I have developed a novel insight into the differences between static and dynamic moduli and their effects on the performance of seismic geomechanics inversion. This achievement is obtained from triaxial deformation tests and ultrasonic measurements on core plugs and reveals that the static Young’s modulus deviates from the dynamic one in porous media, especially in particular ranges of depth and pressure, although conventional regression relationships suggest the opposite, i.e., similar trends for the static and dynamic Young’s moduli. Next, a novel simple approach is formulated to incorporate laboratory information directly into a seismic low-frequency model (LFM) using an artificial neural network to achieve a static low-frequency model (SLFM). Respecting the critical role of the LFM in the reliability of seismic inversion, any modification to the process of building this model can contribute to higher accuracy of the subsequent seismic geomechanics modeling. For this, LFMs are built using static and dynamic data before proceeding to seismic inversion to derive 3D cubes of static Young’s and bulk moduli. The results are successfully validated using data from known wells as well as a blind well. The modeling outcomes demonstrate that the seismic inversion based on the dynamic low-frequency model (DLFM) would return the same results for static and dynamic bulk moduli. In contrast, the results are erroneous for the static Young’s modulus when the conventional DLFM was adopted. Accordingly, the intelligent approach to static low-frequency modeling is found to be a good interpolation technique for estimating geomechanical parameters, as indicated by the good agreement between the static data and the corresponding inversion results at the well locations. My findings place emphasis on the necessity of reconsidering the relationship between the static and dynamic Young’s moduli and highlight the advantage of using an SLFM to increase the accuracy of geomechanical modeling.

中文翻译：

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静态低频模型对地震地质力学反演的影响

我对静态和动态模量之间的差异及其对地震地质力学反演性能的影响有了新的认识。这一成就是通过对岩心塞进行三轴变形试验和超声波测量获得的，并揭示了静态杨氏模量在多孔介质中与动态杨氏模量存在偏差，尤其是在特定的深度和压力范围内，尽管传统的回归关系表明相反，即相似静态和动态杨氏模量的趋势。接下来，制定了一种新颖的简单方法，使用人工神经网络将实验室信息直接纳入地震低频模型 (LFM)，以实现静态低频模型 (SLFM)。尊重 LFM 在地震反演可靠性中的关键作用，对该模型构建过程的任何修改都有助于提高后续地震地质力学建模的准确性。为此，在进行地震反演以导出静态杨氏模量和体积模量的 3D 立方体之前，使用静态和动态数据构建 LFM。使用已知井和盲井的数据成功验证了结果。建模结果表明，基于动态低频模型 (DLFM) 的地震反演将针对静态和动态体积模量返回相同的结果。相反，当采用传统的 DLFM 时，静态杨氏模量的结果是错误的。因此，静态低频建模的智能方法被发现是一种用于估算地质力学参数的良好插值技术，正如静态数据与井位相应反演结果之间的良好一致性所表明的那样。我的发现强调了重新考虑静态和动态杨氏模量之间关系的必要性，并强调了使用 SLFM 提高地质力学建模准确性的优势。