@article{scholars15001,
            note = {cited By 163},
          volume = {200},
             doi = {10.1016/j.petrol.2020.108182},
            year = {2021},
           title = {Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models},
         journal = {Journal of Petroleum Science and Engineering},
       publisher = {Elsevier B.V.},
          author = {Otchere, D. A. and Arbi Ganat, T. O. and Gholami, R. and Ridha, S.},
            issn = {09204105},
        abstract = {The advent of Artificial Intelligence (AI) in the petroleum industry has seen an increase in its use in exploration, development, production, reservoir engineering and management planning to accelerate decision making, reduce cost and time. Supervised machine learning has gained much popularity in establishing a relationship between complex non-linear datasets. This type of machine learning algorithm has showcased its superiority over petroleum engineering regression techniques in terms of prediction errors for high dimensional data, computational power and memory. This review focuses on the most widely used machine learning algorithm employed in the petroleum industry, the Artificial Neural Network (ANN) with different shallow models used in reservoir characterisation. The Support Vector Machine (SVM) and Relevant Vector Machine (RVM) has over the years emerged as competitive algorithms where in most cases based on this review it outperformed the ANN. This makes it preferable than the ANN when there are limited data sets. Finally, hybridisation of multiple algorithms methodologies also showed improved performance over singularly applied algorithms offering a pathway in improving reservoir characterisation based on supervised machine learning as future scope of work. {\^A}{\copyright} 2020 Elsevier B.V.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098974749&doi=10.1016\%2fj.petrol.2020.108182&partnerID=40&md5=1e3df23ecf3ddc04251ec0afa697f8b5},
        keywords = {Clustering algorithms; Cost engineering; Decision making; Gasoline; Learning systems; Neural networks; Petroleum analysis; Petroleum industry; Petroleum prospecting; Petroleum reservoir engineering; Predictive analytics; Reservoir management; Support vector machines, Comparative analysis; Competitive algorithms; High dimensional data; Regression techniques; Relevant vector machines; Reservoir characterisation; Reservoir engineering; Supervised machine learning, Learning algorithms, algorithm; artificial intelligence; artificial neural network; comparative study; model; petroleum engineering; prediction; reservoir characterization; rock property; supervised learning; support vector machine}
}