| --- |
| 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+ |
| |
