| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| - time-series-forecasting |
| language: |
| - en |
| tags: |
| - synthetic |
| - oil-and-gas |
| - upstream |
| - well-control |
| - kick-detection |
| - blowout-prevention |
| - bop-operations |
| - safety-engineering |
| - iadc |
| - xpertsystems |
| pretty_name: "OIL-011 — Synthetic Kick & Blowout Scenario Dataset (Sample)" |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # OIL-011 — Synthetic Kick & Blowout Scenario Dataset (Sample) |
|
|
| **SKU:** `OIL011-SAMPLE` · **Vertical:** Oil & Gas / Upstream Well Control & Safety |
| **License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil011.v1` |
| **Sample version:** `1.0.0` · **Default seed:** `42` |
|
|
| A free, schema-identical preview of XpertSystems.ai's enterprise kick & |
| blowout scenario dataset for well-control ML, BOP analytics, kick-detection |
| modeling, and safety-training data generation. The sample covers |
| **1,500 wells** across **12 global basins** and **10 |
| formation classes**, with **115,251 rows** including |
| **108,000 timepoints** of drilling telemetry linked across **13 tables**. |
|
|
| --- |
|
|
| ## What's in the box |
|
|
| | File | Rows | Cols | Description | |
| |---|---:|---:|---| |
| | `wells_master.csv` | 1,500 | 16 | Well spine: basin, formation, rig type, geology, pore/frac gradients, HPHT/H2S/MPD flags | |
| | `drilling_timeseries.csv` | 108,000 | 19 | Per-timepoint pit / flow / SPP / gas units / BHP / ECD / kick margin / fracture margin | |
| | `kick_events.csv` | 214 | 12 | 7-class kick taxonomy (gas/oil/water/swab/loss-kick/shallow gas/HPHT gas) + severity + escalation | |
| | `blowout_scenarios.csv` | 93 | 9 | 5-class blowout type (surface/underground/riser gas/wellhead/ignited) + release volume | |
| | `bop_operations.csv` | 1,070 | 10 | 5-preventer activation log (annular, upper/lower pipe rams, blind/casing shear rams) | |
| | `choke_manifold_logs.csv` | 1,213 | 9 | Choke-pressure circulation control steps | |
| | `gas_influx_profiles.csv` | 214 | 8 | Gas type (methane/wet/CO2/H2S) + migration velocity + expansion ratio | |
| | `kill_operations.csv` | 214 | 8 | 6-class kill method (driller / wait-and-weight / bullhead / volumetric / lubricate / dynamic) | |
| | `alarms_and_warnings.csv` | 779 | 8 | 8-class alarm taxonomy + acknowledgment delay + operator response | |
| | `equipment_failures.csv` | 26 | 7 | 10-class failure component (BOP / pod / valve / sensor / pump / EDS) | |
| | `safety_response_logs.csv` | 214 | 8 | Operator action + procedural compliance + crew training level | |
| | `incident_root_cause.csv` | 214 | 6 | 10-class RCA category + contributing factor + corrective action | |
| | `scenario_labels.csv` | 1,500 | 7 | ML labels: risk level (low/medium/high/critical) + catastrophic flag + training class | |
|
|
| Total: **115,251 rows** across 13 CSVs, ~14.3 MB on disk. |
|
|
| --- |
|
|
| ## Calibration: industry-anchored, honestly reported |
|
|
| Validation uses a **10-metric scorecard** with targets sourced exclusively to |
| **named industry standards**: API 16D (BOP Control Systems), API RP-53 (BOP |
| Equipment for Drilling Wells), API RP-59 (Well-Control Operations), IADC |
| WellSharp competency framework, IADC Well Control taxonomy, IOGP Report 432 |
| (Loss of Well Control), NORSOK D-010 (well integrity), SINTEF OREDA |
| (Offshore Reliability Data), Bourgoyne et al. (1986) Applied Drilling |
| Engineering Ch.4, Schlumberger drilling-safety analytics. |
|
|
| **Sample run** (seed `42`, n_wells=1,500, timepoints=72): |
| |
| | # | Metric | Observed | Target | Tolerance | Status | Source | |
| |---|---|---:|---:|---:|---|---| |
| | 1 | kick event rate | 0.1427 | 0.13 | ±0.05 | ✓ PASS | IOGP Report 432 (Loss of Well Control) + IADC Well Control incident database — per-well kick incidence rate, global exploration & development drilling portfolio (typical 0.08-0.18 across HPHT/conventional/deepwater mix) | |
| | 2 | avg kick detection delay sec | 38.6589 | 40.0 | ±15.0 | ✓ PASS | IADC WellSharp + Schlumberger drilling-safety analytics — mean kick-to-detection delay for early-warning systems with pit/flow sensors (target <60s per modern MPD spec) | |
| | 3 | avg influx volume bbl | 13.4578 | 14.0 | ±6.0 | ✓ PASS | Bourgoyne et al. (1986) Applied Drilling Engineering Ch.4 + NORSOK D-010 — mean detected kick influx volume, industry-typical 5-50 bbl envelope | |
| | 4 | avg bop closure time sec | 18.2000 | 18.0 | ±8.0 | ✓ PASS | API 16D BOP Control Systems + API RP-53 — mean BOP closure time, annular <30s and ram <45s per spec | |
| | 5 | bop activation success rate | 0.9486 | 0.95 | ±0.04 | ✓ PASS | API RP-53 reliability statistics + SINTEF OREDA (Offshore Reliability Data) — BOP activation success rate across modern equipment portfolio | |
| | 6 | avg bop hydraulic pressure psi | 2908.6168 | 2900.0 | ±500.0 | ✓ PASS | API 16D BOP Control Systems — accumulator hydraulic pressure operating range (1500-5000 psi) | |
| | 7 | avg gas expansion ratio | 6.1411 | 6.5 | ±2.5 | ✓ PASS | Bourgoyne et al. (1986) Applied Drilling Engineering Ch.4 + ideal gas law — bottomhole-to-surface gas expansion ratio at typical kick depths (8-20 kft TVD) | |
| | 8 | hydraulics consistency score | 1.0000 | 0.98 | ±0.03 | ✓ PASS | NORSOK D-010 well integrity envelope — fraction of timepoints where hydraulics stay within fracture window (ECD doesn't catastrophically exceed fracture gradient) | |
| | 9 | basin diversity entropy | 0.9990 | 0.96 | ±0.04 | ✓ PASS | IOGP global well-control activity tracker — 12-class basin diversity benchmark (Permian, Gulf of Mexico, North Sea, Brazil Pre-Salt, Middle East, Eagle Ford, Bakken, West Africa, North Sea HPHT, Arctic, North African, Haynesville), normalized Shannon entropy | |
| | 10 | kick type diversity entropy | 0.9567 | 0.9 | ±0.08 | ✓ PASS | IADC Well Control taxonomy + Bourgoyne Ch.4 — 7-class kick-type diversity benchmark (gas kick, oil kick, water flow, swab kick, loss-kick, shallow gas, HPHT gas kick), normalized Shannon entropy (gas-kick dominant per industry default p=[0.33, 0.08, 0.10, 0.15, 0.12, 0.10, 0.12]) | |
| |
| **Overall: 100.0/100 — Grade A+** |
| (10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics) |
| |
| --- |
| |
| ## Schema highlights |
| |
| **`drilling_timeseries.csv`** — the well-control telemetry spine. |
| Each well has 72 timepoints with **physics-coupled post-kick perturbations**: |
| |
| > pit_volume += influx_bbl × min(1.0, progress × 1.55) |
| > flow_out += 8 + influx_bbl × min(1.0, progress × 2.2) |
| > spp -= 40 + influx_bbl × progress × 1.5 |
| > gas_units += 30 + influx_bbl × progress × 6 |
| > mud_weight -= min(1.6, influx_bbl/120) × progress |
|
|
| This produces **classic kick signatures detectable from the timeseries**: |
| flow_out > flow_in, rising pit volume, falling standpipe pressure, |
| rising gas units. Critical for training kick-detection ML on realistic |
| sensor patterns rather than just labeled-event metadata. |
|
|
| **`bop_operations.csv`** — every kick triggers all 5 preventers per the |
| **API RP-53 + IADC BOP stack convention**: |
| |
| | Preventer | Closure Time (s) | Function | |
| |---|---:|---| |
| | Annular | 18 ± 4 | Soft-seal against varied pipe sizes | |
| | Upper pipe ram | 18 ± 4 | Pipe-specific hard seal (upper) | |
| | Lower pipe ram | 18 ± 4 | Pipe-specific hard seal (lower) | |
| | Blind shear ram | 23 ± 4 | Last-resort cut & seal (+5s for shear time) | |
| | Casing shear ram | 18 ± 4 | Cut casing for emergency disconnect | |
| |
| Activation success ~98.5% baseline, dropping to ~95% under escalation ≥4 |
| and ~94% under blowout — matching SINTEF OREDA reliability statistics. |
| |
| **`kick_events.csv`** — 7-class kick taxonomy weighted per Bourgoyne et al.: |
| gas kick 33% / swab kick 15% / loss-kick 12% / HPHT gas 12% / oil 8% / |
| water 10% / shallow gas 10%. Influx volume drawn from lognormal(μ=2.35, |
| σ=0.65) → median ~10 bbl, p90 ~25 bbl, matching industry envelope. |
|
|
| **`gas_influx_profiles.csv`** — gas expansion ratio computed from TVD via |
| `1 + tvd/2600` — physics-consistent ideal-gas law approximation |
| (BHP_factor / atm_factor ≈ TVD-dependent scaling). At ~13 kft TVD, |
| expected expansion is ~6× — exactly what the sample produces. |
|
|
| **`scenario_labels.csv`** — 4-class risk taxonomy mapped to training |
| class: |
| |
| | Label | Population | Trigger | |
| |---|---:|---| |
| | `normal_drilling` | ~86% | No kick | |
| | `kick` (medium risk) | ~8% | Kick detected, escalation < 4 | |
| | `kick` (high risk) | ~1% | Kick detected, escalation ≥ 4, no blowout | |
| | `blowout` (critical) | ~6% | Catastrophic escalation | |
| |
| The **6% catastrophic rate is intentionally elevated** for safety-training |
| ML (real-world per-well rates are ~0.1-0.3%); see "Honest disclosure" |
| section for the rationale. |
|
|
| --- |
|
|
| ## Suggested use cases |
|
|
| 1. **Kick detection from timeseries** — train binary or sequence |
| classifiers on the 108,000-row drilling_timeseries to predict |
| `kick_detected_flag` from pit / flow / SPP / gas patterns. The |
| post-kick perturbations are physics-coupled, so models will learn |
| real well-control signatures. |
| 2. **BOP reliability ML** — predict `activation_status` and |
| `seal_integrity_score` per preventer from kick severity, blowout |
| risk, and equipment-failure features. |
| 3. **Blowout escalation prediction** — binary classifier on |
| `blowout_escalated_flag` from kick characteristics (influx volume, |
| detection delay, kick type, escalation level). |
| 4. **Kick type classification** — multi-class classifier (7 classes: |
| gas/oil/water/swab/loss-kick/shallow gas/HPHT gas) from |
| pre-detection telemetry features. |
| 5. **Alarm acknowledgment-delay regression** — predict |
| `acknowledgment_delay_sec` from alarm severity, crew training level, |
| and operational context. |
| 6. **Root cause analysis classification** — 10-class classifier on |
| `root_cause_category` from incident features for incident-review |
| automation. |
| 7. **Kill method selection** — predict optimal `kill_method` (6 classes) |
| from well geometry, kick characteristics, and BOP status. |
| 8. **Risk level scoring** — 4-class ordinal classifier on `risk_level` |
| (low/medium/high/critical) from upstream features for real-time |
| well-control dashboards. |
| 9. **Multi-table relational ML** — entity-resolution and graph-based |
| learning across the 13 joinable tables via `well_id` and `kick_id`. |
|
|
| --- |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("xpertsystems/oil011-sample", data_files="drilling_timeseries.csv") |
| print(ds["train"][0]) |
| ``` |
|
|
| Or with pandas: |
|
|
| ```python |
| import pandas as pd |
| wells = pd.read_csv("hf://datasets/xpertsystems/oil011-sample/wells_master.csv") |
| ts = pd.read_csv("hf://datasets/xpertsystems/oil011-sample/drilling_timeseries.csv") |
| kicks = pd.read_csv("hf://datasets/xpertsystems/oil011-sample/kick_events.csv") |
| bop = pd.read_csv("hf://datasets/xpertsystems/oil011-sample/bop_operations.csv") |
| labels = pd.read_csv("hf://datasets/xpertsystems/oil011-sample/scenario_labels.csv") |
| # Join timeseries to kick events |
| ts_with_kicks = ts.merge(kicks, on="kick_id", how="left") |
| ``` |
|
|
| --- |
|
|
| ## Reproducibility |
|
|
| All generation is deterministic via the integer `seed` parameter (driving |
| both `random.seed` and `np.random.default_rng`). 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 for ML prototyping and safety-training research. |
| A few important notes: |
|
|
| 1. **Elevated kick and blowout rates are intentional for safety-training |
| data.** The sample's per-well kick rate (~14%) and blowout-given-kick |
| rate (~43%, vs IOGP global ~3-7%) are deliberately amplified via the |
| `blowout_escalation_rate=0.38` parameter. This produces a **balanced |
| training set with sufficient positive examples** of all escalation |
| levels — useful for ML, but **not representative of true field-rate |
| well-control incident frequencies**. For epidemiological modeling |
| of well-control incidents, scale down events to match IOGP Report |
| 432 baseline rates. |
|
|
| 2. **Successful-kill rate is ~53%, lower than industry success rates |
| (~85-95%).** This is a consequence of (1) above: with ~43% of kicks |
| escalating to blowout in the generator, `kill_success` is |
| constructed as `not blowout AND U(0,1) < 0.93`. For ML training on |
| kill-method selection this is fine; for kill-success forecasting, |
| re-weight the training set. |
|
|
| 3. **BOP activation timing is fast end of the API envelope.** Mean |
| closure time is ~18s, matching modern subsea BOPs but at the |
| aggressive end of the API 16D spec (annular <30s, ram <45s). Older |
| land-rig BOP stacks may run 25-40s. Adjust calibration if your |
| target audience is legacy onshore equipment. |
|
|
| 4. **Each kick triggers all 5 preventers** — generates 5 BOP rows per |
| kick deterministically. Real BOP procedures activate based on |
| escalation state (annular first, then rams, shear rams last). The |
| sample provides activation timing for every preventer regardless of |
| whether it would be procedurally invoked, useful for individual- |
| preventer reliability ML but not for activation-sequence modeling. |
|
|
| 5. **Time-series anomaly flag is uniform 3.1% Bernoulli** across all |
| timepoints (per `anomaly_injection_rate`) — does not concentrate |
| around kick events. For anomaly-detection ML, the `kick_id` non-NONE |
| timepoints are the true positive signal channel; the |
| `anomaly_flag` is sensor noise / data-quality drill. |
|
|
| 6. **`kick_severity` distribution is bimodal** (peaks at 1-2 and 4-5) |
| because blowout-escalated kicks override severity to {4, 5}. This |
| reflects the generator's coupling between blowout-flag and severity, |
| not a modeling error. For severity regression, condition on |
| `blowout_escalated_flag` to avoid label leakage. |
| |
| 7. **Equipment failures are sparse** (~12% of kicks have a failure |
| recorded), reflecting the conditional injection rate |
| `equipment_failure_rate=0.019 + 0.25*blowout + 0.05*high_escalation`. |
| For failure-mode ML, expect class-imbalanced learning conditions. |
| |
| --- |
| |
| ## Full product |
| |
| The **full OIL-011 dataset** ships at **50,000 wells × 15 timepoints** |
| (prod mode) with field-rate-calibrated incident frequencies (IOGP-matched |
| 0.3-0.5% blowout rate), legacy-equipment BOP timing distributions, and |
| procedurally-correct BOP activation sequencing — licensed commercially. |
| Contact XpertSystems.ai for licensing terms. |
|
|
| 📧 **pradeep@xpertsystems.ai** |
| 🌐 **https://xpertsystems.ai** |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{xpertsystems_oil011_sample_2026, |
| title = {OIL-011: Synthetic Kick & Blowout Scenario Dataset (Sample)}, |
| author = {XpertSystems.ai}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/xpertsystems/oil011-sample} |
| } |
| ``` |
|
|
| ## Generation details |
|
|
| - Sample version : 1.0.0 |
| - Random seed : 42 |
| - Generated : 2026-05-21 23:49:57 UTC |
| - Wells : 1,500 |
| - Timepoints/well : 72 |
| - Basins : 12 (Permian, Gulf of Mexico, North Sea, |
| Brazil Pre-Salt, Middle East, Eagle Ford, Bakken, |
| West Africa, North Sea HPHT, Arctic, North African, |
| Haynesville) |
| - Formations : 10 (overpressured shale, fractured |
| carbonate, deepwater turbidite, HPHT gas sand, |
| sour gas carbonate, etc.) |
| - Kick types : 7 (gas, oil, water, swab, loss-kick, shallow gas, HPHT gas) |
| - Blowout types : 5 (surface, underground, riser gas, wellhead, ignited) |
| - BOP preventers : 5 (annular, upper pipe ram, lower pipe ram, blind |
| shear ram, casing shear ram) |
| - Kill methods : 6 (driller, wait-and-weight, bullheading, volumetric, |
| lubricate-and-bleed, dynamic kill) |
| - Calibration basis : API 16D, API RP-53, API RP-59, IADC WellSharp, |
| IADC Well Control taxonomy, IOGP Report 432, |
| NORSOK D-010, SINTEF OREDA, Bourgoyne et al. (1986) |
| - Overall validation: 100.0/100 — Grade A+ |
| |
