Identification of Subsurface Geological Structure in a Geothermal System Using MT Imaging Technology

Development of a geothermal resource has naturally high risks, while the highest risk is faced in the upfront stage (i.e. resource risk). Accordingly, better understanding of the subsurface condition should be achieved by integrating high quality geoscientific data to develop a geothermal conceptual model. Exploratory well targets are then determined on the basis of the conceptual model. The subsurface drilling target is actually directed to high temperature and high permeability zone. Temperature profile could be approximated by using subsurface resistivity values derived from MT data. Base of conductive zone (BOC) in a high temperature geothermal system usually correlates with temperature of about 180-200C. The next challenge is to determine permeable zone. The permeable zone is usually associated with subsurface geological structures. Geological mapping could only figure out geological structures indicated at the surface. However, continuation of the geological structure into the subsurface is difficult to detect. Various technologies are developed to answer it. One of them is by using magnetotelluric technology. The existence of the structure can be indicated by resistivity contrast caused by the structure filled with fluid or contact with other formation which have different resistivity values. This condition will produce splitting on the curve and impedance polarization of MT data as the response of the structure. How big the contrast of the resistivity will affect the pattern of the splitting curve, and so the distance between MT stations with the location of structures will affect the frequency at which the splitting occurs. While the elongation of polar diagram could provide information on the strike direction, in which polar diagram give the response of relatively parallel or perpendicular to the strike. This paper demonstrates the ability of MT technology for delineating the subsurface geological structures using 2-D and 3-D forward modeling/simulation of synthetic MT data as well as real MT data.