Resistivity Imaging of Geothermal Resources Using 1D and 3D MT inversion A Case Study of Menengai Geothermal Field in Kenya

Abstract

This study investigated the geothermal potential over the Menengai Geothermal field through correlation of 1D and 3D resistivity models in order to recover 3D resistivity structures not seen from 1D models which is very important in geothermal systems in order to reduce ambiguities in 1D resistivity models which assumes resistivity varies only with depth as compared to 3D resistivity models where resistivity varies in all directions. Magnetotellurics (MT) and Transient electromagnetics (TEM) is commonly used to analyse the resistivity distribution that can give indications of alteration zones especially the clay capping and to contribute more information to the conceptual model to help target geothermal wells and assess resource capacity in potential areas. MT and TEM data obtained from earlier surveys since 1999 to 2013 has been re-processed and interpreted using 1D and 3D inversions. Usually 1D interpretation is easy to obtain and is always representative to shallow depths while 3D can image resistivity structures at deeper depth. Mainly 1D inversion has always been used due to limitation in computer hardware to perform 3D inversion, but due to developments in computational hardware a workflow for 3D inversion has been set and carried out and the results from the inversion interpreted in form of resistivity maps and cross sections. This has been accomplished using a 3D inversion algorithm. This paper has explained the differences in results between 1D and 3D models and come up with general recommendations for effective MT resistivity imaging of geothermal resources. This has provided reliable information about the presence, location, and size of geothermal system in Menengai Geothermal field. The results from both models correlate quiet well near the surface and then some difference in resolution as you go deeper. 3D models are able to resolve deeper structures quiet well as compared to 1D. So the general recommendation is to interpret MT data using 1D near the surface and rely more on 3D models for the deeper portion to make a conclusive interpretation.