Energy exploration for low-carbon resources: A case study of deep geothermal and lithium extraction by passive seismic interferometry with curvelet denoising

We have investigated technical feasibility of a first exploration tool for deep geothermal and lithium extraction that are seen to be promising future energy resources with low-carbon emission. The method we adapted is called seismic interferometry which can redatum source positions to their receiver ones and then estimate an image of the subsurface from seismic data. We applied seismic interferometry to a passive seismic dataset acquired in the Neuquen basin, Argentina where relatively vast amount of heat resources are expected to be present. This region is highly active in terms of active volcanoes that are being surrounded by a complex geological structure of Andes Mountains associated with Nazca slab subduction. Since there are a number of interferometric techniques available to these days, we compared crosscorrelation, crosscoherence and multi-dimensional deconvolution in order to evaluate individual performance. From the comparison, we found that the virtual reflection profiles given by curvelet denoising applied to multi-dimensional deconvolution, which is stabilized using a singular-value decomposition, showed a better fault image whose existence was previously interpreted by active seismic and exploration wells in this region. Although initial stages of these low-carbon businesses tend to cost a lot especially in frontier regions, our approach has a capability to provide at least a scale of major faults which are considered to be potentially crucial for reservoir performance with a relatively small footprint on the surface.