Optimization of Re-injection in Low Temperature Geothermal Reservoirs Using Neural Network and Kriging Proxies

Re-injection of produced geothermal water for pressure support is a common practice in geothermal field management. The location selection of the reinjection well and the rate of injection is a challenging subject for geothermal reservoir engineers. The goal of optimization for this type of problem is usually to find one or more combinations of geothermal re-injection well locations that will maximize the production and the pressure support at minimum cost and minimum enthalpy decrease. Although the number of well combinations is potentially infinite, it has been customary to prespecify a grid of potentially good well locations and then formulate the search to locate the most timeor cost-effective subset of those locations that meets production goals. Typically, a knowledge base of representative solutions is developed using a simulator. Then an artificial neural network to predict selected outcomes is trained and tested. In the next step well combinations and injection rates of these wells to predict outcomes with a given number of injection wells are generated. On the other hand, knowledge base of representative solutions may be kriged to generate an optimization surface which then be used to select new optimal search directions. In this study, neural network proxy and kriging proxies for fast reinjection location evaluations are compared using low temperature Kizilcahamam, Turkey geothermal field. The results show that neural network proxy method is faster and more accurate then kriging proxy. It is observed that accuracy of kriging proxy optimization method depends on accuracy of variogram analysis. Moreover, kriging proxy optimization may not result in global optimum.