Reservoir Flow Field Characterization during Water Flooding: A Data Mining and Fuzzy Logic Approach

Accurate characterization of reservoir flow fields is critical for enhancing oil recovery in water flooding oil fields. Current methodologies lack standardized criteria for selecting influential factors in flow field characterization. This study introduces a novel quantitative framework integrating data mining and fuzzy logic to address this gap. Principal component analysis (PCA) and Spearman's rank correlation coefficient are applied to identify key indices, forming a robust flow field characterization system. A dual subjective-objective weighting strategy, combining the analytic hierarchy process (AHP) with entropy weighting, determines comprehensive index weights. Statistical analysis of data distributionincluding quartile deviation, variance, and standard deviation, guides the selection of optimal membership functions for each index. Fuzzy logic is then employed to derive a quantitative characterization formula, while K-means clustering categorizes flow field types. Results indicate that the PCA and Spearman correlation coefficient effectively isolate critical factors: oil surface flux, pressure, oil saturation, and permeability. The weighting strategy assigns greater importance to dynamic factors (0.833) than to static factors (0.167). Membership functions align with observed data distributions, and K-means clustering achieves a silhouette coefficient of 0.724, delineating three distinct flow field types: dominant (10.99%), weak flow field type I (52.71%), and weak flow field type II (36.29%). This data-driven framework provides actionable insights for optimizing production strategies and improving oil recovery in mature, high water-cut oil reservoirs.