## 4 Discussion
### Spatial Geology Model
#### 4.1.1 Geobody Extraction and Interpretation  
Building spatial models, such as the geocellular/geobody, is challenging since the model parameters are difficult to directly measure unless there is very dense well spacing in the entire field, which is nearly impossible [27]. To look for the potential contribution of this work to seismic reservoir characterization, the inverted volume of porosity water saturation and the volume of shale are used to model the reservoir body by applying the cut off analysis to the three petrophysical properties volumes. Figure 17 shows three 3D geobodies representing the distribution of the volume of shale, porosity and water saturation in the study field. The geobody of the volume of shale is mapped with cut-off value <70%, porosity >12% and water saturation >70%. As shown in Figure 17, the geobodies clearly show the distribution of the reservoir in the three properties of entire the field, particularly the thick SC3 reservoir. Note that the SC3 is truncated at the three well locations in order to show the matching between the geobody and the well log and the emergent of reservoir spatial pattern. The continuity of the petrophysical properties is useful for the identification of the sedimentary features where the reservoir was built.  
Figure 16: Inverted porosity inline cross section passing near Well 1, Well 2 and Well 3 location. Well 2 and Well 3 are blind wells.  
The petrophysical properties of the thin SC3A gas reservoir (that only can be seen in well log data), in the study field, are delineated well. The inverted petrophysical properties satisfactorily reconstruct the well log with sufficient frequency of bandwidth and provide acceptable petrophysical properties estimation for the whole 3D seismic volume with confidence. The study shows that the estimated volume of shale, porosity and water saturation is not only able to define the reservoir but also reliable to be used for quantitative reservoir characterization, e.g., volumetric analysis and reservoir simulation. This cut-off analysis also points toward a good performance of the inversion algorithm. The inversion recovered vertical variability very well that is conformable with the well log data, and lateral continuity that is conformable with seismic data. The inversion greatly controls the petrophysical properties of spatial continuity that structurally conform to seismic data. This implied that this inversion can be used as quality control and possibly to control or condition a wide range of facies models, which is needed by complex geologic structure modeling.  
Figure 18 shows individually the geobody of the thin SC3A water saturation is mapped individually in Figure 18 with the seismic section as the background. Recall that water saturation has the poorest correlation compared to other properties and, therefore, has the lowest prediction accuracy; the map will further see the performance of the algorithm in inverting thin reservoirs with weak petrophysical seismic respond. It can be seen that the thin water saturation body coincides with the three available wells and seismic data. The distribution of thin reservoir water saturation is delineated very well to the entire field, implying the high resolution of the inversion result. The hydrocarbon distribution, represented by water saturation, was delineated well, was consistent with drilling, implying the stability of the inversion result. The thin, flaky and distributed hydrocarbon-saturated sand is delineated and mostly pinched out from southeast to northwest, which is helpful for future well drilling placement determination.  
Figure 17: Petrophysical geobody constructed using discovery well cutoff value, Volume of shale <70% (top), porosity >12% (middle), water saturation <70% (bottom). SC3 and SC3A are, consecutively, thick and thin reservoir gas sand.