Joint Inversion of Core Porosity and Permeability Based on GeoFE-PPNet

To address the problems of strong vertical heterogeneity in thin interbedded reservoirs of the N block in Daqing Oilfield, the complex coupling between porosity and permeability, and the difficulty of conventional single-parameter inversion methods in balancing local details with global geological background, a joint inversion method for porosity and permeability based on GeoFE-PPNet and logging imaging tensors is proposed. Using conventional logging curves, including GR, RT, RHOB, NPHI, DT, and PE, the method constructs a logging imaging tensor by integrating multi-channel responses with shale constraints and extracts intra-layer textural features through local encoding. Meanwhile, sequence decomposition and frequency enhancement are introduced to capture vertical trend variations and high-frequency non-stationary responses of the reservoir. On this basis, geological constraint fusion and dual-task collaborative prediction are employed to achieve joint inversion of porosity and permeability. Experimental results show that the proposed method achieves favorable inversion accuracy and cross-well generalization under complex reservoir conditions, with a porosity R2 of 0.931, a permeability R2 of 0.887, and an overall accuracy of 90.74%. Ablation and noise robustness experiments further demonstrate the effectiveness of the logging imaging tensor, frequency enhancement, geological constraints, and dual-task collaboration in improving model performance. The study indicates that the proposed method can more accurately characterize the vertical variation in reservoir physical properties and provides a new technical approach for fine reservoir evaluation and intelligent log interpretation.