Special Issue on Intelligent Reservoir Characterisation for Complex Carbonates

Introduction:

Carbonate reservoirs host around 60% of the world's oil and gas reserves. There formations, however, exhibit extreme heterogeneity across all scales, from microscopic pore structures and fracture networks to the macroscopic field scale. Moreover, manual-dominated technical workflows suffer from high labor intensity, strong subjectivity and slow iteration, severely impeding the in-depth understanding of complex fluid flow systems within carbonate reservoirs, as well as hindering the efficient development of hydrocarbon resources.

This Special Issue presents the latest advances in machine-learning/deep-learning methodologies in line with the national "AI+" action plan outlined in the 15th Five-Year Plan. The aim is to accelerate the intelligent transformation of the oil and gas industry, and address core challenges in carbonate reservoir characterisation and development. The Special Issue will systematically demonstrate how data-driven intelligent approaches can transform microscale image analysis and automated petrophysical interpretation into robust quantitative workflows. It also aims to clarify the transformation pathway of core-log-seismic data fusion into actionable field-scale models, with a focus on the characterisation and modelling of complex geological features, such as high-permeability streaks and bitumen layers. These data-driven methodologies would offer practical technical solutions for shortening reservoir characterisation cycles and optimizing development strategies for large-scale carbonate reservoirs, as well as provide valuable insights for the characterisation of clastic reservoirs.

Topics covered:

• Multi-scale and Multi-modal Geological Data Integration
• Deep Learning Geological Image Segmentation and Object Detection
• Intelligent Prediction of Carbonate Fracture Networks
• Deep Feature Extraction from Multi-attribute of Seismic Data
• Intelligent Carbonate Reservoir Prediction
• Intelligent Uncertainty Quantification for Reservoir Modelling
• Intelligent Characterisation Workflows for High-permeability and Bitumen Layers

Important Deadline:

• Submission deadline: 30 September 2026

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online, please select "SI: Intelligent Reservoir Characterisation" on submission. All submissions will undergo a normal peer-review process.

Guest Editors:

• Dr. ZHANG Leifu, CNPC R&D, UAE. zhangleifu@cnpcrd.com
• Dr. ZHANG Sihai, CNPC R&D, UAE. zhangsihai@cnpcrd.com
• Dr. WU Bohong, CNPC RIPED, China. wubohong@cnpcint.com
• Dr. GUO Yanjun, Peking University, China. yanjunguo@pku.edu.cn
• Dr. YUAN Cheng, CNPC RIPED, China. yc0124@petrochina.com.cn
• Mr. SHAO Guanming, CNPC RIPED, China. shaogm_hz@petrochina.com.cn
• Dr. LIU Xin, CNPC R&D, UAE. liuxin@cnpcrd.com
• Ms. Sara Harast Khan, CNPC R&D, UAE. sarahasratkhan@cnpcrd.com

About the journal (JNGGS)

The Journal of Natural Gas Geoscience (JNGGS) is an academic bimonthly journal focused on natural gas exploration and exploitation, sponsored by Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences AND Research Institute of Petroleum Exploration and Development, PetroChina. This journal publishes original research and review articles across a broad range of topics, including natural gas geology, natural gas geochemistry, natural gas geophysics, natural gas field engineering, unconventional natural gas, non-hydrocarbon gases (such as helium, natural hydrogen, carbon dioxide, etc.), natural gas and the environment, and natural gas resources and economics. The primary aim of this journal is to promote the worldwide development of natural gas exploration and exploitation and academic exchange among natural gas researchers and workers. The primary tasks of this journal are to review the present situation and the development of natural gas geosciences, to report new theories, methods, and technologies, to publish new results in global natural gas research, exploration, and exploitation, and to study the contribution of natural gas to sustainable economic and social development.

The journal is indexed by ESCI, SCOPUS, GEOBASE, GeoRef, Chemical Abstract Service, Petroleum Abstracts, INSPEC, EBSCOhost, DOAJ, Cabells Journalytics, NASA ADS, etc.

Website: https://www.sciencedirect.com/journal/journal-of-natural-gas-geoscience

E-mail: jnggs@lzb.ac.cn