Special Issue on Advancements of Artificial Intelligence Techniques Related to Geology and Petroleum Engineering of Subsurface Gas Storage

Artificial intelligence (AI) is revolutionizing various aspects of the oil and gas industry. AI enables real-time optimization, automation and decision-making based on insights from vast amounts of data, which are expected to rise at a compound annual rate of 12.5% from 2024 to 2031.

This special issue aims to disseminate the latest research advancing the application of soft computing methods and predictive analytics in three main domains of petroleum industry: geology, reservoir engineering and drilling. The focus areas include AI for gas storage reservoirs for natural gas, hydrogen and carbon dioxide. We invite contributions addressing reservoir properties modelling, development, wellbore design and enhanced oil recovery (EOR) techniques.

This special issue also welcomes submissions on AI for production forecasting, together with well performance characterization and optimization. Additional areas of interest include hybrid machine learning (ML) and/or deep learning (DL) models, model interpretation, economic evaluations and techniques for industry-wide deployment of AI solutions.

The goal of this special issue is to encourage analytical approaches in petroleum geology and engineering related to subsurface gas storage reservoirs to drive data-driven operations, process understanding and decision-making.

Topics covered:

1. Leveraging AI for optimized development of diverse reservoir types
2. Leveraging AI for intelligent reservoir development and optimal placement wells
3. Leveraging AI for optimized EOR selection and performance prediction
4. Automated methods for leveraging suitable EOR screening and process selection
5. Leveraging AI for optimization of intensified reservoir management techniques
6. AI-enabled prediction and mitigation of reservoir issues and challenges
7. Prediction of oil and gas conventional and unconventional reservoir properties
8. Leveraging machine learning for real-time reservoir optimization
9. Leveraging AI for accurate production forecasting across reservoir types
10. Optimizing well designs and drilling techniques related to gas storage reservoirs
11. AI-Enabled wells performance characterization and prediction
12. Prediction of production issues and challenges
13. Leveraging AI for optimized artificial lift performance prediction and control
14. Prediction and mitigation of production challenges
15. Data-driven based machine learning for carbon and hydrogen subsurface storage

Important Deadlines:

• Submission Open Date: September 01, 2024
• Submission Deadline: March 31, 2025
• Editorial Acceptance Deadline: June 30, 2025

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online, please select SI: AI Subsurface Gas Storage on submission. All submissions will undergo a normal peer-review process. 

Guest Editors:

• David A Wood, Ph.D. (Executive Guest Editor), DWA Energy Limited, Lincoln, United Kingdom. E-mail: dw@dwasolutions.com
• Shadfar Davoodi, Ph.D., Tomsk Polytechnic University, Russia. E-mail: davoodis@hw.tpu.ru
• Hung Vo Thanh, Ph.D., Van Lang University, Vietnam. E-mail:vothanh@vlu.edu.vn 

Guest Editors Biographies:

David A Wood

Dr. David A. Wood is Principal Consultant with DWA Energy Limited, a U.K.-based energy consultancy. Following his PhD in geochemistry from Imperial College, London in 1977, he held post-doctoral fellowships at Institut de Physique du Globe de Paris and University of Birmingham (U.K.) studying ocean floor geochemistry and participating in deep-water exploration. Following a long career in the energy sector, he now works primarily on international energy, petroleum and environmental projects from geological, engineering, and machine-learning perspectives. He has published more than 530 peer-reviewed articles and seven books (as co-author and/or editor), the citations from which provide him with an H-index of >50. He maintains active research collaborations in diverse scientific and engineering fields.

Shadfar Davoodi

Dr. Shadfar Davoodi is a Research Associate at Tomsk Polytechnic University. He holds an M.Sc. in Petroleum Engineering from Sharif University of Technology (Iran), and PhDs in Petroleum Engineering and Information System Analysis from Tomsk Polytechnic University. His research primarily focuses on applying soft computing techniques to address various challenges in Drilling Technology, Petroleum Geomechanics, and Carbon Capture, Utilization, and Storage (CCUS). Dr. Davoodi has co-authored over 50 original and review publications in high-impact journals indexed by SCIE. His significant contributions to Data Science and Applied Machine Learning were recognized with the 2023 Regional Data Science and Engineering Analytics Award by the Society of Petroleum Engineers. He also collaborates with the Journal of Petroleum Exploration and Production Technology as a Guest Associate Editor.

Hung Vo Thanh

Dr. Hung Vo Thanh is an eminent machine learning geoscientist renowned for his specialization in Carbon Capture, Utilization, and Storage (CCUS), underground gas storage, and CO2-related studies. Marking his excellence and influence in the field, Dr. Vo Thanh's work has been acknowledged through several prestigious fellowships in South Korea, including the esteemed BrainPOP Fellowship. He has authored over 70 articles in SCIE-indexed journals, covering a range of topics from CCUS and machine learning to broader geoscience disciplines