Conceptual and 3D simulation modeling of the Sorgun hydrothermal reservoir (Yozgat, Turkey). (March 2017) - Yilmaz Turali, Elif; Simsek, Sakir
Highlights: District and greenhouse heating, and spa uses in Sorgun geothermal area. Developing of the conceptual model of Sorgun geothermal area by evaluating the geology, hydrogeology, water chemistry, isotopic compositions, and well data. 3D numerical simulation model based on the geothermal conceptual model. Calibration with pre-production temperature and pressure data and seven years production/injection data. Abstract: Since November 2007, the Sorgun geothermal field has been developed with two production wells and one reinjection well. Hot water produced from the geothermal wells is mainly used for district heating, greenhouse heating, and spa facilities. A total of 12 geothermal wells were drilled in this field to depths ranging from 90 to 444 mbgs, with measured borehole temperatures between 50 and 85.4 °C. During the heating period between November and May, the average rates of production and reinjection were 75 and 35 l/s, respectively. During the non-heating period, the production rate decreased to 45 l/s and the reinjection rate to 5 l/s. The measured water level was 110 mbgs during the heating period and rose to 96 mbgs during the non-heating period. A conceptual model of the geothermal field was created with defined model parameters and boundary conditions. This geothermal conceptual model was analyzed using the TOUGH2 numerical simulation program. A natural state model was developed, and simulated results were computed using static temperature data for the production wells before exploitation. After a good match between the modeled and measured temperature data was achieved, the natural state model was run with production/injection flow rate and enthalpy data from 2007 to 2014 for history matching. Results of this model were calibrated with measured water level and temperature data for the observation well. A good match was achieved between the measured values and the model's predicted values.