Shale Fracturing Characterization and Optimization by Using Anisotropic Acoustic Interpretation, 3D Fracture Modeling, and Supervised Machine Learning
- Ming Gu (Halliburton Technology) | Deepak Gokaraju (Halliburton Technology) | Dingding Chen (Halliburton Technology) | John Quirein (Halliburton Technology)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- Publication Date
- December 2016
- Document Type
- Journal Paper
- 573 - 587
- 2016. Society of Petrophysicists & Well Log Analysts
- 6 in the last 30 days
- 467 since 2007
- Show more detail
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.
A new workflow was created that integrates anisotropic acoustic log interpretation, 3D fracture modeling, and neural networks to improve fracture prediction accuracy and efficiency for anisotropic shales. The workflow generates a neural network with a limited number of 3D fracture-modeling cases; the fracture modeling uses rock mechanical properties interpreted from sonic logs with properly selected anisotropic acoustic models. The neural network trained from a pilot/offset well can be applied to predict fracture geometries or to optimize fracturing design for other wells from the same geological basin in a timely and cost-effective manner.
The workflow is demonstrated by generating neural-network models for two shale reservoirs. The fracture geometry predicted from the anisotropic models is compared with the one predicted from the conventional isotropic simulator. The results show that ignoring shale anisotropy leads to overestimated fracture widths and underestimated fracture containments, lengths, and net-pressures. The neural-network models are run in large parametric studies to demonstrate how the effective propped length and fracture productivity varies with perforation position, injected volume, and pumping rate in the two shale formations. The results provide valuable insights of selecting perforation location and optimizing pumping strategy.
The combination of hydraulic fracturing and horizontal drilling has made production from shale and tight formations commercially realistic. However, because of the laminated and platy nature intrinsic to shales, the isotropic acoustic model, which computes a single Young’s modulus and a single Poisson’s ratio from sonic and density logs, cannot fully describe their elastic behavior.
|File Size||7 MB||Number of Pages||15|