Hydrogeological modelling of Olkaria domes geothermal field to predict ground subsidence

Abstract

Ground subsidence studies have been done on Olkaria geothermal field conventionally by comparing levels on benchmarks over years. Interferometric synthetic aperture radar (InSAR) systems have also been used to map surface deformation of small spatial extent. For the prediction of future dynamics of land subsidence in Olkaria due to geothermal resource exploitation, a hydrogeological conceptual model has been developed. In this model, hydrologic geothermal fluid properties are analysed and a relationship between the reservoir and geology of the wells established, subsidence is computed numerically. The model takes into account the hydrogeological condition of Olkaria geothermal field. Hydrological reservoir parameters are computed from well testing data. The study considers the Injectivity indices of the various wells under study as pre-computational indicator of the expected subsidence extents. Both two- and three-dimensional geological cross-sections are modelled with the rockworks software by inputting stratigraphic data for Olkaria domes. Geological simulations are used to study subsidence by assigning the ground formation with virtual material that deformed according to some essential relations in Rockworks computer software. Production zones are determined by a comparison between the well properties and corresponding well geology. Subsidence is then computed by the Tezarghi’s modified equation. Cumulative subsidence figures from the computation are in the range of 0.095-0.537m, without any reinjection. Computed values are then mapped in ArcGIS to develop a representative subsidence map. By application of these modelling and numerical computation methods, ground subsidence was effectively predicted using the five selected wells in Olkaria domes field. The hydrogeological model developed, and mapping is an important tool in the planning and development of a reinjection schedule and in subsidence mitigation. Subsidence prediction also is important in design of infrastructure which will be strong enough to resist the forces caused by subsidence.