Advancing Quantitative Seismic Characterization of Physical and Anisotropic Properties in Shale Gas Reservoirs with an FCNN Framework Based on Dynamic Adaptive Rock Physics Modeling  
[PERSON]  
1 Changchun Institute of Technology, Changchun 130021, China 1x11 Changchun Institute of Technology, Changchun 130021, China 1x11 Changchun Institute of Technology, Changchun  
The objective of constructing a rock physics model (RPM) for shale gas reservoirs is to establish quantitative correlations between the physical parameters of the reservoir (including permeability, porosity, pore morphology, clay content, mineral composition, and fracture density) and elastic parameters (such as longitudinal wave velocity, shear wave velocity, and density) [4; 5]. The evolution of RPMs for shale reservoirs has advanced from initial empirical and single theoretical models [6] to the collaborative development of equivalent medium models using diverse methodologies [7; 8; 9; 10; 11; 12; 13; 14]. This developmental trajectory signifies the transition of medium properties from simplicity to complexity, from elasticity to viscoelasticity, from isotropy to anisotropy, and from a singular scale to multiple scales [15; 16; 17; 18; 19]. When geological strata undergo tilting due to folding and thrusting, the axis of symmetry in transversely isotropic (TI) media tilts, leading to the transition into tilted transversely isotropic (TTI) media [20; 21]. Presently, research on rock physics modeling for TTI media falls short of meeting exploration requirements. In the modeling process, it is crucial to incorporate adaptive modeling to address variations in the macroscopic anisotropic characteristics of shale gas reservoirs. However, research on dynamic adaptive rock physics modeling, catering to changes in reservoir characteristics, remains limited. Developing rock physics inversion methods based on rock physics modeling and utilizing conventional well log data to compute reservoir physical parameters can yield diverse information for reservoir evaluation [22]. Processing pre-stack seismic inversion data can provide seismic anisotropy information for reservoir prediction. Therefore, to integrate both types of data and methods for maximum reservoir feature information, it is imperative to apply artificial intelligence algorithms that amalgamate well log data, RPMs, and seismic data. This approach will facilitate the acquisition of spatial distributions of regional reservoir physical parameters and seismic anisotropy parameters within the artificial intelligence framework. With the continuous advancement of artificial intelligence technology, intelligent algorithms based on neural networks are extensively employed across various scenarios in shale oil and gas exploration and development, significantly impacting the field. In 2006, [PERSON] et al. formally introduced the concept of deep learning, distinguishing it from machine learning [23]. The essence of deep learning lies in feature extraction, employing a data-driven approach to extract features representing real-world objects or data, thereby enabling machines to possess analytical and learning capabilities akin to humans [24; 25; 26]. [PERSON] et al. implemented oil and gas reservoir prediction using convolutional neural networks, demonstrating excellent agreement with actual conditions [27]. [PERSON] et al. conducted quantitative seismic interpretation of shale gas reservoirs using back propagation neural networks (BPNN) and rock physics inversion of reservoir parameters, showcasing the robustness of neural networks in reservoir prediction [14]. In this study, through the analysis of the geological characteristics of shale gas reservoirs and consideration of variations in dip angles, we construct a dynamic adaptive rock physics model (DARPM). Focused on describing changes in reservoir properties and seismic anisotropy features resulting from dip angle variations in shale gas reservoirs, the model aims to adapt to geological characteristic changes.