Geological Society of America

T66 Geologic Carbon Storage

Sunday, 15 October 2023
1:30 PM - 5:30 PM
324

IMPROVED MACHINE-LEARNING FAULT DETECTION AT THE SAN JUAN BASIN CARBONSAFE PROJECT SITE

Paper number: T66-5
Program: Technical Sessions
Time: 2:25 PM – 2:40 PM

Authors

Lianjie Huang Presenting Author
Los Alamos National Laboratory, Los Alamos, NM
David Li
Los Alamos National Laboratory, Los Alamos, NM
Kai Gao
Los Alamos National Laboratory, Los Alamos, NM
Rajesh J. Pawar
Los Alamos National Laboratory, Los Alamos, NM
Adewale Amosu
New Mexico Tech, Socorro, NM
George El-kaseeh
New Mexico Institute of Mining and Technology, Socorro, NM
William Ampomah
New Mexico Tech, Socorro, NM

Abstract

Fault detection is crucial for site characterization in geologic carbon storage. Machine-learning fault detection is an emerging technique that has many advantages compared with conventional human fault detection, including high computational efficiency and the capability of detecting faults with tiny geologic formation displacements, etc. However, accurate migration images are also crucial for fault detection. Noisy migration images can result in false detection of faults. We improve migration images of the San Juan Basin CarbonSAFE project site using migration velocity analysis and prestack depth migration. We further remove image noise in migration images for reliable fault detection. We then perform fault detection on migration images using a nested residual neural network. Our results indicates that there are no major faults around the planned CO2 injection zone.

Geological Society of America Abstracts with Programs. Vol. 55, No. 6

doi: 10.1130/abs/2023AM-395871

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