Reliable wellbore cleaning remains difficult in deviated, horizontal, extended-reach, deep, and ultra-deep wells because the downhole distribution and mechanical state of cuttings beds cannot usually be observed directly. This review examines cuttings-bed dynamics, multiscale modeling, compressible multiphase constraints, and field-validation pathways for drill cuttings transport and wellbore cleaning. Bibliometric mapping of 625 Web of Science records was combined with critical assessment of 204 technically screened studies and 56 engineering-oriented OnePetro records. Bed height, cuttings concentration, pressure response, equivalent circulating density/bottomhole-pressure (ECD/BHP) margin, solids residence time, and packoff tendency are identified as bridge variables linking particle-scale behavior with operational risk. Recent studies strengthen wet-bed erosion and friction characterization, non-spherical and geometry-resolved CFD–DEM, hybrid prediction, compressible pressure–solids coupling, and field observability. Study-level comparison shows that ML approaches differ markedly in data provenance, validation design, physical integration, uncertainty reporting, and transfer evidence. An uncertainty-aware, field-calibratable workflow is proposed that links synchronized measurements, complementary models, latent-state estimates with prediction intervals, section-specific probabilistic thresholds, operational response, and post-action verification. Quantitative benchmark criteria are defined for particle realism, wet-bed mechanics, tool-induced flow, compressible transport, transient field models, and advisory outputs.
Li et al. (Thu,) studied this question.
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