## 6 Seismic Data Integration in the Static Reservoir Model
Reservoir property modeling, including porosity, permeability, and water saturation model, was constrained by the lithology model and driven by trends from seismic reservoir characterization. Figure 12: Horizon probe from pre-stack Acoustic Impedance (Zp) volume covering reservoir interval (**a**) and extracted Reservoir geobody for Zp values lower than 2700 (m/s)(g/cm\({}^{3}\)) considered to match gas-saturated reservoir volume (**b**). Application and comparison of the results of different classification methods for litho-fluid facies prediction were described by [PERSON] and [PERSON] (2017) [32], showing the significant impact of the method applied on the resulting model. The method applied here for lithological differentiation was Unsupervised Neural Net classification based on the self-organizing process. Different input well logs and a number of output classes were tested. The final output was a result of the following input well logs: Dielectric; Deep Resistivity; FMI Conductivity; Sonic; Neutron; Density; P-impedance; and _VpVs_ ratio. The good lithology differentiation was obtained with six classes, five of which were inside the reservoir interval and one additional representing the top reservoir seal. Due to the strong gas effect that influences the log response in all cases, within the classification process, the same lithology (e.g., sand) does not belong to the same class if it is water-saturated or gas-bearing. Therefore, each defined lithology class based on well log data, in addition to the proposed nomenclature, also contains the saturation type (G = gas; W = water), thus presenting litho-fluid classification. In that way, three gas-saturated and two water-bearing classes were defined. Based on the used neighboring gas field analogs, the main lithological difference between classes is in the ratio between the granulometric fractions that are sand, silt, and clay, and that ratio controls rock quality. The precondition to utilize the seismic data for facies distribution is that different lithology classes are associated with reasonable seismic visibility or contrast and the possibility of establishing quantitative interdependence. The correlativity between P-impedance well log, Zp, and Lithology classes is illustrated in Figure 14. In the analyzed case, all the seismic properties that could be utilized for lithology propagation in the model are also influenced by saturation type and cannot be used solely as an indicator of lithological change. Depth-converted pre-stack seismic inversion volumes (Zp, _VpVs_ ratio, DFNN Por) were resampled in the reservoir model and analyzed as main drivers for reservoir property spatial distribution. The initial correlation of lithology classes and input data resulted in the best correlation factor (R\({}^{2}\) over 0.95), with P-impedance and _VpVs_ ratio synthetic well curves. Figure 13: Acquired well logs, Zp from seismic volume, and neural network lithology classification output. Different approaches to seismic data utilization for lithofacies propagation in the reservoir model are available. [PERSON] et al. (2013) [33] and [PERSON] et al. (2020) [34] documented good examples of facies probability prediction based on seismic inversion. Interdependence between Lithology classes and Zp here has been examined by facies probability analysis. The probability of each occurring Lithology class is driven by the histogram-based relationship with Zp as a secondary attribute (Figure 15). Generally, lower impedance values are associated with gas saturation and higher impedance with water saturation. According to the facies probability analysis, for the P-impedance values lower than 1800, the most probable Lithology class is gas-bearing sand, and for the values between 1800 and 2200, it is gas-bearing silty sand. P-impedance range between 2200 and 2800 is characteristic of the poor reservoir but still gas-bearing. There is an uncertain probability zone related to the P-impedance range 2800-3100.