Multi-Attribute Clustering for Volcanic Facies Analysis: A Case Study in Block A12, Songliao Basin, China

Abstract

Volcanic reservoirs exhibit strong lithological heterogeneity and complex seismic responses, making lithofacies prediction between wells challenging, particularly in the Yingcheng Formation of the Songliao Basin. To address this issue, this study proposes an integrated workflow for volcanic lithofacies prediction based on Principal Component Analysis (PCA)-optimized multi-attribute seismic clustering. Seismic facies are first identified from reflection configuration and external geometry, and three types—chaotic, layered, and shield-like facies—are established and calibrated using well logs and core data. PCA is then applied to reduce attribute redundancy and optimize the attribute set. The selected attributes, including root mean square (RMS) amplitude, energy half-life, and gradient magnitude, are used for multi-attribute clustering. The results indicate that eruption facies dominate the study area and correspond to favorable reservoirs, accounting for the majority of high-quality reservoir zones. In contrast, overflow and volcanic sedimentary facies show comparatively lower reservoir potential. The predicted lithofacies distribution shows strong spatial consistency with well observations, demonstrating the method’s reliability. Overall, the proposed workflow improves lithofacies prediction accuracy and provides an effective tool for reservoir characterization and well deployment in complex volcanic settings.