Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 364, in tsfile.tsfile_reader.TsFileReaderPy.__init__
                File "tsfile/tsfile_reader.pyx", line 367, in tsfile.tsfile_reader.TsFileReaderPy.init_reader
                File "tsfile/tsfile_py_cpp.pyx", line 789, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
                File "tsfile/tsfile_py_cpp.pyx", line 55, in tsfile.tsfile_py_cpp.check_error
              tsfile.exceptions.FileOpenError: 28: Failed to open file
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

OIL-013 — Synthetic Production Time-Series Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/oil013-sample.

Overview

A free, schema-identical preview of XpertSystems.ai's enterprise production time-series dataset for decline curve ML, artificial-lift optimization, workover-candidate prediction, and water-breakthrough forecasting. The sample covers 250 wells across 10 global basins and 8 asset types, simulated over 365 days, with 116,154 rows including 91,250 daily production records linked across 11 tables.

  • Tables: 11 TsFile table(s), 116,154 rows in total.
    • artificial_lift: 13,250 rows, 2020-01-14 00:00:00 → 2022-12-25 00:00:00 (UTC)
    • daily_production: 91,250 rows, 2020-01-14 00:00:00 → 2022-12-25 00:00:00 (UTC)
    • downtime_events: 491 rows, 2020-02-04 00:00:00 → 2022-11-24 00:00:00 (UTC)
    • facility_constraints: 200 rows, 2020-01-01 00:00:00 → 2020-01-01 00:00:00.199000 (UTC)
    • flow_assurance_events: 47 rows, 2020-03-20 00:00:00 → 2022-09-09 00:00:00 (UTC)
    • injection_support: 40 rows, 2020-09-06 00:00:00 → 2022-08-10 00:00:00 (UTC)
    • production_allocations: 625 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.624000 (UTC)
    • production_labels: 3,250 rows, 2020-01-14 00:00:00 → 2022-12-21 00:00:00 (UTC)
    • reservoir_pressure: 6,750 rows, 2020-01-14 00:00:00 → 2022-12-25 00:00:00 (UTC)
    • stimulation_events: 1 rows, 2020-09-11 00:00:00 → 2020-09-11 00:00:00 (UTC)
    • wells_master: 250 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.249000 (UTC)

Schema (TsFile structure)

artificial_lift.tsfile

  • Time (INT64, milliseconds) — the source record_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (8): lift_id (string), well_id (string), lift_type (string), esp_frequency_hz (double), pump_efficiency (double), motor_temperature_f (double), vibration_index (double), runtime_pct (double)

daily_production.tsfile

  • Time (INT64, milliseconds) — the source production_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (16): prod_id (string), well_id (string), oil_rate_bopd (double), gas_rate_mscfd (double), water_rate_bwpd (double), water_cut_pct (double), gor_scf_bbl (double), choke_size_64ths (double), tubing_pressure_psi (double), casing_pressure_psi (double), uptime_fraction (double), cumulative_oil_bbl (double), cumulative_gas_mscf (double), cumulative_water_bbl (double), anomaly_flag (int64), anomaly_type (string)

downtime_events.tsfile

  • Time (INT64, milliseconds) — the source start_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (6): event_id (string), well_id (string), downtime_type (string), duration_hours (double), lost_oil_bbl_est (double), root_cause_category (string)

facility_constraints.tsfile

  • Time (INT64, milliseconds) — the source effective_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (7): constraint_id (string), facility_id (string), field_name (string), throughput_limit_bopd (double), gas_handling_limit_mscfd (double), water_handling_limit_bwpd (double), constraint_severity (string)

flow_assurance_events.tsfile

  • Time (INT64, milliseconds) — the source event_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (8): flow_event_id (string), well_id (string), flow_assurance_type (string), scale_index (double), paraffin_index (double), sand_index (double), corrosion_risk_index (double), intervention_required_flag (int64)

injection_support.tsfile

  • Time (INT64, milliseconds) — the source record_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (8): inj_id (string), injector_id (string), producer_id (string), field_name (string), injection_rate_bwpd (double), injection_pressure_psi (double), producer_response_lag_days (int64), correlation_score (double)

production_allocations.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 (6): alloc_id (string), well_id (string), zone_name (string), allocation_pct (double), allocation_method (string), confidence_score (double)

production_labels.tsfile

  • Time (INT64, milliseconds) — the source label_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (8): label_id (string), well_id (string), forecast_class (string), workover_candidate_flag (int64), water_breakthrough_flag (int64), steep_decline_flag (int64), lift_limited_flag (int64), next_90d_expected_decline_pct (double)

reservoir_pressure.tsfile

  • Time (INT64, milliseconds) — the source test_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (6): pressure_id (string), well_id (string), reservoir_pressure_psi (double), bhfp_psi (double), pressure_drawdown_psi (double), pressure_test_quality (string)

stimulation_events.tsfile

  • Time (INT64, milliseconds) — the source event_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (7): stim_id (string), well_id (string), event_type (string), frac_stage_count (int64), treatment_volume_bbl (double), expected_uplift_pct (double), actual_uplift_pct (double)

wells_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 (24): well_id (string), api_like_well_code (int64), field_name (string), pad_id (string), basin_name (string), formation_name (string), asset_type (string), completion_type (string), lift_type (string), operator_class (string), first_production_date (string), lateral_length_ft (int64), proppant_lb_per_ft (double), true_vertical_depth_ft (int64), initial_oil_rate_bopd (double), initial_gas_rate_mscfd (double), initial_water_cut_pct (double), initial_gor_scf_bbl (double), nominal_decline_rate_annual (double), arps_b_factor (double), initial_reservoir_pressure_psi (double), reservoir_temperature_f (double), longitude (double), latitude (double)

Conversion notes

  • One TsFile per source CSV (11 tables) for this oil-well production 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:

from pathlib import Path
from tsfile import TsFileReader

path = Path("artificial_lift.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/oil013-sample
  • Source card title / publisher: OIL-013 — Synthetic Production Time-Series Dataset (Sample)
  • License: cc-by-nc-4.0 (as declared by the original dataset).
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