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AuthorRahaman, Md Shokor A.
AuthorIslam, Jahedul
AuthorWatada, Junzo
AuthorVasant, Pandian
AuthorAlhitmi, Hitmi Khalifa
AuthorHossain, Touhid Mohammad
Available date2024-04-16T12:54:42Z
Publication Date2021
Publication NameInternational Journal of Innovative Computing, Information and Control
ResourceScopus
ISSN13494198
URIhttp://dx.doi.org/10.24507/ijicic.17.02.539
URIhttp://hdl.handle.net/10576/53908
AbstractThe most important element for the exploration and development of oil and oil shale is total organic carbon (TOC). TOC estimation is considered a challenge for geologists since laboratory methods are expensive and time-consuming. Therefore, due to the complex and nonlinear relationship between well logs and TOC, researchers have begun to use artificial intelligence (AI) techniques. Hence, the purpose of this research is to explore new paradigms and methods for AI techniques. First, this article provides a recent overview of selected AI technologies and their applications, including artificial neural networks (ANNs), convolutional neural networks (CNNs), hybrid intelligent systems (HISs), and support vector machines (SVMs) as well as fuzzy logic (FL), particle swarm optimization (PSO). Second, this article explores and discusses the benefits and pitfalls of each type of AI technology. The study found that hybrid intelligence technology was the most successful and independent AI model with the highest probability of infer-ring properties of oil shale oil and gas fields (such as TOC) from wireline logs. Finally, some possible combinations are proposed that have not yet been investigated.
SponsorAcknowledgments. The authors would like to thank and highly appreciate Petroleum Research Fund (PRF), Cost Center 0153AB-A33 and the project leader Dr. Eswaran Padmanabhan for supporting the research. The authors also would like to thank Universiti Teknologi PETRONAS (UTP) for Graduate Research Assistantship (GRA) scheme.
Languageen
PublisherICIC International
SubjectArtificial intelligence
Organic shale
Pattern recognition
Total organic carbon (TOC)
Well logs
TitleArtificial intelligence approach to total organic carbon content prediction in shale gas reservoir using well logs: A review
TypeArticle
Pagination539-563
Issue Number2
Volume Number17
dc.accessType Abstract Only


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