@article{scholars15992,
            year = {2021},
       publisher = {Springer Science and Business Media Deutschland GmbH},
         journal = {Studies in Systems, Decision and Control},
           pages = {59--75},
            note = {cited By 6},
          volume = {320},
             doi = {10.1007/978-981-15-8606-4{$_4$}},
           title = {Artificial Neural Network Modeling of Nanoparticles Assisted Enhanced Oil Recovery},
            issn = {21984182},
          author = {Irfan, S. A. and Shafie, A.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093869953&doi=10.1007\%2f978-981-15-8606-4\%5f4&partnerID=40&md5=f9d49e75e6f16db1805039f699a618fb},
        abstract = {The development of artificial neural network and deep learning algorithms in the last decade has provided a crucial development to solve complex mathematical modeling problems. The application of artificial neural network and deep learning algorithms to solve the complex flow problems arises in the reservoir simulation. The reservoir simulation is a complex phenomenon that{\^A} requires understanding of the complex fluid flows phenomenon{\^a}??s and solving nonlinear partial differential equations numerically. Artificial neural network and deep learning algorithm helps to simulate the flow phenomenon by reducing the numerical error and also useful in reducing the approximations in the mathematical model. This chapter deals with current state of the art literature on implementation of artificial neural network and deep learning algorithms in simulation reservoirs for enhanced oil recovery applications. {\^A}{\copyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.}
}