Designing Thermal and Tracer Injection Backflow Tests

Abstract

Thermal injection backflow tests are the best method of determining heat transport parameters in geothermal and sensible energy storage reservoirs. The tracer injection backflow tests, on the other hand, are important tools for characterizing the nearby region of potential injectors.
Developing models of design and interpretation for both type of tests is of great importance for two reasons. First, the models help designing of these tests appropriately to recover maximum amount of information with minimum cost. Second, reliable estimates of parameters are of vital importance in optimal reservoir management specifically designing and operating reinjection operations.
This work presents, separate new analytical models of thermal and tracer injection backflow tests in geothermal reservoirs. Then, a parameter sensitivity analysis is applied to each model to identify the best combination of decision variables. Each combination of the decision variables are referred to as the design alternatives. The decision variables for thermal and tracer tests are, the amount of tracer to be injected, duration of injection and backflow periods and hence the distance of investigation, ratio of the injection to backflow rates, sampling domain in time (sampling interval) and frequency of sampling and finally the temperature of the injected water.
As a result, as interpretation tools, the new models provide in situ and hence, the most reliable estimates of transport parameters. As design tools, they help fixing the most desirable combination of the decision variables for the injection and backflow thermal and tracer tests. A parameter sensitivity analysis is employed to carry out the designing of both types of test. We have found that transverse tracer Peclet number and transverse thermal Peclet number are the most influential parameters. In addition, the injection to backflow ratio is effective to a lesser degree than the transverse Peclet number. The possible values of these two parameters set the best combination of decision variables.
Finally note that, the results of this work are directly applicable to assessing efficiency of sensible energy storage in aquifers as well.