@book{scholars12998,
             doi = {10.4018/978-1-7998-3645-2.ch012},
            year = {2020},
            note = {cited By 0},
           pages = {294--315},
           title = {Development of DNN model for predicting surge pressure gradient during tripping operations},
         journal = {Handbook of Research on Smart Technology Models for Business and Industry},
       publisher = {IGI Global},
          author = {Krishna, S. and Ridha, S. and Vasant, P.},
            isbn = {9781799836469; 9781799836452},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128045521&doi=10.4018\%2f978-1-7998-3645-2.ch012&partnerID=40&md5=0c714ea79b9ce48d7f94dd5af21e0376},
        abstract = {Application of machine learning tools in drilling hydrocarbon well is still exploratory in its stage. This chapter presents a brief review of various applied research in drilling operations using machine learning (ML) tools and develop a deep neural network (DNN) model for predicting the downhole pressure surges while tripping. Tripping in or out drill-string/casing with a certain speed from the wellbore will result in downhole pressure surges. These surges could result in well integrity or well control problems, which can be avoided if pressure imbalances are predicted before this operation is engaged. Existing analytical models focus on forecasting the pressure imbalance but requires cumbersome numerical analysis. This could be solved by integrating DNN tool with the best existing analytical model predicted dataset. Consequently, the aim of this chapter is to provide an overview of various applications of machine learning tools in drilling and presenting a step-by-step process of developing a DNN model for the prediction of downhole pressure surges during tripping operation. {\^A}{\copyright} 2020, IGI Global.}
}