Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.

In the Pannonian Basin, especially in Croatia, there are a small number of wells for which acquired shear-wave (S-wave) velocities have been obtained. Often, quantitative interpretation relies only on compressional-wave (P-wave) velocity data, and S-wave velocity must be modelled. S-wave velocity estimation in combination with other petrophysical data is essential for detailed reservoir characterization. P- and S-wave velocities enable seismic modelling of different saturation states in a reservoir. This paper demonstrates a workflow for S-wave velocity estimation where S-wave velocity data are absent in the gas field and neighbouring fields with the same lithology, based on the Kuster–Toksöz and Xu–Payne models applied to the Pannonian Basin limestone reservoir. The results are calibrated with the P-wave velocity obtained from borehole data and the vertical seismic profile (VSP) S-wave interval velocity. Although rock physics models are idealized analogues of real rocks, a very good correlation was obtained between the modelled and measured P-wave velocity, as well as between the modelled S-wave velocity and the VSP interval velocity. The study also illustrates the problem of defining the pore aspect ratio in zones of increasing shale content. Due to the limited research on the limestones of the Pannonian Basin, these results enable a better understanding of the seismic parameters of the Pannonian Basin limestones. The results indicate that the proposed workflow gives an adequate estimation of S-wave velocities.

Understanding elastic properties of reservoir rocks is essential for seismic modeling under different saturation conditions as well as lithology discrimination. Experiments on elastic properties of limestones are significantly less published compared to siliciclastic sedimentary rocks. The current study presents the results of laboratory measurements on Pannonian Basin limestone cores. The research was carried out for the first time for a hydrocarbon reservoir in the Bjelovar Depression, located in the southern part of the Pannonian Basin. Ultrasonic velocity measurements and determination of dynamic elastic properties were performed on limestone plugs, in dry and saturated condition under different confining pressure steps. Based on the results obtained in laboratory conditions, an empirical relationship between shear wave velocity (Vs) and compressional wave velocity (Vp) has been defined. The saturated samples show an effect of shear modulus weakening, while three samples have a shear modulus strengthening effect. Two models were used in the interpretation of the measured data, the Kuster and Toksöz and the Xu-Payne model. The results show that the Xu-Payne model describes the data well and the dominant pore type system in the limestone samples can been identified. The interpretation revealed an interparticle pore system with a fraction of microcracks from 20% to 35%. The results have helped to understand the elastic properties of limestones from the southern part of the Pannonian Basin, which are necessary for any process of reservoir characterization, such as porosity distribution and permeability variation.