Special Issue on Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry

Published 26 January, 2018

Special Issue on "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry"

Over the past two decades, broad-based and intense efforts have been devoted to apply concepts and methodologies from information technology (IT) and artificial intelligence (AI) to informatics research related to energy and environmental systems. Both academic research and industrial practices have generated an impressive amount of literature, which spans virtually every aspect of synergistic work among these disciplines. Informatics and systems analysis techniques are now being widely used by the practicing engineers and scientists to solve a broad range of problems in the petroleum industry. Speaking at the World Economic Forum at Davos Switzerland in 2017, Ginni Rometty, current IBM CEO, described AI’s role in the partnership between humans and machines as “augmented cognition.” In other words, AI not only supports but augments human cognition so that humans can be more efficient and “do a better job” [1]. This should also be true and very beneficial for the petroleum industry.

In addition, petroleum crude and products prices have fallen dramatically since 2014, forcing most petroleum companies to take drastic actions such as layoffs, cutting investments and budgets, and more. As a result, the petroleum industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it very difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak 5 years ago. Operations in the oil and gas industry today require balancing a vast array of trade-offs in the drive for competitive advantage while maximizing return on investment. This situation has created an urgent need to enhance performance while minimizing the cost of production per barrel. A major opportunity for optimization resides in the vast reserve of data generated and collected in the oil and gas fields. The petroleum industry can leverage the technologies of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) to build a foundation for long-term success. If volatility in oil prices is the new norm, which is expected to be the case, the push for “value over volume” is the key to success going forward.

For example, using AI techniques and tools, upstream oil and gas companies can shift their approach from production at all costs to production in context, such that profit and loss management is performed at the well level to optimize the production cost per barrel. This objective can be achieved by integrating all aspects of production management, collecting the data for analysis and forecasting, and applying artificial intelligence technologies to optimize operations. Alternatively, when remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain up to $50 billion in savings and increased profit by adopting AI and related technologies [2].

Petroleum is one of the pre-eminent international journals managed by ELSEVIER, is pleased to be dedicating this Special Issue to "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in Petroleum Industry". Our objective is to address the challenges in this emerging field, and promote the research, development, and application of AI and related technologies, to this important energy industry. Researchers are invited to submit papers for consideration for publication in this Special Issue.

Scope:

The issue will focus on publication of original theory and applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the petroleum industry. Papers can report on work that is experimental or theoretical, mathematical or descriptive, and about chemical or physical systems of the petroleum industry (including refinery and petrochemical productions). Research papers on process design and development, product research and development, and on applications of informatics to the upstream and down-stream operations of petroleum production, and related policy issues, are welcome. For details on how to submit papers, please see the Petroleum website at: http://ees.elsevier.com/petlm/

Topics:

Topics for consideration include, but are not limited to, the following:

Applications of AI techniques including knowledge-based systems, machine learning, deep learning, case-based reasoning, fuzzy logic, artificial neural networks for problem solving in different aspects of the petroleum industry such as:

Planning and forecasting in oil and gas exploration and production
Drilling operations
Well logging and formation evaluation
Reservoir management
Operational planning and execution
Risk management
Economic analysis of optimization
Enhanced oil recovery operations
Predictive maintenance and real-time responses
Well reservoir survey and inspections
Pipeline operations
Deepwater operations
Petroleum waste management
Carbon capture and storage
Refinery and petrochemical plant operations
Natural gas processing operations
Remote logistics and management of petroleum assets
Forecasting of demands and supplies
Oil and gas transportation systems

Important Dates:

The deadline for manuscript submissions is August 15th, 2018, and the target publication date is January 15th, 2019. For details on the online submission system, please see the journal website at http://ees.elsevier.com/petlm/

We look forward to learning about your research work and results in this exciting field of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) to the petroleum industry. It will be our pleasure to embark with you on this journey of discovery about this emerging and important research area.

Editors:

Dr. Paitoon (PT) Tontiwachwuthikul
Honorary Editor-in-Chief of Petroleum (Elsevier) and Chief Editor of the Special Issue on "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry"
Co-founder, Clean Energy Technologies Research Institute (CETRi)
Fellow, Canadian Academy of Engineering (FCAE)
University of Regina, SK CANADA
Email: paitoon@uregina.ca
Google Scholar: http://scholar.google.ca/citations?user=7sB0sckAAAAJ&hl=en

Dr. Christine W. Chan
Co-Editor for the Special Issue on "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry"
Canada Research Chair Tier I in Energy and Environmental Informatics
Fellow, Canadian Academy of Engineering (FCAE)
Founder, Energy Informatics Laboratory
University of Regina, SK CANADA
Email: chancw@uregina.ca

Dr. Fanhua (Bill) Zeng
Co-Editor for the Special Issue on "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry"
Program Chair and Professor, Petroleum Systems Engineering
University of Regina, SK CANADA
Email: fanhua.zeng@uregina.ca

Dr. Zhiwu (Henry) Liang
Co-Editor for the Special Issue on "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry"
Executive Director, Joint International Center for CO2 Capture & Storage(iCCS)
Hunan University, Changsha, Hunan 410082, CHINA
Tel/Fax: +86-731-88573033
Email: zwliang@hnu.edu.cn

Dr. Teerawat Sema
Co-Editor for the Special Issue on "Recent progress and new developments of applications of Artificial Intelligence (AI), Knowledge-based Systems (KBS), and Machine Learning (ML) in the Petroleum Industry"
Group Leader, CO2 Separation and Purification Research Laboratory
Chemical Engineering Department
Mahidol University
Bangkok, THAILAND
Email: teerawat.sem@mahidol.edu

References:

Useful website on the subject:

http://insights.globalspec.com/article/2772/the-growing-role-of-artificial-intelligence-in-oil-and-gas

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