README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - digital-twin
  - oilfield
  - upstream
  - reservoir
  - well-production
  - artificial-lift
  - scada
  - ot-cybersecurity
  - methane
  - flaring
  - pipeline
  - ics
  - iec-62443
  - isa-99
  - isa-18-2
  - phmsa
  - api-rp-754
  - iso-14224
  - spe
  - aapg
  - ghg-emissions
  - integrated-operations
pretty_name: "OIL-042 — Synthetic Digital Twin Dataset (Oilfield) (Sample)"
size_categories:
  - 100K<n<1M
---

# OIL-042 — Synthetic Digital Twin Dataset (Oilfield) (Sample)

A schema-identical preview of **OIL-042**, the XpertSystems.ai synthetic
**integrated-oilfield digital twin** dataset. The full product covers up to
50,000 wells × 250 reservoirs × 1,800 pipelines across a 3-year horizon at
hourly cadence (~600M rows). This sample is the generator's `sample` mode
(120 wells × 6 reservoirs × 8 facilities × 14 pipelines, 90 days at 6-hour
cadence) covering all 18 product tables.

> **Built by** XpertSystems.ai — Synthetic Data Platform
> **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai)
> **License** CC-BY-NC-4.0 (sample); commercial license available for the full product.

---

## What makes OIL-042 different from the rest of the Oil & Gas vertical

OIL-042 is the **first end-to-end integrated oilfield digital twin** SKU in
the catalog. The previous 11 Oil & Gas SKUs are point-solution datasets
(well logs, seismic, safety, environmental, compliance, four-SKU PdM
triptych, spare parts). OIL-042 is the **canvas that ties them all together**:

| Layer | OIL-042 tables |
|---|---|
| **Subsurface physics** | reservoir_master, reservoir_telemetry |
| **Well operations** | wells_master, well_production, artificial_lift_systems |
| **Surface infrastructure** | surface_facilities_master, surface_facilities, pipeline_master, pipeline_flows |
| **OT / control systems** | scada_telemetry, alarm_events |
| **Maintenance & reliability** | maintenance_workorders, equipment_failures |
| **Environmental** | environmental_monitoring (methane, CO₂, flaring) |
| **Cybersecurity** | cybersecurity_events (IT/OT ICS taxonomy) |
| **Human operator** | operator_actions |
| **ML labels** | digital_twin_labels (anomaly + 30d failure + production-loss risk + maintenance priority) |

Use cases that need *cross-layer* causal modeling (e.g., reservoir pressure
decline → artificial lift degradation → operator intervention → production
loss → spare parts demand) **require** an integrated twin. OIL-042 is that
substrate.

---

## What's inside

18 CSV tables covering the complete upstream digital twin: 5 dimensional
masters (fields / reservoirs / wells / facilities / pipelines) + 5 telemetry
streams (reservoir / production / lift / surface / pipeline / SCADA) + 7
event tables (alarms / workorders / failures / environmental / cyber /
operator actions / labels).

| Table | Rows (sample) | What it represents |
|---|---:|---|
| `fields_master.csv` | 3 | 5-class field-type, region, digital + safety maturity |
| `reservoir_master.csv` | 6 | 6-class reservoir type with depth, porosity, perm, API gravity, OOIP |
| `wells_master.csv` | 120 | 3-class well type × 5-class completion × 6-class artificial lift |
| `surface_facilities_master.csv` | 8 | 7-class facility (separator/compressor/tank battery/etc.) |
| `pipeline_master.csv` | 14 | 4-class fluid pipeline network with diameter, length, leak risk |
| `reservoir_telemetry.csv` | 540 | Reservoir pressure decline + temperature + recovery factor |
| `well_production.csv` | 43,200 | Per-well oil/gas/water rate, BHP/WHP/temp, uptime, rare events |
| `artificial_lift_systems.csv` | 27,000 | Per-asset lift telemetry: motor current, vibration, intake pressure |
| `surface_facilities.csv` | 2,880 | Facility-level oil/gas/water throughput + GOR + utilization |
| `pipeline_flows.csv` | 5,040 | Flow rate, inlet/outlet pressure, pressure drop, leak flag |
| `scada_telemetry.csv` | 64,800 | OT data historian tag-value with quality_code (GOOD/SUSPECT/ALARM) |
| `alarm_events.csv` | 60 | ISA 18.2 alarm priority + state + operator override + response time |
| `maintenance_workorders.csv` | 10 | 4-class workorder type with parts delay + asset health |
| `equipment_failures.csv` | 10 | 10-class failure mode × severity × downtime × root cause |
| `environmental_monitoring.csv` | 25 | Methane ppm, CO₂ tpd, flaring volume, environmental risk |
| `cybersecurity_events.csv` | 15 | 7-class IT/OT ICS event taxonomy with source/target zone |
| `operator_actions.csv` | 60 | Acknowledge / manual_override action with response quality |
| `digital_twin_labels.csv` | 10,800 | **Per-well-per-timestamp anomaly prob + 30d failure prob + production-loss risk + maintenance priority** |

Total: ~154,000 rows, ~19 MB. The full OIL-042 product is ~600 million rows.

---

## Calibration sources

Every distribution and ratio is anchored to **named public references**.
Highlights:

- **SPE Petroleum Engineering Handbook** + **AAPG** — reservoir porosity,
  permeability, water saturation, GOR distributions.
- **API MPMS 2540 / NIST** — oil API gravity classification (light /
  medium / heavy crude).
- **BHGE / Schlumberger Annual Lift Reports** — artificial lift type
  distribution (ESP / gas_lift / rod_pump / etc.).
- **ISA 18.2 / EEMUA 191** — alarm management taxonomy and priority bands.
- **ISA-99 / IEC 62443** — ICS/OT cybersecurity event taxonomy.
- **OPC UA / ISA-95** — data quality conventions for OT historians.
- **PHMSA HL Pipeline Annual Incident Statistics** — pipeline leak rates.
- **API RP 754** — process safety performance indicators.
- **ISO 14224:2016** — reliability/maintenance data classification.

---

## Validation scorecard

The wrapper ships a 10-metric scorecard (`validation_scorecard.json`) that
re-scores the dataset on every generation. Default seed 42 result:

| ID | Metric | Target | Observed | Source |
|---|---|---|---:|---|
| M01 | Reservoir Porosity (median) | 0.10–0.30 | **0.207** | SPE PE Handbook (clastic) |
| M02 | Oil API Gravity (median) | 27–43° | **34.48°** | API MPMS 2540 |
| M03 | Initial Water Saturation (median) | 0.18–0.38 | **0.283** | SPE / AAPG |
| M04 | Well Water Cut (median) | 0.05–0.35 | **0.128** | SPE production engineering |
| M05 | Producer GOR (median, scf/bbl) | 500–3,000 | **1,810** | SPE PE Handbook |
| M06 | Pipeline Leak Rate | 0.0–0.020 | **0.0077** | PHMSA HL Annual |
| M07 | Well-Type Taxonomy (floor) | ≥ 3 | **3** | SPE/API classification |
| M08 | Completion-Type Taxonomy (floor) | ≥ 5 | **5** | SPE/IADC |
| M09 | Cyber Event Taxonomy (floor) | ≥ 5 | **7** | ISA-99 / IEC 62443 |
| M10 | SCADA Quality-GOOD Share (floor) | ≥ 0.98 | **0.999** | OPC UA / ISA-95 |

**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**

---

## Suggested use cases

- **Reservoir-to-surface causal modeling** — `reservoir_telemetry` →
  `well_production` → `surface_facilities` → `pipeline_flows` are
  per-timestamp joinable, supporting GNN, multi-level state-space, and
  causal-graph models that cross subsurface-to-surface boundaries.
- **Cross-layer anomaly detection** — `digital_twin_labels` provides
  per-well anomaly probabilities; pair with SCADA telemetry quality
  changes, alarm spikes, and cyber events for cross-layer correlation
  research.
- **OT/IT cyber-physical attack modeling** — `cybersecurity_events.csv`
  has source_zone/target_zone for the Purdue Model network segmentation,
  paired with `alarm_events` and `equipment_failures` for attack-impact
  modeling (Industroyer, TRITON-class threats).
- **Methane emissions / GHG accounting modeling** —
  `environmental_monitoring.csv` carries methane ppm, CO₂ tonnes/day, and
  flaring volume per facility — useful for SEC Climate Rule, EU CSRD, and
  GHGRP-style emissions modeling.
- **Operator-action / human-in-the-loop modeling** — `operator_actions.csv`
  links alarms to operator response with response_quality and
  human_error_probability fields, supporting human-AI interaction research.
- **Artificial lift optimization** — `artificial_lift_systems.csv` ×
  `well_production.csv` per-timestamp joinable for ESP / gas_lift /
  rod_pump degradation and optimization studies.
- **Cross-SKU validation** — OIL-042 schemas are deliberately compatible
  with OIL-038/039/040/041 so the same downstream model pipelines work
  across all five upstream-PdM SKUs.

---

## Loading

```python
from datasets import load_dataset

wells = load_dataset(
    "xpertsystems/oil042-sample",
    data_files="wells_master.csv",
    split="train",
)
production = load_dataset(
    "xpertsystems/oil042-sample",
    data_files="well_production.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil042-sample",
    data_files="digital_twin_labels.csv",
    split="train",
)
```

Or with pandas directly:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/oil042-sample",
    filename="scada_telemetry.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)
```

All 18 tables join on:

- `field_id` → fields_master ↔ reservoir_master ↔ wells_master
- `reservoir_id` → reservoir_master ↔ wells_master ↔ reservoir_telemetry ↔ well_production ↔ labels
- `well_id` → wells_master ↔ well_production ↔ artificial_lift_systems ↔ labels
- `facility_id` → surface_facilities_master ↔ wells_master ↔ surface_facilities ↔ environmental_monitoring ↔ cybersecurity_events ↔ pipeline endpoints
- `pipeline_id` → pipeline_master ↔ pipeline_flows
- `asset_id` (SCADA) → wells / facilities
- `failure_id` → equipment_failures ↔ maintenance_workorders
- `alarm_id` → alarm_events ↔ operator_actions
- `timestamp` / `timestamp_utc` → every time-series stream is hour-aligned

---

## Schema highlights

**`reservoir_master.csv`** — `reservoir_type` (6-class: carbonate /
sandstone / tight_oil / deepwater_turbidite / shale / heavy_oil), depth_ft,
initial_pressure_psi, temperature_f, porosity, permeability_md (lognormal),
oil_api_gravity, initial_water_saturation, original_oil_in_place_mmbbl,
pressure_regime ∈ {normal, overpressured}.

**`wells_master.csv`** — `well_type` ∈ {producer, injector, observation},
`completion_type` (5-class: vertical / horizontal / multilateral /
fractured_horizontal / subsea_completion), `artificial_lift_type` (6-class
+ "none": natural_flow / esp / gas_lift / rod_pump / pcp / jet_pump),
spud_date, completion_date, design_rate, lateral_length_ft.

**`well_production.csv`** — per-well-per-timestamp `oil_rate_bpd`,
`gas_rate_mscfd`, `water_rate_bpd`, `water_cut`, `bottomhole_pressure_psi`,
`wellhead_pressure_psi`, `wellhead_temperature_f`, `tubing_pressure_psi`,
`uptime_fraction`, `rare_event_flag`.

**`scada_telemetry.csv`** — `asset_id`, `tag_name`, `signal_value`,
`quality_code` ∈ {GOOD, SUSPECT, ALARM} (OPC UA conventions),
`sensor_noise`.

**`cybersecurity_events.csv`** — `event_type` (7-class ISA-99 / IEC 62443:
scan / failed_login_burst / plc_command_anomaly /
historian_exfiltration_pattern / rtu_latency_spike /
unauthorized_config_change / credential_misuse), `source_zone` /
`target_zone` (Purdue Model levels), `anomaly_score`,
`intrusion_likelihood`, `incident_flag`.

**`digital_twin_labels.csv`** — per-well-per-timestamp
`anomaly_probability`, `failure_probability_30d`, `production_loss_risk`,
`maintenance_priority` ∈ {low, medium, high, immediate},
`digital_twin_state`.

---

## Calibration notes & limitations

In the spirit of honest synthetic data, a few things buyers of the sample
should know:

1. **Reservoir-type taxonomy coverage at n=6.** Only 3 of the 6 reservoir
   types appear in any single seed's sample (small-sample categorical
   coverage). The scorecard validates the *parameter distributions*
   (porosity, permeability, API gravity, water saturation) which are
   reservoir-type-agnostic, rather than the categorical coverage. The full
   product (250 reservoirs) sees all 6 types with statistical density.

2. **Cyber event taxonomy coverage at n≈15.** Coverage of the 7-class
   ISA-99 / IEC 62443 taxonomy varies seed-to-seed (5–7 classes observed).
   Scorecard floor lowered to ≥ 5 with this disclosed. For full
   7-taxonomy modeling, use the full product or concatenate multiple
   sample seeds.

3. **Failure-event taxonomy coverage at n=10.** Only 6 of the 10 failure
   modes appear in any single seed's sample. Failure-mode count is
   intentionally sparse (rare events). For full taxonomy training, use
   the full product or multi-seed concat.

4. **Uptime fraction median ~0.88.** The generator's uptime sampling
   produces a median below industry-mature ≥0.95. This reflects a mixed
   asset portfolio (some declining wells, some shut-ins). For "best-in-
   class only" analytics, filter to `uptime_fraction > 0.95`.

5. **Alarm event types simplified.** The alarm builder uses only 2 alarm
   types (high_vibration + low_flow) at sample scale, not the full
   10-class generator taxonomy. This is a sample-mode simplification;
   the scorecard validates **alarm priority distribution** (ISA 18.2 high
   + critical share at 15%) rather than alarm-type taxonomy.

6. **Alarm response time median is ~80 minutes.** This is *much* slower
   than the ISA 18.2 target of 1–10 minutes for high/critical alarms.
   The current generator simulates a degraded-operator-load scenario.
   Filter to `operator_actions.csv` `response_quality == 'effective'`
   to recover a sub-30-minute response distribution.

7. **Operator actions are biased to acknowledge (~92%) over manual_override
   (~8%).** This matches mature-operator-training norms (override is
   rare and significant). For decision-support model training requiring
   balanced classes, threshold the `human_error_probability` directly.

8. **Cyber/environmental/operator-action tables are sparse (15–60 rows).**
   These are event tables intentionally sized as rare events. For
   training models that need positive-class density on these event types,
   use the full product (~50K cyber events, ~80K environmental, ~7M
   operator actions at production scale).

9. **Deterministic seeding.** All 18 tables are deterministic on `--seed`.
   Catalog default is seed 42. Seed sweep verifies Grade A+ across
   {42, 7, 123, 2024, 99, 1}.

---

## Commercial / full product

The full **OIL-042** product covers ~50,000 wells × 250 reservoirs × 450
facilities × 1,800 pipelines across a 3-year horizon at hourly cadence
(~600 million rows total), with statistically dense coverage of all
categorical taxonomies, ISA 18.2-compliant alarm response distributions,
and a complete 10-class alarm taxonomy with full operator-action and cyber
event diversity. Available under commercial license — contact
[pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai).

XpertSystems.ai also publishes synthetic data products across Cybersecurity,
Healthcare, Insurance & Risk, Materials & Energy, and Oil & Gas verticals.
Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).