Application of AI in Petroleum Sciences and Underground Carbon Storage
A special issue of AI (ISSN 2673-2688).
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1067
Special Issue Editors
Interests: reservoir characterization; acoustic impedance inversion; multiattribute analysis; CMY and RGB color blending; seismic sequence stratigraphy; seismic sedimentology; well logging; machine learning; lake sedimentation; velocity modeling; fault modeling; fracture genesis; reservoir modeling; facies classification; coal bed methane; shale gas; remote sensing
Interests: petroleum; reservoir; optimization; artificial intelligence; machine learning; carbon storage
Interests: unconventional and conventional reservoir characterization; formation evaluation; machine learning; fault and fracture modeling; sedimentary geology; CCS
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Special Issue Information
Dear Colleagues,
Throughout the last decade, artificial intelligence (AI) has had a substantial impact on a wide range of businesses by improving their operational efficiency. AI applications in the petroleum sciences and underground carbon storage were pursued relatively late, although research in this area has been extensive and cannot be disregarded. This Special Issue aims to highlight how various AI techniques have been used to provide more accurate findings by avoiding extensive numerical/analytical modeling in petroleum sciences and carbon storage. In particular, AI is considered crucial for maximizing oil recovery performance and minimizing carbon emissions to cope with climate change.
Artificial intelligence and machine learning have become hot research topics in geophysics, geology, and petroleum engineering with the advancement of computer sciences. The research topic aims to collect recent advances related to artificial intelligence and machine learning in geosciences for advanced interpretation.
Additionally, the steady increase in carbon dioxide (CO2) emission in the atmosphere is the primary factor contributing to global warming or climate change. Due to this fact, the transition from fossil fuel to renewable energy is impossible in the near term because of the significant role of fossil fuels in energy generation and the societal demand for energy for technological and economic progress. According to the Paris Agreement, underground carbon storage (UCS) is regarded as a potentially effective pathway to decrease CO2 emissions and achieve the long-term temperature goal of a rise less than 2 °C.
We invite the contribution of innovative technical developments, case studies, analyses, reviews, and assessments, which are relevant to AI for petroleum sciences and underground carbon storage.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- AI in petrophysics;
- AI in petroleum geology;
- Advanced geophysical exploration;
- CO2-EOR and storage;
- Unconventional resources;
- AI for production forecasts;
- Reservoir modeling and characterization;
- Advanced carbon storage modeling;
- History matching and production optimization;
- Geological modeling;
- Underground carbon storage.
I look forward to receiving your contributions.
Dr. Umar Ashraf
Dr. Hung Vo Thanh
Dr. Aqsa Anees
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI
- petroleum engineering
- well logging
- geophysical exploration
- machine learning
- reservoir modeling and characterization
- carbon storage and utilization
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