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Determination of the Appropriate Geometric Structure of Porous Media by Image Processing Methods and Evolutionary Algorithms

Nejad Ebrahimi, Mohammad Ali | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42520 (06)
  4. University: Sharif University of Technology Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Bozorgmehry Boozarjomehry, Ramin; Jamshidi, Saeed
  7. Abstract:
  8. A genetic-based pore network extraction method from micro-CT images is proposed in this work. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two methods are presented for modeling the pore network structure. Unlike the previous models, these methods are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the network. In the first method, only the difference between the real micro-CT images of the porous medium and the sliced images from the generated network is selected as the objective function. A genetic algorithm is used as an optimization tool. A second optimization has been used for achieving some improvements; in addition to the difference between the real porous medium and the generated network, the contrast between the experimental and the predicted values of the network permeability is used as the objective criterion. Proposed models are developed for two different rock samples including Clashach sandstone and Indiana limestone as case studies. Petrophysical properties such as porosity and permeability obtained from the optimally-generated network are compared with the experimental values and other available models. Results show that the proposed methods provide a suitable match to the experimental data; it then does a better prediction than the pore network models generated by other approaches
  9. Keywords:
  10. Image Processing ; Genetic Algorithm ; Porous Media ; Pore Network Model

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