---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- tsfile
- timeseries
- synthetic
- oil-and-gas
- midstream
- downstream
- storage-tanks
- inventory-management
- api-650
- api-653
- mass-balance
- xpertsystems
- time-series
- format:tsfile
pretty_name: OIL-028 — Synthetic Tank Storage Dataset (Sample) (TsFile)
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: inventory_levels
    path: inventory_levels.tsfile
  - split: optimization_labels
    path: optimization_labels.tsfile
  - split: scada_telemetry
    path: scada_telemetry.tsfile
  - split: tank_integrity
    path: tank_integrity.tsfile
  - split: tank_master
    path: tank_master.tsfile
  - split: tank_temperature_profiles
    path: tank_temperature_profiles.tsfile
---

# OIL-028 — Synthetic Tank Storage Dataset (Sample) (TsFile)

Apache TsFile version of [`xpertsystems/oil028-sample`](https://huggingface.co/datasets/xpertsystems/oil028-sample).

## Overview

A free, schema-identical preview of XpertSystems.ai's enterprise tank storage dataset for **inventory forecasting, tank utilization optimization, overfill prevention, tank integrity ML, SCADA telemetry analytics, and storage terminal operations**. The sample covers **500 tanks** across **6 product types** (Crude Oil, Diesel, Gasoline, Jet Fuel, LNG, LPG) and **3 tank type categories** (Floating Roof, Fixed Roof, Cryogenic), with **294,500 rows** linked across **6 tables** spanning **168 hours (1 week) of hourly time-series**.

- **Tables:** 6 TsFile table(s), 294,500 rows in total.
  - `inventory_levels`: 84,000 rows, 2025-01-01 00:00:00 → 2025-01-07 23:00:00.499000 (UTC)
  - `optimization_labels`: 500 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.499000 (UTC)
  - `scada_telemetry`: 125,000 rows, 1970-01-01 00:00:00 → 1970-01-01 00:02:04.999000 (UTC)
  - `tank_integrity`: 500 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.499000 (UTC)
  - `tank_master`: 500 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.499000 (UTC)
  - `tank_temperature_profiles`: 84,000 rows, 2025-01-01 00:00:00 → 2025-01-07 23:00:00.499000 (UTC)

## Schema (TsFile structure)

### `inventory_levels.tsfile`

- **Time** (INT64, milliseconds) — the source `timestamp` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (5): `tank_id` (string), `inventory_bbl` (double), `fill_pct` (double), `inflow_bbl` (double), `outflow_bbl` (double)

### `optimization_labels.tsfile`

- **Time** (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (4): `tank_id` (string), `optimization_score` (double), `utilization_grade` (string), `failure_risk` (double)

### `scada_telemetry.tsfile`

- **Time** (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (5): `sensor_id` (string), `tank_id` (string), `signal_value` (double), `latency_ms` (double), `signal_quality` (double)

### `tank_integrity.tsfile`

- **Time** (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (5): `tank_id` (string), `corrosion_rate_mpy` (double), `sludge_depth_ft` (double), `leak_probability` (double), `integrity_score` (double)

### `tank_master.tsfile`

- **Time** (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (7): `tank_id` (string), `tank_type` (string), `product_type` (string), `capacity_bbl` (int64), `diameter_ft` (double), `height_ft` (double), `commission_year` (int64)

### `tank_temperature_profiles.tsfile`

- **Time** (INT64, milliseconds) — the source `timestamp` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (4): `tank_id` (string), `temperature_f` (double), `ambient_temp_f` (double), `vapor_pressure_index` (double)

## Conversion notes

- One TsFile per source CSV (6 tables) for this tank-farm inventory sample; each table is stored as a single device, so the rig/well/tank/sensor ids stay ordinary FIELDs.
- Time = the table's own timestamp column as INT64 milliseconds (event timestamps, daily record/production dates, or test dates). Master/inventory tables that carry no clock (`rigs_master`, `sensors_master`, `wells_master`, `tank_master`, the label tables and `scada_telemetry`) use a synthetic row index as Time.
- No column is dropped. Rows that share a timestamp are ordered by a 1 ms step in stable source order; daily tables keep their exact calendar day.

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("inventory_levels.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
```

## Source & license

- Original dataset: [`xpertsystems/oil028-sample`](https://huggingface.co/datasets/xpertsystems/oil028-sample)
- Source card title / publisher: OIL-028 — Synthetic Tank Storage Dataset (Sample)
- License: cc-by-nc-4.0 (as declared by the original dataset).