## 3 Materials and Methods
### Basin Modeling
The porosity affects the total volume of the pore fluid in the rock matrix, so the higher the water volume, the slower the warming of the layers, due to the high heat capacity of the water. Finally, the lower temperature results in slower maturity and hydrocarbon generation. Figure 8: Simulated sections with the chronostrigraphic horizons (black lines) and the lithofacies: (**a**) Section_01 is parallel with the direction of paleo deltas; (**b**) Section_01A is perpendicular to sediment influx. Horizons were converted into 2D models in Schlumberger PetroMod software. The temperature and the maturity calibration are also important from the pore pressure prediction point of view, because the chemical compaction as an overpressure generation mechanism is controlled by the temperature. Moreover, the hydrocarbon generation and cracking are driven by the thermal maturity and the temperature, so the complete calibration workflow has been carried out. Temperature values were acquired from DST, and the core-based vitrinite reflectance data have been used as maturity proxy (Figure 9). The calibration process was supplemented with the bulk density, because the lithostatic pressure originated from the bulk density, when vertical stress is the highest. The bulk density logs have been upscaled to 10 m spacing. The difference between the lithostriagraphic pressure and the pore pressure creates the effective stress that controls the mechanical compaction and the permeability reduction within the sediments. Data from several wells have been used simultaneously to calibrate the models (Figure 1), but the wells have been drilled for 40 years, which means that the measuring protocol was updated several times. The pore pressure data types have different reliability but have been considered with the same weight. To handle the data diversity and reliability issues, the calculated parameters have not been matched to the individual measured datapoints. The Gaussian square (\(x\)) had been calculated in case of every datapoint, and the repeated calibration-simulation cycles aimed to decrease the sum Gaussian square value (\(d\) is the estimated value; \(m\) is the measured data; and \(\sigma\) is the standard deviation of measured data). \[x^{2}\ =\ \sum_{i\ =\ 1}^{N}\left(\frac{d_{i}-m_{i}}{\sigma_{i}}\right)^{2} \tag{2}\]  
The low permeability is one of the most frequent overpressure triggers. Permeability reduction occurs during the under-compaction, but the cement precipitation also reduces the porosity and the permeability during the chemical compaction [7]. The low source rock permeability prevents the primary migration, so the generated hydrocarbons could hardly leave the pore space that also can generate overpressure. The burial depth is controlled by the input depth maps in simulations that could be uncertain, because the interpretation is reliable above the basement highs, due to the well control. However, the accuracy of the maps reduces in the direction of the deep basin. Moreover, the overpressure generally  
Figure 9: Temperature-depth and vitrinite reflectance-depth 1D extraction from the 2D basin models: (**a**) extraction of the W-09 well on the Section_01; (**b**) extraction of the W-53 well on the Section_01A. Extractions show a good fit between the simulated values and the measured data. Temperature- and vitrinite reflectance-depth plots show that the simulations were successfully calibrated. is the highest at the deep basins, where interval velocities are not available, so the depth conversion could be also uncertain. The above assumptions indicate that the permeability and the depth maps have been considered as the most uncertain input parameters. The calibrated but deterministic best-case models have been supplemented with an enumerative Monte Carlo-based approach to evaluate the effects of the above uncertainties. The permeability and depth are treated as probability variables.