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Crude Oil Market Data

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Historical and forecast data for global crude oil concepts including supply, demand, trade and pricing. Long term, supply and demand balances for crude and condensate; long term, regional trade balances for crude and condensate; and short and long-term, global pricing trends for crude streams.

Industry Domain:


Energy & Natural Resources

Publisher:


IHS Markit

Content Type:


Structured

Access/Delivery Methods:


REST, Python, S3 Proxy, SQL

Keywords

  • West Texas Intermediate (WTI)
  • Brent
  • Bitumen
  • Asphalt
  • Condensate
  • Sulfur
  • Heavy Sours
  • Medium Sours
  • Light Sours
  • Heavy Sweets
  • Medium Sweets
  • Light Sweets
Interested in this data? Download free data samples, dictionaries and documentation

Data Set

Dataset NameDataset DescriptionData FormatPublishing Frequency
Crude Oil Prices, Trade and Production - MetadataThe data includes a two-year crude oil price outlook that is updated monthly and a 25-year crude oil price outlook that is updated quarterly. In addition, the data include a detailed 25-year outlook for crude oil production, trade, liquids demand, and inventory. This dataset is in a data analytics friendly format containing metadata only, use with the timeseries dataset and join on 'source_id'.ParquetMonthly
Crude Oil Prices, Trade and Production - TimeseriesThe data includes a two-year crude oil price outlook that is updated monthly and a 25-year crude oil price outlook that is updated quarterly. In addition, the data include a detailed 25-year outlook for crude oil production, trade, liquids demand, and inventory. This dataset is in a data analytics friendly format, use with the metadata dataset and join on 'source_id'.ParquetMonthly
Crude Oil Prices, Trade and ProductionThe data includes a two-year crude oil price outlook that is updated monthly and a 25-year crude oil price outlook that is updated quarterly. In addition, the data include a detailed 25-year outlook for crude oil production, trade, liquids demand, and inventory. 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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