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      Effect of Pc and MW C6- on the accuracy of Gas Condensate Dewpoint Pressure estimation using Neural network modelling

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          Abstract

          Dewpoint pressure (DPP) is one of the most important factors to be evaluated by reservoir engineers while planning the development of a gas condensate reservoir. Below the dewpoint pressure, liquid condenses out of the gaseous phase. This liquid condensate forms a “ring” or “bank” around the producing well in the near-well region. Normally this liquid will not flow until its saturation exceeds the critical condensate saturation (Scc) due to the capillary pressure and relative permeability of the porous medium. Hence it is very essential to accurately predict the dewpoint pressure of the reservoir fluid.Numerous studies have been done on predicting dewpoint pressure using neural networks. All of these studies focus on four key input parameters: Reservoir Temperature, Specific gravity, Compressibility factor and Molecular weight of heavier components (C7+). However, in this study, two multi-layer perception neural networks (MLPNN) were built. In developing the MLPNN models two new input parameters were introduced; Critical pressure and Molecular weight of lighter components (C6-).

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          Author and article information

          Journal
          ScienceOpen Posters
          ScienceOpen
          5 May 2021
          Affiliations
          [1 ] School of Computing Engineering and Digital Technologies, Teesside University
          [2 ] School of Computing Engineering and Digital Technologies, Teesside University, UK
          Article
          10.14293/S2199-1006.1.SOR-.PPYCBU8.v1

          This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

          Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

          Earth & Environmental sciences, Geosciences, Engineering

          Artificial Neural Network, Gas Condensate, Dew point pressure

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