Presenting the CCUS GoM Machine Learning Project

CCUS GoM is a Subsurface Study defining the Carbon Capture, Utilization and Storage (CCUS) capacity and risks of key prospects in protraction areas of the US Gulf of Mexico. Comprehensive Machine Learning was successfully implemented throughout the areas.

This extensive AOI study is focused offshore Texas, encompassing a total area of 31,197 km², utilizing 8x 3D coupled with 3x 2D data volumes, and incorporating up to 4,137 wells. The study employs modern data conditioning and semi-supervised machine learning techniques to best characterize the subsurface and assess suitability for carbon capture sights. Products are available from Q1 2023, see below for details. Download the project flyer:

Please contact us to enquire about the project:

Robert Sorley
President, Geoex MCG LLC
robert.sorley@geoexmcg.com
+1 (281) 744 0854

Machine Learning Workflow

In collaboration with Earth Science Analytics

Data Conditioning

1. Assess public domain data for internal inconsistencies
2. Extract wavelets to compare survey quality
3. Select reference survey
4. Bulk scale matching to reference survey
5. Mistie to correct for time shift
6. Time matching using gain pairs
7. True amplitude frequency equalization
8. Phase and wavelet matching

Deliverables