Evidence item

The study reports classification improvement from 86.95% to 89.28% after embedding the geological prior. MODEL_OUTPUT

Authenticity UNVERIFIED · extraction UNVERIFIED · independence group none (unreviewed) · created seq 17512

Source: An integrated method for permeability prediction and fluid identification in tight sandstone reservoirs using geological-prior-guided attention networks: a case study of the X Block, Chang 8 member, Jiyuan oilfield · locator {} · excerpt hash sha256:5fa95c165897200ae191d5fc6aa8308d41c31a6e64daece20906c887d50e5a93

We then design a well-log-based sweet-spot index (Issp) and embed it as a geological prior through an attention-gating mechanism to form a knowledge-guided model (KG-SE-ResNet18). This knowledge guidance improves reservoir-type classification accuracy from 86.95% to 89.28%.