Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation. Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus, which can introduce cumulative errors and reduce the accuracy of inversion results. To address these issues, this paper introduces the analytical solution of the Zoeppritz equation into the inversion process. The equation is re-derived and expressed in terms of Young's modulus, Poisson's ratio, and density. Within the Bayesian framework, we construct an objective function for the joint inversion of PP and PS waves. Traditional gradient-based algorithms often suffer from low precision and the computational complexity. In this study, we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping, Lévy flights, and Gaussian mutation to optimize the quantum particle swarm optimization (QPSO), named improved quantum particle swarm optimization (IQPSO). The IQPSO demonstrates superior global optimization capabilities. We test the proposed inversion method with both synthetic and field data. The test results demonstrate the proposed method's feasibility and effectiveness, indicating an improvement in inversion accuracy over traditional methods.

Currently, horizontal well fracturing is indispensable for shale gas development. Due to the variable reservoir formation morphology, the drilling trajectory often deviates from the high-quality reservoir, which increases the risk of fracturing. Accurately recognizing low-amplitude structures plays a crucial role in guiding horizontal wells. However, existing methods have low recognition accuracy, and are difficult to meet actual production demand. In order to improve the drilling encounter rate of high-quality reservoirs, we propose a method for fine recognition of low-amplitude structures based on the non-subsampled contourlet transform (NSCT). Firstly, the seismic structural data are analyzed at multiple scales and directions using the NSCT and decomposed into low-frequency and high-frequency structural components. Then, the signal of each component is reconstructed to eliminate the low-frequency background of the structure, highlight the structure and texture information, and recognize the low-amplitude structure from it. Finally, we combined the drilled horizontal wells to verify the low-amplitude structural recognition results. Taking a study area in the west Sichuan Basin block as an example, we demonstrate the fine identification of low-amplitude structures based on NSCT. By combining the variation characteristics of logging curves, such as organic carbon content (TOC), natural gamma value (GR), etc., the real structure type is verified and determined, and the false structures in the recognition results are checked. The proposed method can provide reliable information on low-amplitude structures for optimizing the trajectory of horizontal wells. Compared with identification methods based on traditional wavelet transform and curvelet transform, NSCT enhances the local features of low-amplitude structures and achieves finer mapping of low-amplitude structures, showing promise for application.

As an important indicator parameter of fluid identification, fluid factor has always been a concern for scholars. However, when predicting Russell fluid factor or effective pore-fluid bulk modulus, it is necessary to introduce a new rock skeleton parameter which is the dry-rock V_P/V_S ratio squared (DVRS). In the process of fluid factor calculation or inversion, the existing methods take this parameter as a static constant, which has been estimated in advance, and then apply it to the fluid factor calculation and inversion. The fluid identification analysis based on a portion of the Marmousi 2 model and numerical forward modeling test show that, taking the DVRS as a static constant will limit the identification ability of fluid factor and reduce the inversion accuracy. To solve the above problems, we proposed a new method to regard the DVRS as a dynamic variable varying with depth and lithology for the first time, then apply it to fluid factor calculation and inversion. Firstly, the exact Zoeppritz equations are rewritten into a new form containing the fluid factor and DVRS of upper and lower layers. Next, the new equations are applied to the four parameters simultaneous inversion based on the generalized nonlinear inversion (GNI) method. The testing results on a portion of the Marmousi 2 model and field data show that dynamic DVRS can significantly improve the fluid factor identification ability, effectively suppress illusion. Both synthetic and filed data tests also demonstrate that the GNI method based on Bayesian deterministic inversion (BDI) theory can successfully solve the above four parameter simultaneous inversion problem, and taking the dynamic DVRS as a target inversion parameter can effectively improve the inversion accuracy of fluid factor. All these results completely verified the feasibility and effectiveness of the proposed method.