wangdx25/oil033_sample

OIL-033 — Synthetic Crude & Product Inventory Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/oil033-sample.

Overview

A free, schema-identical preview of XpertSystems.ai's enterprise crude oil and refined product inventory dataset for EIA-style weekly inventory forecasting, tank farm utilization optimization, SPR operations modeling, shortage risk classification, seasonal demand pattern ML, disruption event prediction, and PADD-regional inventory analytics. The sample covers 235 storage sites across 5 EIA PADD-aligned regions (USGC, Midwest, East Coast, West Coast, Cushing) and 5 storage types (Tank Farm, SPR, Refinery, Terminal, Floating) over 365 days of daily operations, with 257,936 rows across 6 tables.

• Tables: 6 TsFile table(s), 257,936 rows in total.
  • crude_inventory_levels: 85,775 rows, 2023-01-01 00:00:00 → 2023-12-31 00:00:00.234000 (UTC)
  • disruption_events: 11 rows, 2023-01-18 00:00:00 → 2023-12-05 00:00:00 (UTC)
  • inventory_labels: 85,775 rows, 2023-01-01 00:00:00 → 2023-12-31 00:00:00.234000 (UTC)
  • inventory_master: 235 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.234000 (UTC)
  • refined_product_inventory: 85,775 rows, 2023-01-01 00:00:00 → 2023-12-31 00:00:00.234000 (UTC)
  • spr_operations: 365 rows, 2023-01-01 00:00:00 → 2023-12-31 00:00:00 (UTC)

Schema (TsFile structure)

crude_inventory_levels.tsfile

• Time (INT64, milliseconds) — the source timestamp column.
• TAG: none — the table is stored as a single device.
• FIELD (5): inventory_id (string), inventory_bbl (double), inflow_bpd (double), outflow_bpd (double), utilization_pct (double)

disruption_events.tsfile

• Time (INT64, milliseconds) — the source event_date column.
• TAG: none — the table is stored as a single device.
• FIELD (2): event_type (string), severity_score (double)

inventory_labels.tsfile

• Time (INT64, milliseconds) — the source timestamp column.
• TAG: none — the table is stored as a single device.
• FIELD (3): inventory_id (string), shortage_risk (int64), optimization_score (double)

inventory_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 (5): inventory_id (string), region (string), storage_type (string), capacity_bbl (int64), working_capacity_pct (double)

refined_product_inventory.tsfile

• Time (INT64, milliseconds) — the source timestamp column.
• TAG: none — the table is stored as a single device.
• FIELD (4): inventory_id (string), gasoline_bbl (double), diesel_bbl (double), jetfuel_bbl (double)

spr_operations.tsfile

• Time (INT64, milliseconds) — the source timestamp column.
• TAG: none — the table is stored as a single device.
• FIELD (3): spr_site (string), release_rate_bpd (int64), reserve_level_bbl (int64)

Conversion notes

• One TsFile per source CSV (6 tables); each table is stored as a single device, so the rig / well / tank / equipment / sensor identifiers stay ordinary FIELDs.
• Time = the table's own clock column as INT64 milliseconds (event timestamps, sample or inspection dates, and daily record dates). The clock columns used here are event_date, timestamp.
• Tables with no clock column (inventory_master) use a synthetic row index over their stable source order as Time.
• No column is dropped. Rows that share a timestamp are ordered by a 1 ms step in stable source order, so daily tables keep their exact calendar day.

Usage

Read this dataset with the Java SDK. It is written with the zstd compressor. The Apache TsFile Java SDK reads every compressor, but the Python SDK (pip install tsfile) implements only UNCOMPRESSED, SNAPPY, GZIP and LZ4; opening this file with it yields no rows.

Read a converted file with the Apache TsFile Java SDK (org.apache.tsfile:tsfile):

import org.apache.tsfile.file.metadata.IDeviceID;
import org.apache.tsfile.read.TsFileReader;
import org.apache.tsfile.read.TsFileSequenceReader;
import org.apache.tsfile.read.common.Path;
import org.apache.tsfile.read.expression.QueryExpression;
import org.apache.tsfile.read.query.dataset.QueryDataSet;
import org.apache.tsfile.write.schema.MeasurementSchema;

import java.util.ArrayList;
import java.util.List;

String path = "crude_inventory_levels.tsfile";
try (TsFileSequenceReader sequenceReader = new TsFileSequenceReader(path);
     TsFileReader reader = new TsFileReader(sequenceReader)) {
    // Devices are enumerated as IDeviceID objects. Tables with TAG columns
    // have multi-segment device ids that cannot be rebuilt from their string
    // form, so always use getAllDevices() rather than getAllDeviceIds().
    for (IDeviceID device : sequenceReader.getAllDevices()) {
        List<Path> paths = new ArrayList<>();
        for (MeasurementSchema schema : reader.getMeasurement(device)) {
            String name = schema.getMeasurementName();
            if (name != null && !name.isEmpty() && !name.equalsIgnoreCase("Time")) {
                paths.add(new Path(device, name, true));
            }
        }
        QueryDataSet dataSet = reader.query(QueryExpression.create(paths, null));
        while (dataSet.hasNext()) {
            System.out.println(dataSet.next());
        }
    }
}

Source & license

• Original dataset: xpertsystems/oil033-sample
• Source card title: OIL-033 — Synthetic Crude & Product Inventory Dataset (Sample)
• License: cc-by-nc-4.0 (as declared by the original dataset).