基于XGBoost算法的走滑断裂内部特征带的精细识别

赵军; 汪峻宇; 赖强; 文晓峰; 邬光辉; 焦世祥

doi:10.19509/j.cnki.dzkq.tb20230583

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基于XGBoost算法的走滑断裂内部特征带的精细识别

更新时间：2026-11-05

- 基于XGBoost算法的走滑断裂内部特征带的精细识别
- Bulletin of Geological Science and Technology Vol. 44, Issue 2, Pages: 182-192(2025)
- 作者机构：
  
  1. 西南石油大学地球科学与技术学院
  2. 中国石油西南油气田分公司勘探开发研究院
  3. 中国石油集团测井有限公司
- 作者简介：
- 基金信息：
- DOI：10.19509/j.cnki.dzkq.tb20230583
  CLC： P631.81;P618.13
- Online First：15 April 2024，
  
  Published：2025
- 稿件说明：
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赵军, 汪峻宇, 赖强, et al. 基于XGBoost算法的走滑断裂内部特征带的精细识别[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 182-192.
DOI：

赵军, 汪峻宇, 赖强, et al. 基于XGBoost算法的走滑断裂内部特征带的精细识别[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 182-192. DOI： 10.19509/j.cnki.dzkq.tb20230583.

摘要

受制于走滑断裂内部强烈的非均质性、储集类型多样及流体分布复杂的影响，走滑断裂内部裂缝带、破碎带和溶蚀带之间的测井响应复杂多变，为有效利用成像及常规测井资料识别走滑断裂内部3特征带造成了困难。引入XGboost算法建立模型，以提高对走滑断裂内部3特征带的识别精度。分析了走滑断裂内部3特征带的测井响应特征，优选敏感测井曲线构建基于均值及方差的特征向量空间集，采用极端梯度提升算法，建立了走滑断裂溶蚀带、破碎带和裂缝带的XGBoost回归预测模型，并通过多分类评价指标对XGBoost模型的关键参数进行调优，提高了走滑断裂内部特征带的识别精度。利用构建的XGBoost模型对研究区走滑断裂内部特征带进行了识别，其中总样本数234个，识别正确样本208个，识别正确率达88.89%；预测结果表明在走滑断裂内部特征带中，裂缝带分布范围最广，破碎带其次，溶蚀带最窄，这与实际走滑断裂内部特征带的分布范围相符。基于XGBoost算法的走滑断裂内部特征带识别模型能够有效地识别裂缝带、破碎带和溶蚀带，从而有助于对走滑断裂内部尺度更小的溶蚀孔洞及裂缝储集空间的分布进行更为有效的分析，对走滑断裂内部结构的精细刻画有一定借鉴意义。

Abstract

Because of the strong heterogeneity of fault zones

diverse reservoir types

and complex fluid distributions

the logging responses between damage

fault breccias

and dissolution zones within strike-slip faults are complex and variable

making it difficult to identify the three characteristic zones effectively inside strike-slip faults using imaging and conventional logging data. ObjectiveThe extreme gradient boosting (XGBoost) algorithm is introduced to establish a model to improve the identification accuracy of the three characteristic zones within strike-slip faults. MethodsThe logging response characteristics of the three characteristic zones within strike-slip faults are analyzed

and the sensitive logging curves are selected to construct a feature vector space set based on the mean and variance. The XGBoost algorithm is applied to establish XGBoost regression prediction models for the dissolution

breccias

and damage zones of strike-slip faults. The key parameters of the XGBoost model are optimized through multiclass evaluation indicators to improve the identification accuracy of the characteristic zones within strike-slip faults. ResultsThe constructed XGBoost model was used to identify the internal characteristic zones of strike-slip faults in the study area

with a total of 234 samples; 208 samples were correctly identified

resulting in an identification accuracy of 88.89%. The prediction results reveal that

within the internal characteristic zones of strike-slip faults

the damage zone has the widest distribution

followed by the breccias zone

and the dissolution zone is the narrowest

which is consistent with the actual distribution of the internal characteristic zones of strike-slip faults. ConclusionThe identification model of internal characteristic zones within strike-slip faults based on the XGBoost algorithm can be used to effectively identify the damage

breccias

and dissolution zones

thereby supporting more effective analysis of the distribution of small-scale dissolution cavities and fracture reservoir spaces inside strike-slip faults

and providing reference information for the accurate characterization of the internal structure of strike-slip faults.

关键词

Keywords

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