statistics + SPE 142884 — fraction of drilled depth intervals showing kick precursor signatures (overpressure + elevated gas + mud-weight underbalance), global mud-logging dataset |
| 9 | lithology diversity entropy | 0.7421 | 0.65 | ±0.1 | ✓ PASS | Global mud-logging literature — 9-class lithology diversity benchmark (shale, sandstone, siltstone, limestone, dolomite, marl, anhydrite, volcanic, coal); normalized Shannon entropy. Shale-dominant global mix produces a deliberately sub-uniform distribution |
| 10 | basin diversity entropy | 0.9881 | 0.95 | ±0.05 | ✓ PASS | Rystad Energy + IHS Markit global mud-logging coverage — 10-class basin diversity benchmark (Permian, Eagle Ford, Bakken, Marcellus, GoM Deepwater, North Sea, Middle East, Brazil Pre-Salt, Canadian Oil Sands, Tight Gas Sandstone), normalized Shannon entropy |

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

---

## Schema highlights

**`gas_chromatography.csv`** — the **Pixler (1969) hydrocarbon ratio**
canonical formulation for mud-log gas typing:

> wetness_ratio   = (C2 + C3 + C4 + C5) / (C1 + C2 + C3 + C4 + C5) × 100
> balance_ratio   = C1 / (C2 + C3)
> character_ratio = (C4 + C5) / C3

These are the three ratios used by every commercial mud-logging service
(Halliburton, SLB, Geoservices, Pason) to classify shows as dry gas / wet
gas / condensate / oil. Sample wetness mean ~12 is in the **wet-gas / oil-
rich-gas** Pixler zone (5-17.5); sample balance ~10 is in the **light oil**
zone (1.5-100).

**`gas_readings.csv`** — basin-specific gas means with in-reservoir
amplification (1.65×) and overpressure amplification (1.28×), plus
log-normal noise. Background gas levels match the IADC mud-logging
convention (50-500 units typical, >500 anomalous).

**`pore_pressure_indicators.csv`** — implements the **Jorden & Shirley
(1966) d-exponent** overpressure detection method:

> d = log(ROP/60·N) / log(12·WOB/10⁶·D)     (corrected for mud weight)

Normal-compaction shale baseline is ~1.0-1.5; values decreasing with depth
indicate undercompacted shales and impending overpressure. Sample mean
d-exponent ~1.30 with downward deviations correlating with the
`overpressure_flag` column.

**`lithology_intervals.csv`** — 9-class lithology (shale, sandstone,
siltstone, limestone, dolomite, marl, anhydrite, volcanic, coal) drawn
from basin-conditioned probability mixes. Shale dominates at 36% reflecting
the modern unconventional-heavy global drilling portfolio.

**`drilling_events.csv`** — 10-class event taxonomy (normal drilling,
drilling break, connection gas, trip gas, lost circulation, kick precursor,
tight hole, differential sticking, sensor dropout, lag correction). Kick
precursors gated by (overpressure + gas > 220 + mud-weight underbalance);
drilling breaks gated by (hydrocarbon show + 38% draw rate).

---

## Suggested use cases

1. **Pixler hydrocarbon-ratio classification ML** — train classifiers on
   wetness / balance / character ratios → dry-gas / wet-gas / condensate /
   oil / no-show labels. Pixler crossplot zones are well-separated targets.
2. **Lithology identification from gas + chromatograph** — multi-class
   classifier on `lithology_label` (9-class) from drilling mechanics + gas
   composition + cuttings fluorescence features.
3. **Kick-risk early warning** — binary classifier on `kick_risk_flag`
   from upstream features (d-exponent decline, gas elevation, mud-weight
   underbalance). Sample has 1% positive rate matching IADC field statistics.
4. **Pore-pressure regression** — regress `pore_pressure_ppg_equiv` from
   d-exponent, shale density, depth, and drilling-mechanics features.
5. **Hydrocarbon show detection** — binary classifier on
   `hydrocarbon_show_flag` from gas + fluorescence + lithology features.
6. **Reservoir quality grading** — multi-class classifier on
   `reservoir_quality` (low/medium/high) from petrophysical and
   show-related features.
7. **Drilling event classification** — 10-class classifier on
   `drilling_event_type` from time-series drilling-mechanics features.
8. **Multi-table relational ML** — entity-resolution and graph-based
   learning across the 12 joinable tables via `well_id` and depth.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil009-sample", data_files="gas_readings.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
gas   = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/gas_readings.csv")
chr_  = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/gas_chromatography.csv")
lith  = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/lithology_intervals.csv")
lbl   = pd.read_csv("hf://datasets/xpertsystems/oil009-sample/drilling_labels.csv")
joined = gas.merge(chr_, on=["well_id","depth_ft"]).merge(lbl, on=["well_id","depth_ft"])
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
`random.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 ML prototyping and mud-logging
research, not for live drilling decisions. A few notes:

1. **Long-tail lithology classes are under-represented at sample scale.**
   Anhydrite (~0.9%), volcanic (~1.4%), and coal (~0.3%) are rare classes
   that appear only when their parent basins are drawn. Full product
   (18,000 wells) gives sufficient samples for these rare classes; the
   sample provides only handful-of-rows demonstrations of the schema.

2. **All detail tables are co-resolved at the depth_step granularity**
   (100 ft in the sample). Real mud-logging data has higher-frequency gas
   readings (1-5 ft intervals) and lower-frequency cuttings descriptions
   (5-30 ft intervals). The schema is the same; only the resolution differs.
   For high-frequency ML, use the full product with `--depth-step-ft 5`.

3. **Anomaly injection rate is 3%** (`anomaly_injection_rate=0.03`) — gas
   units randomly multiplied by [0.25, 0.45, 1.9, 2.8] to simulate sensor
   dropouts and lag corrections. These appear as outliers in `gas_readings`
   and can be filtered out via `gas_chromatography.gas_quality_flag == 1`.

4. **Hydrocarbon show rate (10%) and kick risk rate (1%) match aggregate
   IADC field statistics** but are not stratified by basin. Per-basin
   show rates in real data range from 2-3% (Marcellus dry gas) to 25-30%
   (Pre-Salt carbonate plays). Future generator v1.1 will introduce
   basin-conditioned show priors.

5. **Mud-log timeseries uses `mud_log_timeseries.csv`** as the canonical
   time-axis spine — all other tables (gas, lithology, cuttings, etc.) are
   indexed at the same depth grid for clean ML joins. This makes the
   tables more relational and less "time-series-y" than real MWD/LWD
   streams; treat the sample as **depth-domain mud-log records**, not
   time-domain telemetry.

---

## Full product

The **full OIL-009 dataset** ships at **18,000 wells** with **~9M depth-
resolved mud-log records**, 5-ft default depth resolution, basin-conditioned
hydrocarbon show priors, and per-basin chromatograph stratification —
licensed commercially. Contact XpertSystems.ai for licensing terms.

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

---

## Citation

```bibtex
@dataset{xpertsystems_oil009_sample_2026,
  title  = {OIL-009: Synthetic Mud Logging Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil009-sample}
}
```

## Generation details

- Generator version : 1.0-file1-simulation-engine
- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-21 23:20:37 UTC
- Wells             : 150
- Depth step        : 100 ft
- Anomaly rate      : 3.0%
- Basins            : 10 (Permian, Eagle Ford, Bakken, Marcellus,
                      GoM Deepwater, North Sea, Middle East Carbonate,
                      Brazil Pre-Salt, Canadian Oil Sands, Tight Gas Sandstone)
- Lithologies       : 9 (shale, sandstone, siltstone, limestone, dolomite,
                      marl, anhydrite, volcanic, coal)
- Calibration basis : Pixler (1969), Jorden & Shirley (1966), IADC Mud
                      Logging Standards, IADC Well Control Statistics,
                      API RP-13B-1, SPE 142884, Schlumberger Mud Logging
                      Field Manual, Halliburton Mud Logging guide
- Overall validation: 100.0/100 — Grade A+