Abstract: Abstract: [Objective] In the development of unconventional oil and gas, there exist problems such as complex reservoir structure and unclear rock mechanical properties. During the design of horizontal well trajectories and fracturing schemes, it is necessary to rely on rock mechanical models for segment and cluster division and design, thereby increasing the modification volume, reducing the risk of set changes, and achieving efficient development of unconventional oil and gas. The rock mechanics model can simultaneously reflect the continuous changes of mechanical parameters in the longitudinal and transverse directions with high resolution in the target area of the horizontal well trajectory. Therefore, in response to the model requirements, this paper provides a mechanical parameter prediction and modeling method based on statistical regression and prestack inversion. [Methods] Basing on the core test data and logging data, the quantitative relationship between elastic parameters and rock mechanical parameters is established and analyzed. Then, three-dimensional prestack inversion is performed using both drilling data and seismic data to obtain precise elastic parameters, including longitudinal wave velocity, density, Poisson's ratio, and Young's modulus. [Results] The application of this method in the tight glutenite reservoirs of the Bonan Sag has yielded significant and practical results. First, a robust quantitative relationship between mechanical and elastic parameters was established. The statistical regression relationship between Young's modulus and Uniaxial Compressive Strength (UCS) demonstrated a strong correlation (e.g., R² > 0.75), validating the feasibility of predicting rock mechanical strength from elastic parameters in this glutenite formation. Statistical analysis confirmed that the mechanical parameters of these highly compacted, low-porosity glutenites are primarily controlled by lithology and gravel content, exhibiting a very weak correlation with burial depth. This justifies the use of a unified predictive model across the entire studied depth interval. Second, a high-resolution 3D mechanical parameter model was constructed. A depth-domain structural model was built using logging data and a smoothed time-depth velocity field. Following attribute extraction, a comprehensive 3D mechanical parameter model for the target block was established. This model effectively overcomes the discreteness and non-continuity inherent in core and logging data, accurately characterizing the continuous lateral and vertical variations of mechanical properties. Third, the results are directed toward engineering application. To fully leverage the high vertical and lateral resolution of the mechanical parameter volume, the calculated seismic attributes were integrated into the 3D geological model based on engineering requirements. This outcome provides an accurate model for fracturing design, enabling precise assessment of the rock mechanical distribution along horizontal well sections, rational design of stage and cluster placement, optimization of pumping parameters, and enhancement of fracture conductivity. Consequently, it significantly increases the stimulated reservoir volume, effectively boosting single-well productivity and recovery rates. [Conclusion] The study leads to the following main conclusions: The integrated methodology of pre-stack inversion and statistical regression is a viable and effective solution for predicting 3D mechanical parameters in tight glutenite reservoirs. This method builds a bridge between petrophysical and seismological parameters, effectively resolving the challenge of poorly constrained rock mechanical properties in complex unconventional reservoirs. The constructed 3D mechanical parameter model provides a more reliable geological basis for optimizing well trajectories and fracturing designs. [Significance] The primary significance of this research lies in providing a reliable and scalable method for 3D rock mechanical parameter prediction and modeling, possessing substantial theoretical innovation and practical application value. It offers a dependable data foundation and a theoretical framework for defining mechanical boundaries and optimizing fracturing designs in heterogeneous tight reservoirs. This approach holds significant potential for improving fracturing efficiency and maximizing production in challenging tight glutenite formations.