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A Seismic Multi-Attribute Sandbody Identification Method Based on the LightGBM-RFECV Coupling Algorithm

delete2025-09-01
delete1
PRE
AI
R
Ren, Teng-fei
Z
Zhi-bing Feng *
Y
Ying Zhang
X
Xiang Zhang
李江 封面图
李江 (Li, Jiang)
N
Ning, Yuan-li
J
Jingyi Wang
J
Jian Ding
Q
Qi, Zeng-shuo
DOI：10.1007/s11770-025-1292-ydelete
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摘要

摘要

En 中文
地震属性蕴含了丰富的储层表征信息，能够有效支持储层预测。鉴于砂体与地震属性之间存在高维非线性关系，本研究采用RFECV方法进行地震属性优选，并将优化后的属性输入LightGBM模型以提升砂体识别的空间界定能力。通过基于优化地震属性与测井资料构建训练数据集，并经类别不平衡校正后作为机器学习模型的输入变量，以砂体概率为输出变量，运用网格搜索优化模型参数，成功建立高精度砂体预测模型。以荷兰北海F3区块的3D地震数据为例，该方法成功刻画了目标层段砂岩的三维空间分布。结果表明，即使在强噪声条件下，基于LightGBM的多属性砂体识别方法仍能有效表征砂体的分布特征。与未筛选属性相比，采用筛选属性后的预测结果具有更高的垂向分辨率和井间一致性，单井预测精度达到80.77%，显著提升了砂体边界界定的准确性。
Keyword:
Sandbody identification
Seismic attributes
LightGBM model
RFECV method

期刊

J
Journal of Applied Geophysics
IF:
2.1
论文数:
251
被引数:
8.3K

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