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.

Cross-well acoustic-impedance reconstruction combines seismic observations with retained auxiliary logs whose relationships change between wells. We introduce Multimodal Interaction and Residual Fusion (MIRF), which expresses multimodal fusion as an additive correction to a source-supervised seismic estimate. Three residual heads propose updates, a conditional router determines their composition, and an admission coefficient controls the applied magnitude. Separate anchor supervision retains a seismic reference for examining the effect of auxiliary-log correction. Using the original processed-log protocol on Penobscot L-30 to B-41 transfer, ten matched seeds reduce mean root mean squared error (RMSE) from 1905.5 to 1489.6 $\mathrm{kRayl}$ and mean absolute error from 1659.5 to 1178.4 $\mathrm{kRayl}$ relative to the standalone seismic CNN. MIRF also has the lowest mean RMSE among four matched neural fusion alternatives in this direction. Bidirectional comparisons, ablations, and depth-resolved profiles locate the benefits in reduced broad reconstruction bias and show how component effects change with transfer direction.