Fluid migration screening requires the integration of seismic structure and well log response. This study develops a multimodal framework for ranking an attribute-derived fluid migration favorability proxy from paired local seismic patches and well log windows in the F3 survey. The framework couples frozen seismic foundation model (SFM) features with a trainable well log token encoder and evaluates pooled concatenation, cross attention, and learned query fusion for multimodal integration. Under sample-stratified evaluation across three random seeds, frozen SFM features with learned query fusion achieved the highest observed normalized discounted cumulative gain at rank 20 (nDCG@20) of 85.15%, exceeding the best single-modality baseline by 3.95 percentage points. The results support combining pretrained seismic representations with well log evidence to prioritize intervals in the F3 benchmark.