README.md

---
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

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

Or with pandas:

```python
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

```bibtex
@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+