As deep oil and gas resources and Carbon Capture and Storage (CCS) are developed, enhancing drilling efficiency in hard rock formations has emerged as a critical technology in oil and gas extraction. The advancement of ultrasonic, high-frequency vibration rock-breaking technology significantly facilitates efficient rock crushing. When subjected to ultrasonic high-frequency vibrations, the rock’s response is a crucial issue in implementing ultrasonic vibration rock crushing technology. This study employed numerical simulation and theoretical deduction methods, utilizing a multi-physics approach that couples solid mechanics with pressure acoustics. It integrated information on common influencing parameters of ultrasonic generators and reservoir rock properties to establish model parameters, analyze simulation results, and perform theoretical deductions. The research investigated the response patterns of different-sized rock samples under high-frequency ultrasound vibration excitation across various frequencies, amplitudes, and confining pressure conditions. Through the development of a three-dimensional model and the application of principles from solid mechanics and elastoplasticity, the study derived equations that describe the resonance frequencies of rock blocks under confining pressure as functions of relevant rock parameters. The findings indicate that ultrasonic vibrations can effectively induce rock displacement. Under excitation frequency sources, the rock exhibits a natural frequency correlated with the rock sample size. When the excitation frequency approximates the natural frequency, the rock resonates. At this point, the rock’s surface displacement is maximal. The rock undergoes tensile stress, leading to stress concentration that facilitates rock damage and fragmentation. Increasing the excitation amplitude enhances rock crushing, as it amplifies the maximum surface displacement under the same frequency excitation. Confining pressure exerts an inhibitory effect on the rock’s vibration response, but it does not alter the resonance frequency of the rock sample, a fact verified by both numerical simulation and theoretical results. Based on the research findings in this paper, it can help to optimize the parameters of ultrasonic vibration rock breaking in field application to achieve the best rock-breaking effect.

Aiming at the problem that the well depth parameters in existing intelligent drilling technology can not be obtained underground, a multi-branch parallel neural network is proposed to solve the problem of downhole well depth tracking, and its effectiveness is verified by a field example. After analyzing and correcting the quality of the logging data collected on site by using DBSCAN (a density clustering algorithm), five parameters of WOB, rotating speed, displacement, pump pressure, and torque are selected to predict and calculate the downhole mechanical ROP. Adjust the structure of a traditional artificial BP neural network and design a multi-branch parallel neural network, change the basic architecture of the original hierarchical operation, make full use of the operation efficiency of a computer to realize parallel operation, and adopt the method of point-to-point depth comparison when evaluating the well depth tracking effect. The results indicate that the MAE and mechanical drilling rate evaluation values obtained were 1.18 and 0.873, respectively. The multi-branch parallel neural network achieved a 66.55% improvement in MAE compared to the original BP neural network, while the R² evaluation method showed a 61.82% increase. The average point-by-point comparison error in the example calculation was 0.012 m, with a maximum error of 0.268 m. This result can serve as a fundamental basis for judging changes in well depth during the drilling process.