Enhancing drilling efficiency and sustainability through advanced cuttings transport: Fundamentals, modeling, and emerging technologies

Efficient cuttings transport is a critical determinant of drilling performance, directly influencing non-productive time (NPT), energy efficiency, and environmental footprint, particularly as modern wells increasingly involve deviated, horizontal, and high-pressure and high-temperature (HPHT) conditions. Unlike existing review studies, which largely provide descriptive overviews without quantitative benchmarking or integrated assessment of HPHT fluid behavior, sensing limitations, and machine learning (ML) applicability, this review delivers a unified and quantitative synthesis that critically compares empirical, mechanistic, computational fluid dynamics (CFD), and ML approaches across laboratory, numerical, and field contexts. More than two decades of experimental, numerical, and field evidence are synthesized to quantify the influence of fluid rheology, pipe rotation, flow regime, inclination, and wellbore geometry on transport efficiency. Reported results indicate that maintaining annular velocities in the range of 0.6–3.0 m/s and applying pipe rotation above 60–90 rpm can reduce cuttings-bed thickness by approximately 30–45% in horizontal wells. In addition, nanoparticle-enhanced and viscoelastic drilling fluids demonstrate 10–25% improvements in suspension capacity and slip-velocity reduction under HPHT conditions compared with conventional systems. A critical evaluation of empirical correlations, mechanistic formulations, CFD simulations, and ML-based models reveals persistent limitations, including inadequate representation of non-Newtonian and temperature-dependent rheology, limited validation for transient flow and eccentric annular geometries, and poor generalizability of ML models under sparse, noisy, or out-of-distribution downhole data. Key field challenges such as stuck pipe, barite sag, pack-off, and inconsistent hole cleaning are examined in conjunction with mitigation and optimization strategies involving hydraulic parameter tuning, mechanical agitation tools, and real-time monitoring. The review further assesses emerging technologies, including advanced downhole sensing, automated control strategies, digital twins, and environmentally responsible drilling-fluid systems, and proposes a prioritized and actionable research roadmap focusing on the development of HPHT-capable cuttings-concentration sensors, physics-informed ML surrogate models for real-time decision support, scalable multiphysics CFD frameworks that resolve particle-size distributions and eccentric geometries, and sustainable fluid formulations aligned with modern environmental constraints. By integrating transport physics with data analytics, sensing, and automation, this review establishes a quantitative benchmark and decision-oriented framework for advancing next-generation hole-cleaning performance and drilling efficiency.