Machine-learning Fault Detection on 3D Seismic Migration Images of the San Juan Basin CarbonSAFE Project Site
Description:
The San Juan Basin CarbonSAFE Phase III project is performing a comprehensive site characterization for geologic carbon storage in the San Juan region located in northwest New Mexico, USA. Subsurface fault detection is crucial for site characterization and risk assessment. The project procured a legacy 3D surface seismic dataset acquired at the San Juan CarbonSAFE storage site for imaging subsurface structures using seismic migration. We refine the 3D velocity model using prestack depth migration velocity analysis and produce a high-resolution seismic migration image based on this updated velocity model. We then use our recently developed machine learning algorithm to perform 3D fault detection. Our result shows that the site does not contain significant faults that may lead to CO2 leakage, indicating that the site may be suitable for large-scale geologic carbon storage.
Session: Seismology for the Energy Transition
Type: Oral
Date: 4/18/2023
Presentation Time: 04:45 PM (local time)
Presenting Author: Lianjie Huang
Student Presenter: No
Invited Presentation:
Authors
Lianjie Huang
Presenting Author
Corresponding Author
ljh@lanl.gov
Los Alamos National Laboratory
David Li
davidzli@lanl.gov
Los Alamos National Laboratory
Kai Gao
kaigao@lanl.gov
Los Alamos National Laboratory
Rajesh Pawar
rajesh@lanl.gov
Los Alamos National Laboratory
George El-kaseeh
george.el-kaseeh@nmt.edu
New Mexico Institute of Mining and Technology
William Ampomah
William.Ampomah@nmt.edu
New Mexico Institute of Mining and Technology
Machine-learning Fault Detection on 3D Seismic Migration Images of the San Juan Basin CarbonSAFE Project Site