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<p>This study addresses numerous issues in traditional fracturing sweet spot identification and segment cluster design by proposing a method based on big data analysis and intelligent fusion algorithms. First, based on the key influencing factors for sweet spot identification, a combined big data analysis method using K-Means and KNN is employed to perform clustering analysis, aiming to achieve the goal of identifying fracturing sweet spots. Secondly, an algorithm integrating expert experience, Self-Organizing Map (SOM) clustering, K-Means, and KNN is introduced to carry out segment cluster design, thereby realizing the objective of intelligent segment cluster design based on the comprehensive sweet spot index.The research demonstrates that in the sweet spot identification process, there are many influencing factors. The combination of K-Means and KNN for big data analysis can accurately identify geological sweet spots, engineering sweet spots, and the comprehensive sweet spot index. In the segment cluster design process, the expert experience model first integrates geological and engineering data from the block, forming design schemes through inductive analysis. Intelligent algorithms centered around SOM, K-Means, and KNN are capable of deeply analyzing geological and engineering data, achieving the goals of stratification and clustering.</p>

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Research on Fracturing Sweet Spot and Cluster Design Based on Big Data Analysis and Intelligent Fusion Algorithms

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Dejun Zhang,
  • Wenchen Zhang,
  • Abidan Sitiwalidi,
  • Mingyan Liu,
  • Yongjie Liu

摘要

This study addresses numerous issues in traditional fracturing sweet spot identification and segment cluster design by proposing a method based on big data analysis and intelligent fusion algorithms. First, based on the key influencing factors for sweet spot identification, a combined big data analysis method using K-Means and KNN is employed to perform clustering analysis, aiming to achieve the goal of identifying fracturing sweet spots. Secondly, an algorithm integrating expert experience, Self-Organizing Map (SOM) clustering, K-Means, and KNN is introduced to carry out segment cluster design, thereby realizing the objective of intelligent segment cluster design based on the comprehensive sweet spot index.The research demonstrates that in the sweet spot identification process, there are many influencing factors. The combination of K-Means and KNN for big data analysis can accurately identify geological sweet spots, engineering sweet spots, and the comprehensive sweet spot index. In the segment cluster design process, the expert experience model first integrates geological and engineering data from the block, forming design schemes through inductive analysis. Intelligent algorithms centered around SOM, K-Means, and KNN are capable of deeply analyzing geological and engineering data, achieving the goals of stratification and clustering.