@inproceedings{scholars16840,
         journal = {IOP Conference Series: Earth and Environmental Science},
       publisher = {Institute of Physics},
            year = {2022},
           title = {Application of Machine Learning for Shale Reservoir Permeability Prediction},
             doi = {10.1088/1755-1315/1003/1/012025},
          number = {1},
          volume = {1003},
            note = {cited By 0; Conference of 2nd International Conference on Earth Resources 2020, ICER 2020 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:178997},
            issn = {17551307},
          author = {Prajapati, S. and Padmanabhan, E.},
        abstract = {Due to ultra-low permeability, the characterization of shale reservoir is always being a challenge. The traditional models are insufficient to estimate the ultra-low permeability of shale reservoirs. Based on Machine Learning, we proposed a simple mathematical approach to predict the permeability of shale reservoirs. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction because of their strengths to deliver the variables. Additionally, used the Kozeny's equation with power mean approach to constraint the estimated permeability for more reliable. To do this, we used a pure shale well-log downloaded from open source. The results show that the predicted permeability is well correlated with the neutron log and significantly match with the other well-logs. {\^A}{\copyright} Published under licence by IOP Publishing Ltd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129841723&doi=10.1088\%2f1755-1315\%2f1003\%2f1\%2f012025&partnerID=40&md5=51d19f5650e5763ccfd99b1b9301e454}
}