Petroleum Science

Aims & Scope

Petroleum Science is the only English journal in China on petroleum science and technology that is intended for professionals engaged in petroleum science research and technical applications all over the world, as well as the managerial personnel of oil companies.

It covers petroleum geology, petroleum geophysics, petroleum engineering, petrochemistry & chemical engineering, petroleum mechanics, and economic management.

It aims to introduce the latest results in oil industry research in China, promote cooperation in petroleum science research between China and the rest of the world, and build a bridge for scientific communication between China and the world.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Earth and Planetary Sciences and Energy, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Preservation url
• Apc url
• License

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Earth and Planetary Sciences and Energy, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A review of CO2 storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches

  Citation: 485

  Authors: Temitope, Jorge Salgado, Achinta

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

  Citation: 262

  Authors: Sreela, M., Fawzi, Ali M.

• A review of development methods and EOR technologies for carbonate reservoirs

  Citation: 232

  Authors: Zheng-Xiao, Song-Yan, Bin-Fei, Dan-Qi, Zhong-Yun, Zhao-Min

• The effect of nanoparticles on reservoir wettability alteration: a critical review

  Citation: 161

  Authors: Hilmy, Yu-Long, Ji-Rui

• Surfactant induced reservoir wettability alteration: Recent theoretical and experimental advances in enhanced oil recovery

  Citation: 157

  Authors: Yefei, Huaimin, Weizhao, Baojun, Xinwang, Jichao

• Petroleum geology features and research developments of hydrocarbon accumulation in deep petroliferous basins

  Citation: 153

  Authors: Xiong-Qi, Cheng-Zao, Wen-Yang

• Water shutoff and conformance improvement: an introduction

  Citation: 152

  Authors: Randy, Bergit

• Characteristics of CO2 sequestration in saline aquifers

  Citation: 139

  Authors: Fang, Baojun, Dazhen, Dunn-Norman, Wronkiewicz

• State of the art of synthetic threshold scale inhibitors for mineral scaling in the petroleum industry: a review

  Citation: 130

  Authors: Musa, Shan-Fa

• Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks

  Citation: 116

  Authors: Tuan-Feng, Peter, Emilien, Ling-Chen, Lin, William