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oil008-sample

Tasks:
Tabular Classification
Tabular Regression
Languages:
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Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
directional-drilling
wellbore-trajectory
geosteering
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Initial release: OIL-008 sample, 200 wells / 306K rows, Grade A+ (10/10)
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - oil-and-gas
  - upstream
  - directional-drilling
  - wellbore-trajectory
  - geosteering
  - survey-qc
  - anti-collision
  - minimum-curvature
  - iscwsa
  - xpertsystems
pretty_name: OIL-008 — Synthetic Wellbore Trajectory Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-008 — Synthetic Wellbore Trajectory Dataset (Sample)

SKU: OIL008-SAMPLE · Vertical: Oil & Gas / Upstream Directional Drilling License: CC-BY-NC-4.0 (sample) · Schema version: oil008.v1 Generator version: 1.1-fixed · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise wellbore- trajectory dataset for directional drilling, geosteering, survey QC, and anti-collision ML. The sample covers 200 wells across 10 global basins with 306,250 surveyed stations linked across 11 tables.

What's in the box

File Rows Cols Description
wells_master.csv 200 6 Well spine: basin, type, kickoff/TVD/lateral length
planned_trajectory.csv 30,605 8 Planned MD/TVD/inclination/azimuth/N-E
actual_trajectory.csv 30,605 7 Surveyed MD/TVD/inclination/azimuth + per-station DLS
geosteering_targets.csv 30,605 6 5-class target zones (Wolfcamp A/B, Eagle Ford, Bakken Middle, Carbonate Pay)
collision_monitoring.csv 30,605 5 Anti-collision: separation factor + center distance per offset well
survey_uncertainty.csv 30,605 5 ISCWSA-style uncertainty ellipse (major/minor axes + covariance)
drilling_sections.csv 30,605 5 Section classification (Vertical / Build / Lateral) + build/turn rates
bha_directional_data.csv 30,605 6 RSS flag, bend angle, toolface, slide/rotate ratio
torque_drag_effects.csv 30,605 6 Surface torque, drag, friction factor, buckling risk
survey_qc_flags.csv 30,605 5 Magnetic interference / gyro discrepancy flags + QC score
well_spacing_labels.csv 30,605 5 ML labels: spacing grade, collision risk flag, target hit flag

Total: 306,250 rows across 11 CSVs, ~16.3 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: SPE 67616, SPE 90408 (Williamson 2000), SPE 178215, ISCWSA MWD error model, API SPEC 7 directional survey QC, IADC Directional Drilling Manual, IADC anti-collision guidelines, OWSG (Operator Wellbore Survey Group), Rystad Energy global rig fleet, Spears & Associates unconventional analytics, and Halliburton/SLB directional drilling handbooks.

Sample run (seed 42, n_wells=200):

# Metric Observed Target Tolerance Status Source
1 avg lateral length ft 9151.7850 9200.0 ±1800.0 ✓ PASS Spears & Associates + Rystad Energy unconventional rig tracker — global mean lateral length, 2020-2024 horizontal well portfolio (US/Canada/Argentina)
2 avg dogleg severity deg per 100ft 3.1809 3.2 ±1.0 ✓ PASS SPE 67616 + IADC Directional Drilling Manual — global mean DLS across mixed-trajectory directional well portfolio
3 avg lateral inclination deg 88.4955 88.5 ±2.0 ✓ PASS SPE geosteering best practices + Halliburton/SLB directional drilling handbooks — lateral hold inclination for landing in horizontal target zones
4 lateral section fraction 0.6045 0.6 ±0.1 ✓ PASS Rystad Energy + EnverusDX unconventional well analytics — lateral-MD / total-MD ratio for modern long-lateral horizontal portfolio, 2020-2024
5 survey repeatability 0.9620 0.96 ±0.02 ✓ PASS ISCWSA error model + API SPEC 7 directional survey QC — MWD/gyro survey repeatability score across modern surveyed directional wells
6 anti collision separation factor mean 4.6982 4.7 ±1.0 ✓ PASS IADC anti-collision separation factor guidelines + OWSG (Operator Wellbore Survey Group) collision avoidance rules — typical mean separation factor for surveyed well pairs in mature basins (target >3.0, alarm <1.5)
7 avg uncertainty ellipse ft 11.4819 11.5 ±4.0 ✓ PASS ISCWSA MWD error model + SPE 90408 (Williamson 2000) — characteristic survey uncertainty ellipse major axis for MWD-surveyed horizontal wells at TD
8 planned vs actual inc mae deg 0.3182 0.4 ±0.3 ✓ PASS SPE 178215 (geosteering delivery accuracy) + Halliburton Sperry directional engineering benchmarks — mean absolute inclination delivery error vs plan
9 trajectory curvature realism 0.9287 0.93 ±0.05 ✓ PASS SPE 67616 + IADC — composite curvature realism index (1 − σ(DLS)/10), benchmarking dogleg-severity dispersion vs field-data envelopes
10 basin diversity entropy 0.9885 0.92 ±0.08 ✓ PASS Rystad Energy + IHS Markit global rig fleet — 10-class basin diversity benchmark (Permian, Eagle Ford, Bakken, Marcellus, North Sea, Gulf of Mexico, Middle East, Canadian Oil Sands, Brazil Pre-Salt, North Africa), normalized Shannon entropy

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

Schema highlights

actual_trajectory.csv — the surveyed trajectory spine, one row per station per well. Computed via the minimum-curvature method (Bourgoyne et al., 1986; API/SPE industry standard):

Δnorth = ΔMD/2 · (sin(I₁)·cos(A₁) + sin(I₂)·cos(A₂)) · RF Δeast = ΔMD/2 · (sin(I₁)·sin(A₁) + sin(I₂)·sin(A₂)) · RF Δtvd = ΔMD/2 · (cos(I₁) + cos(I₂)) · RF

where RF is the dogleg ratio factor RF = (2/β)·tan(β/2) and β is the dogleg angle between consecutive station vectors. This is the same math used by Compass, Landmark, SLB DDS, and every commercial survey-calculation package.

drilling_sections.csv classifies each station as Vertical (MD < kickoff), Build (kickoff ≤ MD < build-end), or Lateral (MD ≥ build-end). DLS distributions are section-aware:

Section DLS μ DLS σ
Vertical 2.7 0.55
Build 3.9 0.65
Lateral 3.05 0.55

collision_monitoring.csv uses the IADC separation factor convention (target SF > 3.0, alarm SF < 1.5) with a mean ~4.7 — typical for mature basins with established offset-well drilling history.

survey_uncertainty.csv ellipse axes follow ISCWSA error model conventions for MWD-surveyed wells (Williamson 2000, SPE 90408): major axis 5–18 ft, minor axis 2–9 ft, covariance index 0.88–0.98.

bha_directional_data.csv distinguishes rotary-steerable systems (RSS, ~58%) from positive-displacement-motor (PDM) BHAs via the rss_flag column, matching the modern industry mix where RSS dominates long-lateral and ERD wells.

Suggested use cases

  1. Trajectory anomaly detection — flag stations where DLS exceeds section-specific envelopes using ML on the 30,605-row station- resolution spine.
  2. Geosteering target-hit prediction — binary classifier on target_hit_flag (whether the lateral landed in the target zone) from BHA + trajectory + geosteering features.
  3. Anti-collision risk scoring — regress collision_risk_flag and separation_factor from trajectory and offset-well features.
  4. Survey QC ML — predict qc_score, magnetic_interference_flag, and gyro_discrepancy_flag from station-resolution trajectory data to triage surveys for human review.
  5. Planned-vs-actual delivery analytics — quantify drilling delivery accuracy by regressing the inclination/azimuth/TVD delta between planned and actual at each station.
  6. Section classification — multi-class classifier on section_type (Vertical/Build/Lateral) from trajectory shape features for automated well section segmentation.
  7. Torque-drag prediction — regress torque and drag from trajectory complexity (DLS, inclination profile) and BHA features.
  8. Multi-table relational ML — entity-resolution and graph-based learning across the 11 joinable tables via well_id and survey_id.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil008-sample", data_files="actual_trajectory.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
wells    = pd.read_csv("hf://datasets/xpertsystems/oil008-sample/wells_master.csv")
actual   = pd.read_csv("hf://datasets/xpertsystems/oil008-sample/actual_trajectory.csv")
planned  = pd.read_csv("hf://datasets/xpertsystems/oil008-sample/planned_trajectory.csv")
sections = pd.read_csv("hf://datasets/xpertsystems/oil008-sample/drilling_sections.csv")
joined = actual.merge(planned, on=["well_id","md_ft"], suffixes=("_act","_plan"))

Reproducibility

All generation is deterministic via the integer seed parameter (seeds both random.seed() and 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 trajectory research, not for live well planning. A few notes:

  1. Global-mean inclination is structurally lower than the generator's 72° target. The generator's section composition (~19% Vertical + ~21% Build + ~60% Lateral) mathematically averages to ~64° — Vertical at 4°, Build at 47°, Lateral at 88.5° — even though each individual section is correctly modeled. The scorecard validates the lateral- section inclination (88.5°, on target) and lateral section fraction (60%, on target) directly, which are the operationally meaningful quantities. Future generator v1.2 will rebalance section weights to bring the global mean closer to 72° per the file header intent.

  2. Each station has an aligned row across all 11 tables — the per-station tables (planned/actual/geosteering/collision/uncertainty/ sections/BHA/torque/QC/labels) are joinable by both well_id and station index. This is convenient for ML but slightly over-coupled relative to real-world data where uncertainty, BHA, and QC are typically sparser than the trajectory itself.

  3. Offset-well IDs in collision_monitoring.csv are synthetic — the offset_well_id field samples from a 10,000-well synthetic pool independently per station, so the same offset well will not appear in multiple collision rows. For graph-based anti-collision ML, treat each row as an independent (well, offset_well) pair rather than as evidence of shared offset structure.

  4. Section spacing is uniform at 100 ft in the sample. Real surveys are sparser in vertical sections (200-500 ft) and denser through build (50-100 ft). Future generator v1.2 will introduce non-uniform station spacing.

  5. Anomaly rate is 1.5% (anomaly_rate=0.015) injected as randomly-elevated DLS values. This is a controlled noise channel for QC model training; filter qc_score < 0.95 to remove the noisy stations.

Full product

The full OIL-008 dataset ships at 1,000 wells with full ISCWSA error model error-band stratification per survey tool type (MWD/gyro/ inertial), per-basin offset-well graph structure with realistic neighborhood density, and non-uniform station spacing matching field survey practice — licensed commercially. Contact XpertSystems.ai for licensing terms.

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

Citation 

@dataset{xpertsystems_oil008_sample_2026,
  title  = {OIL-008: Synthetic Wellbore Trajectory Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil008-sample}
}

Generation details

  • Generator version : 1.1-fixed
  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-21 23:11:22 UTC
  • Wells : 200
  • Station spacing : 100 ft
  • Anomaly rate : 1.5%
  • Basins : 10 (Permian, Eagle Ford, Bakken, Marcellus, North Sea, Gulf of Mexico, Middle East Carbonates, Canadian Oil Sands, Brazil Pre-Salt, North Africa)
  • Well types : 4 (Horizontal, Extended Reach, J-Well, S-Well)
  • Survey method : Minimum curvature (Bourgoyne et al. 1986)
  • Calibration basis : SPE 67616, SPE 90408 (Williamson 2000), SPE 178215, ISCWSA error model, API SPEC 7, IADC Directional Drilling Manual, OWSG, Rystad Energy, Spears & Associates, Halliburton/SLB directional handbooks
  • Overall validation: 100.0/100 — Grade A+