A Novel Interwell Connectivity Identification Method Based on Segmented Matching of Injection–Production Rate Fluctuations

Accurate interwell connectivity characterization is critical for fine-grained waterflood optimization and reservoir management, often requiring integrated analysis across multiple disciplines and methods. Among these, injection–production response analysis stands as the most cost-effective and widely adopted approach. However, it remains highly subjective, heavily reliant on senior engineers’ decades of accumulated experience, and prohibitively labor-intensive for large-scale oilfields with hundreds of wells. With the exponential growth of production data in modern oilfields, manual analysis has become the bottleneck restricting the timeliness of reservoir management decisions. While signal processing techniques offer a promising path to automation, general-purpose algorithms fail to incorporate fundamental reservoir fluid flow laws, resulting in insufficient accuracy for practical engineering applications. To address this gap, we propose a novel connectivity identification method that mimics expert analysis logic by focusing on large-amplitude fluctuation segments rather than full-curve matching. Using curve slope as the core metric, cosine similarity quantifies trend consistency, while Root Mean Square Error (RMSE) measures amplitude proximity. Three targeted strategies enhance accuracy: key region screening with segmented matching, outlier removal accounting for time-varying lags, and multi-index weighted fusion. Validated on synthetic and mature carbonate waterflood field cases, the method improves the identification performance over benchmark Normalized Cross-Correlation (NCC) and Capacitance-Resistance Model (CRM) methods by more than 12% in both cases. It retains the reliability of traditional response analysis while achieving full automation, and can help estimate the timing of preferential flow path formation, requiring only routine production data to provide valuable reference for timely field development decision-making.