--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - oil-and-gas - upstream - drilling - drill-bits - bit-performance - bit-wear - dysfunction-detection - mse-efficiency - hydraulic-performance - xpertsystems pretty_name: "OIL-010 — Synthetic Drill Bit Performance Dataset (Sample)" size_categories: - 100K 1.0) - `failure_risk_score` (continuous, 0-1) --- ## Suggested use cases 1. **Bit-type selection ML** — multi-class classifier on `bit_type` from formation + depth + manufacturer-class features, trained against ROP-grade target. 2. **ROP-grade classification** — train classifiers on `rop_efficiency_grade` (A/B/C/D) using drilling parameters, hydraulics, vibration, and bit spec features. 3. **Bit wear regression** — predict `cutter_wear_pct` and `bearing_wear_pct` from run depth, formation, drilling parameters, and thermal exposure. 4. **Dysfunction detection** — binary classifier on whether a run experiences a dysfunction event (join `dysfunction_events` to runs; ~35% positive rate). Then a 5-class secondary classifier on which dysfunction type given an event. 5. **MSE optimization** — regress `mse_efficiency` from WOB, RPM, torque, ROP using the 142,002-row drilling-parameters spine (5-15 samples per run for distribution-aware training). 6. **Hydraulic efficiency prediction** — predict `hydraulic_efficiency` from flow rate, pressure drop, HSI, and bit size. 7. **Thermal overload risk** — binary classifier predicting thermal-overload dysfunction from BH temperature, cutter temperature, and thermal cycles. 8. **Multi-table relational ML** — entity-resolution and graph-based learning across the 9 joinable tables via `bit_id` and `run_id`. --- ## Loading ```python from datasets import load_dataset ds = load_dataset("xpertsystems/oil010-sample", data_files="drilling_runs.csv") print(ds["train"][0]) ``` Or with pandas: ```python import pandas as pd bits = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/bits_master.csv") runs = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/drilling_runs.csv") params = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/drilling_parameters.csv") wear = pd.read_csv("hf://datasets/xpertsystems/oil010-sample/bit_wear_logs.csv") joined = runs.merge(bits, on="bit_id").merge(wear, on="run_id") ``` --- ## Reproducibility All generation is deterministic via the integer `seed` parameter (driving `np.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 bit- performance research, not for live bit-selection decisions. The generator is a **direct-sampling design** (independent Gaussian draws around industry-anchored target means) — fast to validate and easy to extend, but with several limitations users should know about: 1. **No cross-feature physics coupling.** Each table is sampled independently of the others — WOB and torque are not correlated, vibration and dysfunction events are uncoupled, and thermal exposure does not drive wear progression. **ML models trained on this sample will learn marginal distributions but will not learn realistic cross-feature relationships.** The full OIL-010 product (v1.1 roadmap) will introduce physics coupling via shared latent factors (UCS-driven ROP, WOB-driven torque, thermal-driven cutter wear, vibration-driven dysfunction). 2. **Formation and dysfunction are uniformly sampled, not conditioned.** In real drilling data, geothermal hard rock has very different dysfunction profiles (thermal overload dominant) than Permian shale (stick-slip dominant). The sample uses uniform 5-class draws for both; treat the joint distribution as ML-balanced rather than field-realistic. 3. **Three CSVs listed in the generator docstring are not generated** in this version: `lithology_transitions.csv`, `directional_performance.csv`, and `economics_metrics.csv`. These will ship in OIL-010 v1.1. Current product is 9 CSVs. 4. **Long-tail cutter-count outliers.** Cutter counts are drawn from N(45, 12), so a small fraction of bits (~0.1%) have unrealistically low cutter counts (<5). PDC bits in practice have 30-80 cutters; filter `cutter_count >= 20` if you need clean PDC training data. 5. **MSE efficiency is sampled, not computed.** The `mse_efficiency` column is a direct Gaussian draw N(0.88, 0.04), not derived from the Pessier MSE formulation (MSE = WOB/A + 120·π·N·T / (A·ROP)). For physically-consistent MSE labels, use OIL-007 (Drilling Parameters), which implements the full Teale/Pessier MSE physics with bit-size-aware area. 6. **All non-bit-master tables use uniform/Gaussian random IDs.** The `well_id` field in `drilling_runs.csv` samples from a 50,000-well synthetic pool independently per run, so the same well will not typically appear in multiple runs in the sample. For ML that requires well-level grouping, the full product introduces realistic well clustering. --- ## Full product The **full OIL-010 dataset** ships at **5,000 bits / 25,000 runs**, with the **v1.1 roadmap** adding cross-feature physics coupling, the three missing tables (lithology_transitions, directional_performance, economics_metrics), and basin-conditioned dysfunction priors — licensed commercially. Contact XpertSystems.ai for licensing terms. 📧 **pradeep@xpertsystems.ai** 🌐 **https://xpertsystems.ai** --- ## Citation ```bibtex @dataset{xpertsystems_oil010_sample_2026, title = {OIL-010: Synthetic Drill Bit Performance Dataset (Sample)}, author = {XpertSystems.ai}, year = {2026}, url = {https://huggingface.co/datasets/xpertsystems/oil010-sample} } ``` ## Generation details - Sample version : 1.0.0 - Random seed : 42 - Generated : 2026-05-21 23:33:12 UTC - Bits : 3,000 - Runs : 15,000 (5 runs per bit on average) - Bit types : 4 (PDC, Roller Cone, Hybrid, Diamond Impregnated) - Formations : 5 (Permian shale, carbonate, abrasive sandstone, deepwater turbidite, geothermal hard rock) - Dysfunction types : 5 (stick-slip, bit whirl, bounce, balling, thermal overload) - Calibration basis : SPE 21943 (Pessier MSE), SPE 96652 (Dupriest), SPE 178850, SPE 178215, API RP-7G, API RP-13D, ISO 13503-5, IADC dull grading, IADC Drilling Manual, Spears & Associates, Rystad Energy - Overall validation: 100.0/100 — Grade A+