## 3 Results and Discussion
### Field Deployment of Deep Ensemble Super Learner and Wettability Determination Methodology  
The proposed methodology will be applied to the entire sandstone reservoir interval of a well from south-eastern coast of the Arabian Peninsula. Field deployment of the proposed methodology is relevant in evaluating the final model. As such, the Deep Ensemble Super Learner is used to predict the T\({}_{2}\)LM for the well. The well has two different reservoirs which is the focus in this deployment. After the prediction, the developed methodology and mathematical model are employed to predict the wettability of the reservoir. The predicted wettability is then compared to the initial wettability established by relative permeability data from the reservoir. For wettability to be established using relative permeability, the following condition needs to be established:  
1. When relative permeability to oil for oil/water displacement (krow) > relative permeability to water (krow) at 50% water saturation, the reservoir is interpreted as water wet. 2. When krow < krow at 50 % water saturation, the reservoir is interpreted as oil wet. 3. When krow = krow at 50 % water saturation, the reservoir is interpreted as neutral wet. To account for uncertainty, it is relevant to note the reservoir types used in this research. One of the reservoirs is a vertical sequence of Early Permian clastic deposits. It has been categorized into three sections. The upper two sections are exclusively fluvial deposits, whilst the lowermost part is mostly deltaic. The Coniston field is made up of Upperarrow Group sandstone from the Berasian period, with interbedded minor areancous sandstone that grades into interbedded siltstone at the bottom. Towards the end of the Jurassic, the reservoir was deposited by the erosion and re-deposition northeast-flowing river systems. This caused parasitic deltaic wedges to be deposited north and west of Coniston. The reservoir data used to train and test the models were from fluvial and deltaic depositional environments. The reservoir that the methodology is being deployed was also deposited by the same depositional system and is an oil bearing sandstone. The Deep Ensemble Super Learner was deployed on the well to predict the T\({}_{2}\)LM of the reservoir interval. The predicted T\({}_{2}\)LM, shown in Figures 18 and 19, is plotted along with some logs to analyse its behaviour in the reservoir. The predicted T\({}_{2}\)LM data was then plotted against to SW data as shown in Figure 20. The well was further subdivided into their distinct reservoirs with the upper reservoir having 2 zones. The results of this sub-division is shown in Figure 21. From the plots, it can be observed qualitatively that all the net pay intervals are water wet. Table 8 shows the quantitative wettability of the interval when the proposed equation is used. Based on the results, the shallower reservoir exhibited moderately water-wet conditions. The deeper reservoir also exhibited weakly water wet condition. The weakly water-wet condition is results from the micropores being wet by oil whiles water wets the macropores. This situation occurs due to low water saturation and low T\({}_{2}\)LM refers to intervals of low permeability as well as a low free fluid index. WithT\({}_{2}\)LM having a direct relationship with these parameters, it may be deduced to be the cause of the low T\({}_{2}\)LM value in that interval. Figure 19: Predicted NMR T\({}_{2}\)LM for Reservoir B in new well. Figure 18: Predicted NMR T\({}_{2}\)LM for Reservoir A in new well. Figure 21: Plot of figures showing the SW against T\({}_{2}\)LM trends in different oil intervals.