## 3 Materials and Methods
### Pre-Screening Input and Calibration Parameters (Pressure, Porosity, and Permeability)
#### 3.2.2 Porosity, Permeability, and Compaction Trends  
Next to the pore pressure, the porosity, permeability and compaction trends have been calculated during the numerical simulation [50].  
Core-based measured and well log-derived porosity values belong to a given depth. The effective stress value can be calculated to the specific depth if pore pressure data is available near the porosity value. Continuous bulk density logs can deliver the lithost stratigraphic pressure values when the vertical stress is bigger than the horizontal stress. The difference between the lithost stratigraphic and the pore pressure value is the effective stress that controls the compaction of the sediments [58].  
The continuous bulk density log was available only in the case of one well from the evaluated dataset, so the lithost stratigraphic pressure calculation was not feasible; bulk density log has been upscaled and applied as calibration data to lithostatic pressure simulation. Moreover, the pressure data deficiency has been mentioned previously at the pressure database. As a result of the insufficient dataset, the porosity-depth trends were calculated first, and after that, they were transformed into porosity-effective stress relationships. A porosity and a permeability measurement program have been carried out in case of underrepresented lithost stratigraphic units. Shale-rich formations (delta slope, prodelta, and pelagic sediments) were underrepresented, due to its low hydrocarbon reservoir  
Figure 7: The most typical pore pressure proxies that have been used for the calibration of basin models. The overlapping intervals can delineate the most probable magnitude of the pore pressure. Mud weight drop is the process when the pore pressure exceeds the equivalent mud pressure (calculated from mud weight) and the pore fluids could enter into the borehole that lead to the dilution of the mud weight with less dense brines (mud weight drop down) or hydrocarbons (gas kick).  
perspectives [59; 60]. The measure program delivered that the finalized porosity-depth and porosity-effective stress trends rely on measured core-based and calculated well log-derived porosity values. The well log-based porosity values have a few centimetres of vertical spacing, the high resolution is not an issue in case of regional compaction trends, but the huge dispersion of porosity data could be. The well log-based porosity values were upscaled with 10 m spacing.  
Calculation of the porosity-depth trends rely on [PERSON]'s law [61], so the calculated or measured porosity values were the known part of the compaction equations, and [PERSON]'s factor has been back calculated. [PERSON]'s factor and the initial surface porosity let the porosity-depth trend extrapolate for the missing intervals, which were not sampled by calculated or measured values [61]  
\[\phi\ =\ \phi_{0}(e^{-kz_{e}}), \tag{1}\]  
where \(\phi\) is the porosity, \(\phi_{0}\) is the initial porosity, \(k\) is the [PERSON]'s factor, and \(z_{e}\) is the equivalent hydrostatic depth. Not only the porosity values decrease with the burial, the permeability values also reduce during the compaction. The size of the pore throats is decreasing in the first phase; after that, the pore throats are closed, and the pore connectivity decreases drastically [62; 63]. To properly simulate this dual process, Kozeny-Carman-type poropperm relationships have been implemented in the numerical simulation [50] that rely on two different exponential factors, one for the early phase burial and the second one for the pore separation phase.