A landing string is directly exposed to seawater and subjected to significant stresses and complex deformations due to environmental loads such as wind, waves, and ocean currents during the phase in which the drill string carries the casing to the wellhead. Meanwhile, as the water depth increases, the weight of the drill string increases, leading to an increase in the tensile loads borne by the drill string, which can easily cause a risk of failure. Therefore, a quasi-static load calculation model for the deepwater insertion of the pipe column was established. Using the Ansys platform, simulations were conducted for average wind, wave, and ocean current conditions during different months throughout the year. The ultimate loads and stress distributions of the string were derived from theoretical analyses and numerical simulations for different operational sea states, and the suggested safe operating window and desired BOP trolley restraining reaction force for landing strings’ lowering are given according to the existing industry standards. The research findings can help in identifying the potential risks and failure modes of the deepwater landing string under different working conditions.

In deepwater riserless drilling, inefficient cuttings transport remains a critical challenge, leading to incidents such as stuck pipes and lost circulation. While existing studies have primarily focused on liquid–solid two-phase flow, they largely overlook the nonlinear coupling effects of bubble dynamics (coalescence and breakup) in gas–liquid–solid three-phase systems, resulting in poor predictive accuracy under high-pressure, high-viscosity conditions. This study introduces a systematically integrated framework that presents a novel integration of visualization experiments, multiscale numerical simulation (CFD-DEM coupled with the population balance model, PBM), and machine learning. This methodology provides an in-depth understanding of the bubble coalescence and breakup mechanisms in cuttings bed formation and transport efficiency under riserless drilling conditions. The coupled CFD-DEM-PBM model provides high-fidelity quantification of bubble dynamics and its cross-scale interaction with cuttings. Subsequently, high-resolution, experimentally validated data are utilized to train both backpropagation (BP) and radial basis function (RBF) neural networks to achieve physics-informed, intelligent prediction of cuttings concentration and transport rate. The results demonstrate that bubble coalescence reorganizes local flow fields to suppress sedimentation, whereas bubble breakup induces microscale turbulence that promotes particle resuspension; their synergy critically governs the overall transport efficiency. Notably, the RBF neural network achieved superior predictive accuracy (R ² = 0.91182), significantly outperforming both the BP network and traditional empirical models. This work not only offers a reliable basis for real-time parameter optimization and decision-making but also establishes a new paradigm for understanding complex multiphase transport phenomena through the seamless integration of high-fidelity simulation, systematic experimentation, and explainable machine learning.

To address the challenge of sparse monitoring data for particle sedimentation in non-Newtonian suspensions in stirred tanks and the risk of non-physical solid-phase inventory drift in purely data-driven models during long-term extrapolation, this study proposes a parametrized physics-informed neural network (P-PINN). The method embedded a two-dimensional depth-averaged sedimentation equation and a macroscopic mass conservation constraint into the neural-network framework. Specifically, to reduce the gradient competition between local data fitting and global physical constraints, a ramp strategy was designed to gradually adjust the weight of the conservation term. Furthermore, a log-time mapping was also introduced to improve the resolution of early-stage high-gradient sedimentation dynamics. The model was validated by using nine-point ultrasonic thickness measurements from bench-scale experiments under multiple operating conditions. The results showed that P-PINN successfully reconstructed the continuous spatiotemporal evolution of the sediment bed with a global root-mean-square error (RMSE) of 3.71 mm. It also exhibited stable generalization in leave-one-out tests. Compared with purely data-driven baselines, the ramp strategy substantially reduced solid-phase inventory drift during long-term extrapolation and helped maintain macroscopic mass conservation. This study offers a physically constrained surrogate modeling approach for sediment-bed prediction under sparse observations with potential for online monitoring of complex multiphase flow systems.