Shale Fracturing Characterization and Optimization by Using Anisotropic Acoustic Interpretation, 3D Fracture Modeling, and Supervised Machine Learning

摘要

Elastic anisotropy resulting from shale lamination makes fracture prediction in shale more complex, and traditional methods to predict fracture geometry assuming isotropy frequently prove to be inadequate. Common 3D fracture-modeling software is based on isotropic rock models, and models that account for anisotropy are computationally expensive, especially when numerous simulations must be performed by varying the input parameters for parametric study.