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<p>Compressional sonic logs is one of the important logs for subsurface characterization, reservoir evaluation, and wellbore stability analysis. However, acquiring these logs is often challenging due to logistical constraints. This study explores the application of machine learning (ML) techniques to predict compressional sonic logs using conventional well logs from five wells. The methodology involves data preprocessing, feature selection, and training various regression models, including Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN). Model performance is optimized through hyperparameter tuning and evaluated using correlation coefficients and root mean square error (RMSE) metrics. Results indicate that ensemble models (Random Forest, CatBoost, XGBoost) achieve the highest accuracy, with correlation coefficients ranging from 89 to 89.6% and RMSE between 5.85 and 6.03. Additionally, feature engineering and data cleaning significantly improve model performance, while input scaling is essential for SVM, KNN, and DNN models. Incorporating blind well testing further enhances reliability. This study presents a robust ML-based workflow for predicting compressional sonic logs, offering a cost-effective solution for reservoir management and geomechanical analysis.</p>

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Machine learning model optimization for compressional sonic log prediction using well logs in Shahd SE field, Western Desert, Egypt

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月29日

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  • Khaled Saleh,
  • Walid M. Mabrouk,
  • Ahmed Metwally

摘要

Compressional sonic logs is one of the important logs for subsurface characterization, reservoir evaluation, and wellbore stability analysis. However, acquiring these logs is often challenging due to logistical constraints. This study explores the application of machine learning (ML) techniques to predict compressional sonic logs using conventional well logs from five wells. The methodology involves data preprocessing, feature selection, and training various regression models, including Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN). Model performance is optimized through hyperparameter tuning and evaluated using correlation coefficients and root mean square error (RMSE) metrics. Results indicate that ensemble models (Random Forest, CatBoost, XGBoost) achieve the highest accuracy, with correlation coefficients ranging from 89 to 89.6% and RMSE between 5.85 and 6.03. Additionally, feature engineering and data cleaning significantly improve model performance, while input scaling is essential for SVM, KNN, and DNN models. Incorporating blind well testing further enhances reliability. This study presents a robust ML-based workflow for predicting compressional sonic logs, offering a cost-effective solution for reservoir management and geomechanical analysis.