The application of geostatistics in seismic inversion techniques has been proven somewhat reliable in the delineation of reservoir properties and has recently attracted the attention of many geoscientists. However, there are cases where its prediction returned negative results after drilling. In this research, we re-evaluated a reservoir in Inas Field, whose geostatistical inversion result wrongly predicted sand continuity, resulting in the spudding of a dry hole. When a geostatistical seismic inversion is successfully applied, it provides an increase in seismic resolution and aids the prediction of sand continuity. Although this method relies more on the statistical data from a well because of the limitation of the seismic data in resolving thin geologic features, the spatial variation of reservoir parameters still depends on seismic data, which often have poor resolution quality. Therefore, to investigate the impact of bandlimited data on the geostatistical inversion, we harmonically extended the seismic bandwidth by applying a sparse-layer spectral inversion algorithm to the data. This algorithm increased the seismic data bandwidth from 80 Hz to 180 Hz, and its tuning thickness reduced from 32 m to 10 m at the reservoir interval. The resultant broadband (180 Hz), as well as the original seismic (narrowband of 80 Hz) data, were both used as input to build two separate geostatistical prediction models, respectively. Twenty (20) realizations of these models were generated, ranked into P10, P50, and P90, and the best case was selected for interpretation. These realizations were used to characterize the reservoir lithofacies distribution. When compared, the result of the broadband inversion, facies and sand distribution model showed that the reservoir facies changed towards the location of the dry well. The broadband geostatistical inversion efficiently improved the reservoir characterization process by not only producing an accurate estimation of the lateral extent of the reservoir heterogeneities but also generating outcomes that help us understand why other geostatistical inversion analyses of the target reservoir were misleading. Contrary to the popular assumption, it was discovered that the tuning effects of bandlimited data could affect the result of a geostatistical inversion and result in wrong facies predictions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.