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 characteristics 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 optimization 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.