@article{scholars17026,
          number = {5},
          volume = {15},
            note = {cited By 1},
             doi = {10.3390/en15051725},
           title = {Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines},
            year = {2022},
         journal = {Energies},
       publisher = {MDPI},
        abstract = {Wax deposition and gelation of waxy crude oil in production pipelines are detrimental to crude oil transportation from offshore fields. A waxy crude oil forms intra-gel voids in pipelines under cooling mode, particularly below the pour point temperature. Consequently, intrusion of non-reacting gas into production pipelines has become a promising method to lessen the restart pressure required and clear the clogged gel. A trial-and-error method is currently employed to determine the required restart pressure and restart time in response to injected gas volume. However, this method is not always accurate and requires expert knowledge. In this study, predictive models based on an Artificial Neural Network (ANN) and multilinear regression are developed to predict restart pressure and time as a function of seabed temperature and non-reacting gas injected volume. The models{\^a}?? outcomes are compared against experimental results available from the literature. The empirical models predicted the response variables with an absolute error of below 5 compared to the experimental studies. Thus, such models would allow accurate estimation of restart pressure, thereby improving transportation efficiency in offshore fields. {\^A}{\copyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
        keywords = {Crude oil; Gelation; Offshore oil well production; Offshore technology; Petroleum transportation; Pipelines; Regression analysis, Artificial neural network modeling; Multilinear regression; Offshore fields; Production pipelines; Reacting gas; Regression modelling; Restart pressure; Restart time; Wax deposition; Waxy Crude Oil, Neural networks},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125167423&doi=10.3390\%2fen15051725&partnerID=40&md5=e0cbe30151dd94152326d9d7205e4408},
            issn = {19961073},
          author = {Chala, G. T. and Negash, B. M.}
}