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Logarithmic Differentiation01:28

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Few-Shot Learning Method for Continuous Prediction of Rock Mechanical Parameters Based on Logging Data.

Weiguang Zhao1,2, Shuxun Sang1,3,4, Sijie Han3,4

  • 1School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China.

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Summary

This study introduces a new method using geophysical logging data to continuously predict rock mechanics parameters, overcoming limitations of scarce rock samples for detailed geological characterization.

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Area of Science:

  • Geology
  • Geophysics
  • Rock Mechanics

Background:

  • Stratigraphic sequences exhibit significant vertical variations in lithological and physical properties.
  • Continuous rock mechanics parameters are crucial for detailed stratigraphic characterization, especially in reservoir layers.
  • Current methods struggle to obtain complete stratum rock mechanics parameters due to limited rock samples.

Purpose of the Study:

  • To propose a novel method for continuously predicting rock mechanics parameters in geological sequences using geophysical logging data.
  • To address challenges of sample scarcity and global geological feature extraction in continuous parameter prediction.
  • To enable continuous extraction of stratigraphic features, sample generation, and mechanical parameter prediction.

Main Methods:

  • Development of a method utilizing geophysical logging data for continuous rock mechanics parameter prediction.
  • Implementation of an autoencoder architecture for stratigraphic feature extraction.
  • Utilization of a generator for creating synthetic rock mechanical samples.
  • Training a prediction model incorporating stratigraphic feature extraction capabilities.

Main Results:

  • The stratigraphic feature extractor demonstrated global stratigraphic perception, accurately identifying lithological transitions and anomalies.
  • Generated synthetic rock mechanical samples matched the characteristics of genuine samples.
  • The proposed model significantly outperformed traditional models, achieving high R² values (0.91) and low errors for friction angle and cohesion.
  • The model provided more reasonable predictions for Longtan Formation rock mechanics parameters compared to traditional approaches.

Conclusions:

  • The developed methodology effectively overcomes the limitations of insufficient samples for continuous geological parameter prediction.
  • The approach offers a robust framework for detailed stratigraphic characterization and rock mechanics analysis.
  • This method enhances the accuracy and reliability of predicting rock mechanical parameters in geological formations.