Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025
Description
These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and validating stress prediction models using ultrasonic velocity experiments on core samples and applying those models to sonic log data. The other report uses those near-field predictions as input to a thermo-poro-mechanical model to estimate far-field stress profiles under various thermal and pore pressure conditions.
Resources
| Name |
Format |
Description |
Link |
|
33 |
Applies physics-based modeling to estimate far-field stress changes along the wellbore due to drilling and pre-cooling effects. |
https://gdr.openei.org/files/1742/2-2439%20v2%20-%20University%20of%20Pittsburgh%20-%20Milestone%204.3.1%20Report_v3.pdf |
|
33 |
Uses lab experiments and machine learning to estimate vertical and horizontal stresses from sonic log data in well 16B(78)-32. |
https://gdr.openei.org/files/1742/2-2439%20v2%20-%20University%20of%20Pittsburgh%20-Milestone%202.3.2%20Report_Final.pdf |
Tags
- utah
- stress-prediction
- ultrasonic-velocity
- modeling
- 16b78-32
- egs
- 16b
- stress-anisotropy
- deep-learning
- utah-forge
- sonic-logs
- stress-profiling
- far-field-stress
- true-triaxial-testing
- finite-element-model
- technical-report
- thermo-poro-mechanical
- in-situ-stress
- machine-learning
- geothermal
- energy
- geothermal-reservoir