Case Study: EMerald Geomodelling cuts drilling by 30% with Seequent's AEM, Workbench, and machine learning

How a Nordic highway reduced drilling by 30% with AEM, Workbench, and machine learning

EMerald Geomodelling needed a faster, more accurate way to determine bedrock depth and reduce the number of boreholes required for a 19-kilometer highway project in Norway, where complex glacial deposits and marine clays made ground conditions highly variable. Working with Seequent’s Workbench and airborne TEM survey data, they set out to improve subsurface understanding and target drilling more effectively.

Seequent’s Workbench software, combined with SkyTEM airborne geophysical data and EMerald Geomodelling’s machine learning models, enabled the team to build a more complete 3D view of the subsurface and continuously update it as new data came in. The result was a 30% reduction in drilling costs, about 200 fewer boreholes than originally estimated, and up to six months saved on the project timeline, while also reducing environmental impact.

EMerald Geomodelling

Andi A. Pfaffhuber

Chief Executive Officer and Co-founder