## 4 Discussion  
### Spatial Geology Model  
The spatial modeling of geology is important for reservoir characterization. The model parameters, such as porosity, water saturation and the volume of shale, are difficult, if not impossible, to directly measure spatially [27]. Knowing the spatial distribution of the reservoir model parameter measured at well logging is valuable; hence, constructing the quantitative model with a higher resolution than seismic data is essential. In this investigation the average thickness of oil and gas pay zones in the Malay Basin (the study field location) is 10 m or thinner, which challenges to resolve [28]. Evaluating the proposed EEI inversion in terms of mapping the spatial distribution of thin bed reservoirs is interesting.  
Figure 16 shows an inline cross section, of the inverted porosity, that passes near Well-1, Well-2, and Well-3 locations. The porosity section exhibits a good structural match with the seismic data. Figure 3a,b implies that no model overprint or over precise occurred in the inversion process. It means that the geology content is preserved in the inversion result. That is promising since the inversion used no lateral continuity constraint, such as variograms, usually required for a continuous variable, such as porosity and inversion [29]). The inter or extrapolation approaches, which can lead to some degree of calculation error and biasing of the result [30], are not required. The inversion only requires seismic and horizon data to predict the lateral distribution of petrophysical properties, which is very efficient. The applied EEI reflectivity perturbation, which is responsible for local updates, indicatively works very well. It provides a broader solution with less deterioration. The perturbation  
Figure 15: Overlay (upper row) and cross plots (lower row) of the estimated and real volume of shale at well locations. From left to right: (first column) Well 3 (blind), (second column) Well 1 (control), (third column) Well 2 (blind). Both estimated and real data are smoothed. Red lines in the cross plot are the best line between the real and estimated volume of shale.  
modifies the values of EEI reflectivity without losing their lateral continuity, resulting in a high signal-to-noise ratio EEI Porosity output for the entire seismic data volume. The cross section in Figure 16 shows that porosity anomalies found in Well one are extended in almost the entire field, which confirms the seismic interpretation. The anomalies extension is conformable with the two blind well tests (Figures 12a,c and 13a,c). The blind well test results indicate that the proposed inversion method has good generalizability. It is inferred that prediction errors in conventional inversion, which come from seismic data limitation and modeling procedure uncertainty, are reduced by the proposed method.  
The porosity section in Figure 16 shows many high porosity anomalies above and below the SC3 major reservoir. The anomalies were clearly delineated in the area between Well one and Well two. It potentially confirms the unconventional six thin reservoirs which were encountered by the three available wells. These reservoirs are subtle in structure and have relatively weak and discontinuous horizons, and are hence difficult to be inverted by the conventional inversion method. The inversion scheme proposed in this article is capable of resolving the petrophysical properties of unconventional thin bed reservoirs that, due to seismic data limitation and applied method uncertainty, are difficult to be inverted conventionally.