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.

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