@article{scholars15216,
            year = {2021},
           pages = {225--244},
         journal = {International Journal of Innovative Computing, Information and Control},
       publisher = {ICIC International},
             doi = {10.24507/ijicic.17.01.225},
          number = {1},
          volume = {17},
            note = {cited By 9},
           title = {Lithology prediction using well logs: A granular computing approach},
        abstract = {With the advancement of machine learning and artificial intelligence, the automated estimation of a bed{\^a}??s complex lithology has become one of the most crucial requirements in petroleum engineering because of its important role in reservoir characterization. In the past geophysical modelling, petro-physical analysis, artificial intelligence and several statistical approaches have been implemented to estimate lithology since prediction of lithology from recorded continuous cores are very expensive and unprofitable. Geoscience researchers often encounter uncertain, inexact, and vague data in the process of lithology identification that results in inefficient classification. Additionally, the complexities that are coupled to the lithology trends and their equivalent fluid responses, produce ambiguity and confuse the models. The goal of this work is to develop a lithology prediction technique by applying rough set theory (RST) as a granular computing approach to construct logical rules from an inconsistent information system that includes data from several well log attributes including the lithology indicator, SQp and the fluid indicator, SQs that have noticeable contribution in lithology classification. In addition, the rules will be established as a baseline for application in practice and future developments for multivariate well-log analysis. The results were validated with cutting data, and it was proved that the proposed approach has classified the lithology effectively with misclassification rate less than 18 which is less than other methods in comparison. Moreover, the result has confirmed that the method has a promising prospect as a lithology prediction tool, especially in real-time operation, because of the white-box nature of the module that represents the ability of describing the model{\^a}??s calculation steps and results in easily understandable form. {\^A}{\copyright} 2021, ICIC International. All rights reserved.},
        keywords = {Artificial intelligence; Classification (of information); Computation theory; Forecasting; Granular computing; Petroleum geology; Petroleum reservoir engineering; Rough set theory; Well logging, Geophysical modelling; Inconsistent information systems; Lithology classification; Lithology identification; Misclassification rates; Prediction techniques; Reservoir characterization; Rough set theory (RST), Lithology},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100613055&doi=10.24507\%2fijicic.17.01.225&partnerID=40&md5=7420d35d2581d73d2eaa66a51329cc20},
            issn = {13494198},
          author = {Hossain, T. M. and Watada, J. and Aziz, I. A. and Hermana, M. and Meraj, S. T. and Sakai, H.}
}