Estimation of carbonates permeability using pore network parameters extracted from thin section images and comparison with experimental data

Rabbani, A ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jngse.2017.02.045
  3. Publisher: Elsevier B.V , 2017
  4. Abstract:
  5. Petrography and image analysis have been widely used to identify and quantify porous characteristics in carbonate reservoirs. This paper uses the thin section images of 200 carbonate rock samples to predict the absolute permeability using intelligent and empirical methods. For each thin section, several pore network parameters are extracted from thin section images of rocks including the average pore size, average throat size, average throat length and average 2-D coordination number of pore network. A neural-based model successfully predicts the permeability of samples using pore network parameters as the inputs. Second neural network is applied for predicting absolute permeability considering lithology, pore type and fabric of the rock samples. Finally, an empirical formula containing porosity and average coordination number as inputs is proposed to predict the permeability. It has been found that the porosity and coordination number can directly describe the permeability of carbonates while pore and throat sizes extracted from a single 2-D cross section of rock cannot explain the permeability of carbonates very well. The results of this study indicate the better performance of pore network extraction method compared to the simple regression analysis for prediction of the permeability. © 2017 Elsevier B.V
  6. Keywords:
  7. Carbonate rocks ; Pore network modeling ; Thin section images ; Carbonates ; Carbonation ; Forecasting ; Lithology ; Mechanical permeability ; Petrography ; Pore size ; Porosity ; Regression analysis ; Rocks ; Sedimentary rocks ; Absolute permeability ; Average coordination number ; Carbonate rock ; Coordination number ; Pore-network modeling ; Porous characteristics ; Simple regression analysis ; Thin section ; Image processing
  8. Source: Journal of Natural Gas Science and Engineering ; Volume 42 , 2017 , Pages 85-98 ; 18755100 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1875510017301075