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During the drilling of highly deviated and horizontal wells, a pump shutdown causes drill cuttings to settle and form a cuttings bed in the annulus. This study investigated the incipient motion law of the particles on the cuttings bed surface when the drilling fluid starts circulating again. This work could help field engineers to determine a reasonable incipient pump displacement to improve hole-cleaning efficiency. In this study, the effects of the well inclination angle, cuttings size, and different cuttings densities on the critical velocity of particle motion are analyzed experimentally, using a large-scale flow loop. Next, based on a stress analysis of the particles on the cutting bed surface and on the boundary layer flow around the particles, an analytical formula for the surface shear force of the drilling fluid on particles is derived and a critical velocity model for incipient motion is established. Verification is then carried out and combined with the experimental results. This study has important implications for the design of drilling operations and for the management of cuttings transport in oil and gas wells. It can guide the setting and prediction of pump discharge to improve hole-cleaning efficiency.

Ground roll is usually considered as a common linear noise in land seismic data. The existence of the ground roll often masks the effective reflection information of underground media, resulting in the deterioration of seismic data quality. Therefore, ground roll suppression is one of the main tasks in seismic data processing. A large number of previous studies have proved that the time‐frequency signal processing method based on mathematical transformation has shown excellent performance in ground roll attenuation and still has development potential. Meanwhile, a convolutional neural network, as one of the popular deep learning technologies, has also been widely used in the field of seismic signal processing. In this paper, we combine the convolutional neural network with the time‐frequency signal processing method based on mathematical transformation, that is, spatial domain synchrosqueezing wavelet transform, and propose a complete ground roll suppression workflow of shot gathers in spatial wavenumber domain, realizing high‐precision and automatic ground roll removal. Field data examples show that compared with bandpass filtering, FK filtering, time domain synchrosqueezing wavelet transform, spatial domain synchrosqueezing wavelet transform and the convolutional neural network, the spatial domain synchrosqueezing wavelet transform convolutional neural network has achieved satisfactory results in effectively attenuating ground roll and retaining valid information.