@book{scholars19094,
           pages = {155--174},
           title = {Machine Learning Application in Gas Hydrates},
         journal = {Machine Learning and Flow Assurance in Oil and Gas Production},
       publisher = {Springer Nature},
             doi = {10.1007/978-3-031-24231-1{$_9$}},
            year = {2023},
            note = {cited By 1},
        abstract = {The issue of gas hydrates is one of the major concerns in flow assurance industry. In order to study the gas hydrates theroretically, reserachers have developed different thermodynamic and kinetic models to predict the hydrate formation parame{\^A}?ters. The use of machine learning in gas hydrate inhibition prediction and analysis has become a well-established field of study as computational capability has increased in recent years. In the literature, both supervised and unsupervised learning methods have been applied to predict the gas hydrate parameters. This chapter has discussed the conventional modeling approaches in gas hydrate parameters prediction and the use of machine learning techniques in this field of study. Four different case studies involving the use of machine learning in gas hydrates prediction have also been presented. {\^A}{\copyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174775060&doi=10.1007\%2f978-3-031-24231-1\%5f9&partnerID=40&md5=c19804eb70890243ee386974b579db7b},
            isbn = {9783031242311; 9783031242304},
          author = {Qasim, A. and Lal, B.}
}