Stochastic reservoir modelling for prospect mapping: a case study of ''bright'' field, Niger delta

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Deterministic reservoir modeling using geostatistical approach is inherently ambiguous because of the uncertainties contained in the generated reservoir models. Stochastic reservoir modelling using sequential gaussian simulation algorithm can resolve this problem by generating various realizations of petrophysical property models in order to map this uncertainties caused by subsurface heterogeneity. Suites of well logs for four wells with seismic data in SEG-Y format were used for this analysis. The wells were correlated and a reservoir was mapped across them in other to map their lateral extent, synthetic seismogram was generated in other to match the event on the seismic with that of the synthetic after carrying out a shift of -12ms. Seismic to well tie was done to ensure that the horizons were mapped accurately. The structural maps generated and the wells were input that goes into the stochastic modelling process. Five realizations each of facies(lithology), effective porosity, total porosity, net to gross, volume of shale and one realization for permeability and water saturation were generated. The facies models showed the distribution of sand and shale with sand at the existing well locations and the effective porosity, total porosity, net to gross, volume of shale models showed excellent values around the well location. Permeability and water saturation models showed that the existing wells were drilled at the flank of the anticlinal structure. Two drillable points (prospects) were proposed by considering all the initial petrophysical property models and the parameters of the two points named P1 and P2 showed that they contain hydrocarbon in commercial quantity. Stochastic reservoir modelling has proved effective in mapping uncertainties and detecting bypassed hydrocarbons.

     

     


  • Keywords


    Sequential Gaussian Simulation; Modeling; Geostatistics.

  • References


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Article ID: 30846
 
DOI: 10.14419/ijag.v8i1.30846




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