Effect of slickwater-alternate-slurry injection on proppant transport at field scales: A hybrid approach combining experiments and deep learning

Proppant transport in underground fractures plays a key role mitigating sand screen-out and enhancing the stimulated production for hydraulic fracturing. The effects of field pumping schedules, however, are not fully studied. We investigate effect slickwater-alternate-slurry injection on proppant at field-practical scales. A new hybrid approach is proposed to directly connect experimental studies with operations, which consists observation experiments, calculations, deep learning (DL) workflow. experiments reveal that alternate induces unexpected ridge. modified calculations (considering ridge height) extract features training DL algorithm. workflow predicts downhole pressure (mainly governed by injection) error analyses. Approximately 20.2% eliminated considering ridge, thus demonstrating its injection. predictions significantly improved early late periods fracturing operations when fracture initially created highly filled. sensitivity analysis suggests pump rate may dominate height compared other parameters. study complements mechanisms transport, essential controlling boosting