Petroleum Science

Open access

ISSN: 1995-8226

Petroleum Science

Open access

Petroleum Science is a peer-reviewed English journal owned by China University of Petroleum-Beijing. It was founded in 2004 and switched into open access since 2015, now it is published in collaborati...

Petroleum Science is a peer-reviewed English journal owned by China University of Petroleum-Beijing. It was founded in 2004 and switched into open access since 2015, now it is published in collaboration with KeAi. Petroleum Science publishes the latest and most significant advances in fundamental theoretical and applied research of the petroleum and petrochemical fields. It includes:

  1. petroleum geology and geophysics
  2. petroleum engineering and mechanics
  3. petrochemistry and chemical engineering
  4. petroleum economics and management.

Petroleum Science is committed to encourage communication of novel and significant research results across the entire spectrum of petroleum science and engineering, and build a high-level international platform for academic exchange in petroleum and petrochemical fields. Petroleum Science has established excellent reputation and is growing into a highly influential, leading journal in global petroleum area.

Productivity enhancement in multilayered coalbed methane reservoirs by radial borehole fracturing

Investigation of oil and water migrations in lacustrine oil shales using 20 MHz 2D NMR relaxometry techniques

A novel hybrid thermodynamic model for pore size distribution characterisation for shale

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Review of surfactant-assisted chemical enhanced oil recovery for carbonate reservoirs: challenges and future perspectives

Emission and performance analysis of a diesel engine burning cashew nut shell oil bio diesel mixed with hexanol

A review of CO<inf>2</inf> storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches

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News

Petroleum Science Latest impact factor: 4.757

Petroleum Science CiteScore 2021 Announced: 6.3

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Call for Papers

Special Issue on InterPore2022: Uncertainty quantification and machine learning in porous media

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