Prediction of residual water saturation using genetically focused neural nets

Mohd Azizi Ibrahim, David K. Potter

    Research output: Contribution to conferencePaper

    2 Citations (Scopus)

    Abstract

    The "genetic petrophysics" approach for predicting petrophysical parameters using genetically focused neural nets (GFNN) has only recently been introduced. The approach only requires a minimum number of core plugs, along with associated wireline log data, from a chosen representative genetic unit (RGU). This case study has successfully developed and tested this new methodology to predict residual water saturation. Combinations of wireline logs and core data from a short 7m RGU interval in a North sea well were used to train the GFNN predictors. These were then applied to predict the residual Sw throughout the whole logged section in the training well and adjacent wells in the same field. Traditional hydraulic unit analysis provided the basis for selecting the minimal training plugs. Only 4 core plugs were finally required to represent the hydraulic units in the RGU and provide good results. This approach is very cost effective in terms of core material and computing time. Presently we have only tested this approach in oil bearing shoreface reservoirs. Thus, it is recommended that this approach be tested in other environments. Copyright 2004, Society of Petroleum Engineers Inc.

    Original languageEnglish
    Pages111-114
    Number of pages4
    DOIs
    Publication statusPublished - 2004
    EventSPE Asia Pacific Oil and Gas Conference and Exhibition - Perth, Australia
    Duration: 18 Oct 200420 Oct 2004

    Conference

    ConferenceSPE Asia Pacific Oil and Gas Conference and Exhibition
    CountryAustralia
    CityPerth
    Period18/10/0420/10/04

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  • Cite this

    Ibrahim, M. A., & Potter, D. K. (2004). Prediction of residual water saturation using genetically focused neural nets. 111-114. Paper presented at SPE Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia. https://doi.org/doi:10.2118/88457-MS