1887

Abstract

Summary

The glaciofluvial sandstones of the Sarah Formation in the Jalamid area of northwestern Saudi Arabia were deposited in subaqueous turbiditic fans or channelized incised valley fills, which gave rise to significant lateral and vertical facies variations. Through integration of borehole images, conventional wireline logs and core data, a reliable robust stratigraphic framework was established enabling detailed examination of successive reservoir architectures.

Eleven cored wells were used to calibrate textural and estimated lithology grainsizes derived from image-based petrophysics analysis of microresistivity borehole images. Outside of the cored intervals, wireline logs were used to generate “gross” lithologies. Integration of palaeoflow data further refined identification of “linked” depositional packages to the identified high-resolution electrofacies.

The core-directed electrofacies were subsequently analyzed in terms of their relative porosity difference and hydraulic flow units to better define the reservoir architecture in terms of potential reservoir capacity and flow character.

The workflow developed allows the distinction and correlation of promising sand bodies to improve both pay sand definition and spatial modeling to optimize field exploration, realistic production prediction and reservoir performance.

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/content/papers/10.3997/2214-4609.201702380
2017-10-09
2024-04-16
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References

  1. Burmester, G., Alghannam, A. and MacPherson, K
    [2014] A New Approach Combining Image-based Petrophysics and Neural Network Analysis to Build Electrofacies in the ’Unayzah. 76th EAGE Conference & Exhibition, Amsterdam, Extended Abstract.
    [Google Scholar]
  2. Craigie, N., Rees, A. and MacPherson, K
    [2016] Chemostratigraphy of the Sarah Formation, Northern Saudi Arabia: an integrated approach to reservoir correlation. GEO 2016: 12th Middle East Geosciences Conference & Exhibition, Bahrain.
    [Google Scholar]
  3. DeshenenkovI., MacPherson, K. and Burmester, G.
    [2015] The Rock Physics Analysis of Tight Sandstones Grain Size Classes with Image Based Petrophysics and Neural Net Modeling. 77th EAGE Conference & Exhibition, Madrid, Extended Abstract.
    [Google Scholar]
  4. Tiab, D and DonaldsonE.C.
    [2004] Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties. Gulf professional publishing, Oxford, U.K.
    [Google Scholar]
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