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      Analyzing and evaluation of vintage logs for an integrated reservoir characterization and field development strategy

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      Petophysics, Techlog, Field development strategy, Neural network, Vintage logs, Niger delta, Reservoir characterisation

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          Using neural network to train new set of logs for full reservoir characterisation


          Obom field is a mature field in the Greater Ughelli onshore Niger delta, which has been producing since 1967. The field is a simple rollover structure elongated in the east-west direction, and bounded to the north by an east-west trending, south-hading, main growth fault. The reservoirs are made mainly of channel/shoreface complexes. The closures are faults assisted dip closures in shallow reservoir and dip assisted fault closure in deeper sections. As a huge producing field with some potential for further sustainable production, field monitoring is therefore important in the identification of areas of unproduced hydrocarbon. The aim of this study is to evaluate and train logs which will be an input into other discipline for an integrated field development study. Petrophysical parameters were evaluated from logs and plots of shaly sand saturation equations (Waxman smith and Normalized Qv method) were compared to water saturation from drainage capillary pressure and a good match was observed. Due to some radioactive reservoir levels without density and neutron logs, volume of shale was evaluated from both gamma ray (GR) and spontaneous potential (SP) log which was later spliced with data editor to give a final volume of shale . Furthermore, paucity of density logs drove the decision to use neural network for density log training from SP logs- using density SP logs would capture the radioactive level - and TVDSS which went into Seismic to well tie for horizon interpretation. With the aid of python scripting, the flow zone indicator (FZI) workflow was used in evaluating the permeability and hydrocarbon correction on porosity was also done. The use of python scripting saved computing time by more than 70% due to the numbers of wells in the field - fourteen wells. This study demonstrates the effectiveness of integrating trained dataset for a field development study. Hence, has provided a framework for future prediction of reservoir performance and production behavior of the field.

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          Author and article information

          ScienceOpen Posters
          4 March 2020
          [1 ] University of Benin

          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 .

          The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


          Niger delta, Vintage logs, Neural network, Reservoir characterisation, Field development strategy, Techlog, Petophysics


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