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Refining and Product Market Data

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Historical and forecast data for refined products including supply, demand, trade and pricing. Long term, global supply and demand balances for refined products; long term, regional trade balances for refined products; and short and long-term, global pricing trends for refined products.

Industry Domain:


Energy & Natural Resources

Publisher:


IHS Markit

Content Type:


Structured

Access/Delivery Methods:


REST, Python, S3 Proxy, SQL

Keywords

  • Diesel
  • biodiesel
  • gasoil
  • gasoline
  • jet fuel
  • kerosene
  • naphtha
  • residual fuel oil
  • ethanol fuel
  • natural gas liquids
  • refining margins
  • alternative fuels
  • sulfur
  • octane
Interested in this data? Download free data samples, dictionaries and documentation

Data Set

Dataset NameDataset DescriptionData FormatPublishing Frequency
Refined Product, Outlook, Prices and Margins - TimeseriesHistorical, short-term, and long-term pricing forecast for refined products, and global pricing trends for refined products. Historical and long-term forecasts for global and regional refined product supply, demand, and trade. This dataset is in a data analytics friendly format, use with the metadata dataset and join on 'source_id'.ParquetMonthly
Refined Product, Outlook, Prices and Margins - MetadataHistorical, short-term, and long-term pricing forecast for refined products, and global pricing trends for refined products. Historical and long-term forecasts for global and regional refined product supply, demand, and trade. This dataset is in a data analytics friendly format containing metadata only, use with the timeseries dataset and join on 'source_id'.ParquetMonthly
Refined Product, Outlook, Prices and MarginsHistorical, short-term, and long-term pricing forecast for refined products, and global pricing trends for refined products. Historical and long-term forecasts for global and regional refined product supply, demand, and trade. This dataset is in a nested parquet format, for more data analytics friendly format use the separate timeseries and metadata datasets and join on 'source_id'.ParquetMonthly
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