How the 3D model of an Icelandic geothermal field picked its way through a mass of data to find the best energy hotspots

By simplifying their view of the Hellisheiði geothermal field, the plant’s geoscientists could deliver more productive and better-targeted wells

The science and geology of geothermal fields can be complex, confusing, and famously difficult to predict. That’s especially the case when drilling new wells, and having the confidence they can achieve the output expected of them.

Perhaps more than other green energies, geothermal power must also integrate a range of different geoscientific disciplines, while dealing with the varying data types and formats they generate.

(Across the years companies can amass a great deal of such diverse information.) None of which makes the job of establishing a ‘big picture’ to guide future development any easier.

But if you created simpler 3D models that were easier to understand, would they still deliver the accuracy and insight essential for the confident positioning of new wells?

That was the question a collaborative project between Iceland GeoSurvey and Icelandic energy and utility company Orkuveita Reykjavíkur set out to answer at the Hellisheiði Geothermal Field (home to the world’s eighth largest geothermal power plant). Using Leapfrog, the team compiled and analysed the data from 73 wells to create a series of 3D models representing the geology, alteration mineralogy, natural state temperature and resistivity of the area.

In this paper , you can see how they combined the models to observe their relationships, and began to unpick the complex interactions of the existing wells and – crucially – direct the next one.

The study also noted how, by combining the different models, several characteristics of the geothermal system could be highlighted and better understood, such as the clay cap confining the reservoir, and the convection system. “The combination of the natural state temperature with the alteration mineralogy also provided a great insight on the thermal evolution of the resource through time.”

Example extract

“Grouping the basaltic flow units between wells was done by visual correlation of units with matching thickness, location and depth. This method allowed to differentiate ten basaltic flows, including the surface Holocene flows and the basement rocks. A similar technique to group the corresponding intrusive basaltic units was completed to draw the intrusions, but instead of using the thickness and depth, they were grouped using known surface fractures and eruptive fissures, considering sub vertical planes. Fifteen intrusive units were generated using this technique, including the two most recent eruptive fissures visible on the surface. Other information such as aquifer studies and televiewer analyses were also taken into account to define the intrusions.”