The Mita Geothermal Field in Cerro Blanco Guatemala was discovered in 1997 during mineral exploration by Goldcorp and is presently being developed with the intention to supply power for the planned gold mining operation. Drilling and testing of the production wells completed in 2012 led to the refinement of the Mita reservoir conceptual model and geological model as well as the development of a numerical model of the field. The initial numerical model created was developed using the TOUGH2 reservoir simulation package. Several simulation tools were investigated in the numerical modeling including the PEST optimization program (Doherty, 2005) and automation scripts written in Python combined with the PyTOUGH module (Croucher, 2010). Python was used to allow both transient and steady state TOUGH2 simulation models to be calibrated simultaneously, significantly improving the speed and ease of reservoir model calibration. PEST was used on the manually calibrated natural state model implementing the Python code to improve the natural state match. The successful integration of PEST, Python and TOUGH2 suggests that significant developments in the automation of geothermal reservoir modeling can be made. Such improvements could include the creation of algorithms in Python that can change reservoir model structure and geological features; something which is currently not included in PEST as a standalone optimization program. Natural state modeling produced good matches to the measured temperatures and pressures, and showed good agreement with the major flow features of the Mita reservoir conceptual model. Short-term production history matching was also able to match the well discharge trends that formed a basis for the preliminary predictive simulations.

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