首页|基于非线性定向降维的k近邻致密砂岩储层含气性预测方法

基于非线性定向降维的k近邻致密砂岩储层含气性预测方法

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本文提出利用全连接人工神经网络(FANN)进行非线性定向降维并结合k近邻方法分类的致密砂岩储层含气性预测方法.k近邻方法能够依据样本间相似性,针对性地选取对应的部分训练样本建立局部模型,但缺乏含气敏感属性的提取能力,并面临"维度灾难"问题.由于样本中的含气性特征虽然是重要特征,但不一定是主要特征.线性降维方法难以准确提取这些特征.我们通过训练一个合理搭建的FANN并输出其中间低维特征实现对训练样本和待预测样本的非线性定向降维.这种做法既能够增加样本的可分性,同时避免了通过样本低维空间中的最大差异实现降维而改变样本固有分布特征的问题.另外,k近邻方法对降维数据进行分类,还等效于用k近邻方法替代FANN中具有线性分类作用的深层结构,有利于白化FANN的黑箱问题.本方法在具体的物理场景中挖掘机器学习算法的物理内涵,提高了智能方法的可解释性.将本方法应用在实际数据中,预测结果显示本方法能够一定程度上挖掘局部波形属性中蕴含的含气敏感信息,实现小范围的致密砂岩储层精确刻画.
Gas-Bearing Reservoir Prediction Using k-nearest neighbor Based on Nonlinear Directional Dimension Reduction
In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish a local model according to feature similarities.However,the kNN method cannot extract gas-sensitive attributes and faces dimension problems.The features important to gas-bearing reservoir prediction could not be the main features of the samples.Thus,linear dimension reduction methods,such as principal component analysis,fail to extract relevant features.We thus implemented dimension reduction using a fully connected artificial neural network(ANN)with proper architecture.This not only increased the separability of the samples but also maintained the samples'inherent distribution characteristics.Moreover,using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN,which is considered to have a linear classification function.When applied to actual data,our method extracted gas-bearing sensitive features from seismic data to a certain extent.The prediction results can characterize gas-bearing reservoirs accurately in a limited scope.

gas bearing predictioninterpretabilityk-nearest neighbornonlinear directional dimension reduction

宋朝辉、桑文镜、袁三一、王尚旭

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中国石油大学(北京),地球物理学院,北京,102249

k近邻方法 致密砂岩储层预测 非线性定向降维 可解释性

国家重点研发计划国家自然科学基金国家自然科学基金Strategic Cooperation Technology Projects of CNPC and CUPB

2018YFA07025044217415241974140ZLZX2020-03

2024

10.1007/s11770-022-0980-0

应用地球物理(英文版)
中国地球物理学会

应用地球物理(英文版)

影响因子：1.01
ISSN：1672-7975
年,卷(期)：2024.21(2)
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