储层物性参数是表征储层储集与渗滤流体能力的重要参数,测井解释是获取物性参数的重要途径,是一个复杂非线性回归任务.针对已有物性参数测井解释方法在小样本学习条件下的泛化能力不足这一问题,本文首先提出了基于聚类分析的样本优选方法,通过K均值聚类划分样本的空间结构,根据样本在空间结构中的分布优选学习样本,从而最大化学习样本的多样性;然后提出了基于层次化残差神经网络的物性参数测井解释方法.方法在全连接神经网络基础上引入 4 种机制:(1)引入残差连接学习输入与输出间的残差映射,深化小样本的复杂物性特征提取过程;(2)引入集成学习整合多种不同机器学习方法,通过算法多样性降低过拟合风险;(3)引入多任务学习联系起孔隙度解释和渗透率解释这两个任务,以提高小数据情况下单一任务的泛化性;(4)引入二次加权均方根误差损失函数,降低高渗储层的物性解释误差.在实际研究区中设计的 90 组对照实验的分析结果表明,基于聚类分析的样本优选方法能够有效提升多种机器学习模型在小样本条件下的泛化能力;基于本文提出的层次化残差神经网络进行研究区孔隙度与渗透率测井解释,解释结果的决定系数分别达 88%、94%.与已有的多种方法相比,本文方法基于分布特征的样本选择及多任务协同等方式的算法优化有效提高了岩石物理数据的特征表征,方法的物性解释精度更高、泛化能力更强,在取心盲井上的精度分别领先 12和20个百分点.

Reservoir physical parameters serve as fundamental quantitative indices for characterizing the storage capacity and fluid percolation potential of subsurface reservoirs.Well logging interpretation,a critical methodology for accurately estimating these parameters,constitutes a sophisticated nonlinear regression challenge.To address the inherent limitations of existing petrophysical parameter interpretation techniques,particularly their inadequate generalization performance under few-shot learning conditions,this investigation systematically devises a dual-framework analytical approach.This study initially proposes a sample optimization methodology based on cluster analysis.The spatial configuration of samples is partitioned through the implementation of the K-means clustering algorithm,followed by selective sample curation according to spatial distribution char-acteristics to maximize learning sample diversity.Building upon this optimized sample architecture,the study further introduces a hierarchical residual neural network-based interpretation framework for petrophysical parameter estimation.The proposed methodology enhances conventional fully connected neural architecture through four innovative mechanisms:(1)Integration of cross-layer residual connections facilitates progressive refinement of residual mappings between multivariate logging inputs and target petrophysical outputs,thereby enabling hierarchical abstraction of complex petrophysical relationships from limited training instances.(2)The integration of ensemble learning paradigms amalgamates diverse machine learning methodologies,effectively mitigating overfitting risks through algorithmic diversity.(3)The implementation of a multi-task learning framework establishes intrinsic correlations between porosity and permeability interpretation tasks via shared latent representations,thereby enhancing individual task generalizability under data scarcity constraints.(4)The introduction of a quadratically weighted root mean square error loss function preferentially reduces interpretation errors in high-permeability reservoir intervals.Results from 90 rigorously designed comparative experimental configurations in the study area demonstrate that the cluster-based sample opti-mization methodology effectively enhances generalization performance across multiple machine learning models under few-shot learning constraints.Application of the proposed hierarchical residual neural network framework for well-logging interpretation of reservoir porosity and permeability within the investigated reservoir area achieves coefficients of determination of 88%and 94%,respectively,demonstrating statistically significant superiority over conventional methodologies in both petrophysical interpretation accuracy and generalization capability.Blind testing validation on cored wells reveals 12 and 20 percentage point improvements in predictive precision compared to other various existing methodologies,the proposed approach in this study demonstrates substantial advancements in addressing few-shot learning challenges through algorithm optimization strategies encompassing distribution-based sample selection and multi-task collaborative frameworks.This methodology significantly enhances feature representation fidelity in petrophysical datasets,exhibiting superior petrophysical interpretation accuracy and enhanced generalization capabilities.