The issue of wellbore instability poses a significant challenge in the current exploration of shale gas reservoirs. Exploring more efficient shale stabilizers has always been a common goal pursued by researchers. In this paper, a novel shale stabilizer, denoted as ANS, was prepared by employing a silane-coupling modification method to graft (3-Aminopropyl) triethoxysilane (APTES) onto the surface of nano-silica. The structure of ANS was characterized through Fourier transforms infrared spectroscopy (FT-IR), thermo-gravimetric analysis (TGA), and particle size tests (PST). The shale stabilizing properties of ANS were evaluated through tests such as pressure penetration, BET analysis, hydration expansion and dispersion. Furthermore, the interaction between ANS as a shale stabilizer and clay was explored through clay zeta potential and particle size analysis. The results indicated that ANS exhibited a stronger plugging capability compared to nano-silica, as evidenced by its ability to increase the shale pressure penetration time from 19 to 131 min. Moreover, ANS demonstrated superior hydration inhibition compared to commonly used KCl. Specifically, it reduced the expansion height of bentonite from 8.04 to 3.13 mm and increased the shale recovery rate from 32.84% to 87.22%. Consequently, ANS played a dual role in providing dense plugging and effective hydration inhibition, contributing significantly to the enhancement of wellbore stability in drilling operations. Overall, ANS proved to be a promising shale stabilizer and could be effective for drilling troublesome shales.

The Rate of Penetration (ROP), as a core indicator for evaluating drilling efficiency, holds significant importance for optimizing drilling parameter configurations, enhancing drilling efficiency, and reducing operational costs. To address the limitations of existing ROP prediction models—such as difficulties in modeling, solution complexity, and inefficient utilization of field big data—this paper proposes a Bayesian-Optimized LSTM-based ROP prediction model with fused feature selection (BO-LSTM-FS). The model innovatively introduces a sequential-cross-validation fused feature selection framework, which organically integrates Pearson correlation analysis, variance filtering, and mutual information, and incorporates a forward search strategy for final validation. Building on this, the Bayesian optimization algorithm is employed for systematic global optimization of the key hyperparameters of the LSTM neural network. Experimental results demonstrate that the BO-LSTM-FS model achieves significant performance improvements compared to traditional Backpropagation (BP) neural networks, standard LSTM neural networks, and CNN-LSTM models: Mean Absolute Error (MAE) is reduced by 48.0%, 29.3%, and 23.5%, respectively; Root Mean Square Error (RMSE) by 45.5%, 38.5%, and 32.2%, respectively; Mean Absolute Percentage Error (MAPE) by 47.8%, 29.4%, and 22.6%, respectively; and the Coefficient of Determination (R²) is increased by 8.6%, 4.4%, and 3.0%, respectively. The model exhibits high prediction accuracy, fast convergence speed, and strong generalization capability, providing a scientific reference for improving the Rate of Penetration in practical drilling operations.

Marine shale is highly prone to wellbore collapse due to its high pore pressure, propensity for hydration and swelling, distinct bedding planes, and low tensile strength. Horizontal in situ stress serves as a critical parameter for wellbore stability analysis; however, its accurate prediction is extremely challenging in complex geological environments. Conventional studies often simplify the wellbore as a circular shape, neglecting its natural elliptical deformation under non-uniform in situ stress, which leads to reduced predictive accuracy. To address this limitation, this study establishes an elliptical wellbore model that incorporates the strain-softening characteristics of shale. Theoretical models for stress distribution in both elastic and plastic zones were derived. The strain-softening behavior was validated through triaxial compression tests, providing a foundation for analytical solutions of stress distributions around circular and elliptical wellbores. Furthermore, an elliptical wellbore-based model was developed to derive a new prediction equation for horizontal in situ stress. Numerical programming was employed to compute stress distributions, and finite element simulations under various aspect ratios corroborated the theoretical results, showing excellent agreement. Results demonstrate that the elliptical wellbore model captures the near-wellbore stress state more accurately. As the aspect ratio increases, the extreme values of radial and tangential stresses increase significantly, with pronounced stress concentrations observed around the 180° and 360° positions. Predictions of horizontal in situ stress based on the proposed model achieved over 89% accuracy when verified against field data, confirming its reliability. This study overcomes the limitations inherent in the traditional circular wellbore assumption, providing a more precise analytical method for wellbore stability assessment in Marine shale under complex geological conditions. The findings offer a valuable theoretical basis for wellbore stability management and drilling engineering design.

Shale oil and gas reservoirs are characterized by low porosity and low permeability. The development of ultra-long horizontal wells can significantly increase reservoir contact area and enhance single-well production. Shale formations exhibit distinct bedding structures, high formation pressure, high rock hardness, and strong anisotropy. These characteristics result in poor drillability, slow drilling rates, and high costs when drilling horizontally, severely restricting efficient development. Therefore, accurately predicting the drillability of shale gas wells has become a major challenge. Currently, most scholars rely on a single parameter to predict drillability, which overlooks the coupled effects of multiple factors and reduces prediction accuracy. To address this issue, this study employs drillability experiments, mineral composition analysis, positional analysis, and acoustic transit-time tests to evaluate the effects of mineral composition, acoustic transit time, bottom-hole confining pressure, and formation drilling angle on the drillability of horizontal well reservoirs, innovatively integrating multiple parameters to construct a nonlinear model and introducing three intelligent optimization algorithms (PSO, AOA-GA, and EBPSO) for the first time to improve prediction accuracy, thus breaking through the limitations of traditional single-parameter prediction. Based on these findings, a nonlinear regression prediction model integrating multiple parameters is developed and validated using field data. To further enhance prediction accuracy, the model is optimized using three intelligent optimization algorithms: PSO, AOA-GA, and EBPSO. The results indicate that the EBPSO algorithm performs the best, followed by AOA-GA, while the PSO algorithm shows the lowest performance. Furthermore, the model is applied to predict the drillability of Well D4, and the results exhibit a high degree of agreement with actual measurements, confirming the model’s effectiveness. The findings support optimization of drilling parameters and bit selection in shale oil and gas reservoirs, thereby improving drilling efficiency and mechanical penetration rates.

To extend the drilling limit of horizontal sections, the demand for utilizing multiple hydraulic oscillators at intervals has become increasingly prominent. However, current research on the operating range of multiple oscillators, their mutual interference characteristics, and the impact of oscillation on drill string fatigue damage remains scarce. The results indicate that when the vibration ranges of multiple oscillators overlap, the overlapping segment experiences negative effects under identical excitation frequencies. If the oscillators operate at different frequencies, the displacement envelope of the drill string tends to become irregular. Within the normal amplitude range of oscillators, no significant fatigue damage is generally observed in adjacent sections of the drill string. However, when minor initial cracks exist in the drill string, an increase in oscillator amplitude leads to accelerated crack propagation, significantly hastening the fatigue failure process. In field operations, the parameters of multiple oscillators should be optimized according to specific working conditions, and timely non-destructive inspection of the drill string sections within the effective range of the oscillators must be conducted. The study provides novel insights into vibration wave propagation and fatigue damage in drill strings under multi-point excitation.