Seismic Inversion Resolution Enhancement With (3S) Spectral Blueing, Spectral Balancing, and Stochastic Inversion on Fluvio Deltaic Environment

The accuracy of the geomodelling stage is vital in the exploration and development of oil and gas fields. One of the many factors that can improve the precision of the modeling is seismic inversion. As secondary data in estimating collocated co-kriging, seismic inversion is notable. The results of seismic inversions are often limited in terms of resolution. It is precarious if it is still used for rock property modeling because of the inability to separate subsurface geological events. Therefore, this research offers an integrated seismic inversion enhancement method, namely the 3S method. Using a combination of 3S, namely Spectral Blueing, Spectral Balancing, and Stochastic Inversion, it is expected to give a solution in overcoming issues in thin-bed seismic inversion. Spectral Blueing aims to increase the dominance of Blue Spectrum by analyzing the slope spectrum of the well data, bandpassing, and analyzing the deconvolution operator. In this method, the spectrum of the well data is used to analyze the slope of the blue spectrum component, which is absent in seismic data in general. This process will produce a deconvolution operator wavelet that increases the amplitude spectrum in the blue spectrum area. In addition, spectral balancing is a feature that can balance the shape of the amplitude spectrum to resemble a plateau shape. This approach's base is bandpassing in a few frequency ranges combined with each frequency's amplitude normalization process. This approach aims to equalize the dominance in each spectrum interval. The final result will be a volume merging spectrum to re-unite it into a more balanced seismic spectral cube. The seismic inversion model is applied based on seismic data with better blue spectrum dominance. The data inversion process can be maximized at the upper limit of the seismic spectrum by using this method's output. The improved resolution was further improved using the Stochastic Inversion method by performing a geostatistics-based seismic inversion and realization with the results of a model-based inversion as a trend guide for input data. A variogram model is required to provide an anisotropic constraint on the inversion results. They are using a deterministic inverted cube as the initial lateral variogram model and upscaling well data as the actual vertical variogram model. A total of 5 stochastic seismic inversion realizations have been produced. QC and QA are performed on each inversion result to ensure the inversion results have good reliability. Reliability analysis was carried out using the Method of RMS error, coefficient of determination, and property difference. The forward modeled seismic is compared with the actual seismic, and the inverted acoustic impedance is performed with the actual acoustic impedance. It can be seen that the seismic enhancement method can significantly increase the wellness and seismicity correlation. In addition, stochastic enhancement can effectively improve the correlation of wells by providing options for geostatistical uncertainty outputs. This method provides the advantage of increasing multiple resolutions by maximizing seismic data and carrying out geostatistics realization. So that anomalies and subsurface geological events can be separated and described optimally.