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.

1. Recent developments in high-performance Nafion membranes for hydrogen fuel cells applications

2. CO2 storage with enhanced gas recovery (CSEGR): A review of experimental and numerical studies

3. A review of high-performance MEMS sensors for resource exploration and geophysical applications

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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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