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A Method for Studying the Temperature Dependence of Dynamic Fracture and Fragmentation
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Fracture Conductivity Prediction Based on Machine Learning.

Xiaopeng Wang1,2, Binqi Zhang1,2, Jianbo Du1,2

  • 1State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China.

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|March 25, 2024
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This summary is machine-generated.

This study introduces a machine learning model to predict fracture conductivity, a key factor in hydraulic fracturing for low-permeability reservoirs. The model accurately estimates conductivity, reducing time and labor costs associated with traditional methods.

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence in Reservoir Development

Background:

  • Hydraulic fracturing is crucial for exploiting low-permeability reservoirs.
  • Fracture conductivity is a critical parameter for assessing hydraulic fracturing effectiveness and optimizing designs.
  • Existing methods for calculating fracture conductivity are labor-intensive and time-consuming.

Purpose of the Study:

  • To develop a machine learning-based model for predicting fracture conductivity.
  • To identify the primary factors influencing fracture conductivity.
  • To offer a more efficient alternative to traditional fracture conductivity calculation methods.

Main Methods:

  • Identification of key controlling factors for fracture conductivity using Pearson coefficient and gray correlation analysis.
  • Development of a fracture conductivity prediction model utilizing a Backpropagation (BP) neural network.
  • Optimization of the BP neural network using a genetic algorithm.

Main Results:

  • The machine learning model accurately predicts fracture conductivity.
  • The developed BP neural network model achieved high accuracy, with R² values of 0.981 for block A and 0.975 for block B.
  • Pearson coefficient and gray correlation analysis successfully identified the main controlling factors of fracture conductivity.

Conclusions:

  • Machine learning provides an accurate and efficient method for predicting fracture conductivity.
  • The proposed model significantly reduces the time and labor required for fracture conductivity assessment.
  • This approach offers a valuable tool for optimizing hydraulic fracturing designs in low-permeability reservoirs.