xpertsystems/oil011-sample

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.

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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.

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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)

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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.

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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.

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Loading

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil011-sample", data_files="drilling_timeseries.csv")
print(ds["train"][0])

Or with pandas:

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")

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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.

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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.

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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

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Citation

@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+