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Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.

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This study optimizes reservoir facies modeling using a Deep Forward Neural Network (DFNN) combined with multi-point geostatistics (MPG). The enhanced method improves the simulation of complex sedimentary microfacies structures for better reservoir characterization.

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Area of Science:

  • Geosciences
  • Petroleum Engineering
  • Computational Geology

Background:

  • Reservoir facies modeling is crucial for understanding sedimentary characteristics.
  • Conventional methods struggle with complex microfacies structures.
  • Multi-point geostatistics (MPG) and deep neural networks (DNNs) offer advanced capabilities.

Purpose of the Study:

  • To optimize the multi-point geostatistical reservoir facies modeling algorithm.
  • To integrate Deep Forward Neural Network (DFNN) with MPG for improved accuracy.
  • To enhance the characterization of complex sedimentary microfacies.

Main Methods:

  • Developed an optimized multi-point geostatistical reservoir facies modeling algorithm.
  • Utilized a Deep Forward Neural Network (DFNN) to learn geological models and non-linear relationships.
  • Optimized multi-grid training data organization and repeated simulation of grid nodes.

Main Results:

  • The optimized DFNN-MPG algorithm effectively simulates complex sedimentary microfacies.
  • Optimized data organization and node simulation yield results closer to the real target.
  • The method successfully simulates microfacies models of varying scales and sedimentary types.

Conclusions:

  • Combining DFNN with MPG significantly enhances reservoir facies modeling.
  • The optimized approach provides more accurate and realistic simulations of sedimentary microfacies.
  • This method offers a powerful tool for detailed reservoir characterization.