Coupling Flow & Transport Modeling with Electromagnetic Geophysics to Better Understand Crustal Permeability

Over the last 30 years, considerable effort has focused on understanding the distribution of permeability within the earth's crust and its implications for flow and transport. The scarcity of direct observations makes the description of permeabilities beyond depths of about 3 km particularly challenging. Numerous studies have defined depth-decay relationships for basement permeability, while others note that it is too complex to be characterized by a general relationship. Hydrothermal modeling studies focusing on two geothermal systems within the tectonically active Rio Grande rift of New Mexico suggest that there may be laterally extensive regions of highly permeable (10-14 to 10-12 m2) basement rocks at depths ranging between 4 and 8 km. The NaCl groundwater signature, elevated fracture density, and secondary mineralization of fractured basement outcrops associated with these geothermal systems indicate that there may indeed be significant groundwater flow within the basement rocks of the rift. We hypothesize that there are extensive regions of highly permeable crystalline basement rocks at depths greater than 3 km within the Rio Grande rift. These fractured zones serve as large conduits for geothermal fluids before they ascend to shallow depths through gaps in overlying confining sediments or along faults. To test these hypotheses, we use a combination of geophysical observations and flow and transport modeling. We used electromagnetic geophysics (TEM & MT) to image resistivity in one of the hypothesized deep circulation geothermal systems near Truth or Consequences, NM. The resistivity dataset, in tandem with geochemical and thermal observations, is then used to calibrate a hydrothermal model of the system. This new calibration methodology has the potential to change the way researchers study crustal fluid flow and geothermal systems; thereby providing a tool to explore depths greater than 3 km where minimal data is available. In addition, it has the advantage of being scale-independent; meaning it can be applied to localized models just as well as continental-scale models.