The slug test is still one of the simplest and cost-effective methods to interpret the hydraulic parameters for aquifer analysis. This study introduces two new estimation approaches for the slug test, the time shift method (TSM) and arc-length matching method (AMM), to identify aquifer parameters in a reliable and accurate manner, which was established on the idea that any change in the normalized drawdown or arc-length measurements of the data curve at the predefined drawdown levels is linked with the variation of storativity. These approaches remove the need for superimposition of the type curves and the field data. The proposed methods are straightforward to apply and automatize the parameter estimation process. TSM and AMM were tested with a number of numerical experiments including synthetically generated data augmented with random noise, hypothetical slug tests conducted in a heterogeneous rock-fracture system, and well-known real field data. The skin effect was also implemented to evaluate its impact on the estimation performance of the suggested approaches. The results verified that both proposed methods are able to produce estimates of hydraulic parameters more accurately than existing methods. The proposed methods could serve as a viable supplementary interpretation tool for slug test analysis.