Optimization of Reinjection in Geothermal Reservoirs

Abstract

Re-injection of produced geothermal water for pressure support is a common practice in geothermal field management. The location selection of the re-injection 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 temperature decrease. Although the number of well combinations is potentially infinite, it has been customary to pre-specify a grid of potentially good well locations and then formulate the search to locate the most time- or cost-effective subset of those locations that meets production goals. To achieve this goal neural network technology is proposed. First, a knowledge base of representative solutions for a geothermal field located in Turkey was developed using a simulator. Then artificial neural networks to predict selected outcomes was 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 were generated.