## 4 Conclusions  
Being able to quantify the surface wettability in an early stage is vital for every reservoir. This can give detailed information and advice on the EOR, especially CEOR, to increase production. This research proposes a novel empirical formula for predicting surface wettability in the field. There is also the proposal of a novel deep ensemble super learner that can predict NMR T\({}_{2}\)LM. The NMR T\({}_{2}\)LM, when analysed against water saturation data, can indicate the type of wettability condition existing in a reservoir. The average T\({}_{2}\)LM can then be calculated where the mathematical formula is used to predict the type of wetting condition in existence quantitatively. The integration of these computational techniques thus gives a better indication of surface wettability in the absence of any core wettability data and NMR logs.  
Focusing on the findings of this study, the following conclusions can be drawn:  
1. An exponential relationship was established between laboratory Amatt-USBM wettability index and field NMR T\({}_{2}\)LM, achieving a high R\({}^{2}\) of 0.95.
2. An exponential function was used to formulate a mathematical formula that captures domain knowledge on the existence of 100% water but not oil with the net reservoir zones.
3. The relationship between Sw and NMR T\({}_{2}\)LM was used to understand surface wettability conditions in the reservoir. The mean NMR T2 LM was calculated and used in the mathematical equation to predict wettability based on increasing or decreasing Sw.
4. Since surface wettability occurs in pore spaces and between two immiscible fluids, a new feature was engineered using DEN and Sw. This feature improved the performances of all the machine learning models used in this study.
5. The performance of the deep ensemble super learner was superior to the deep learning and ensemble models in predicting NMR T\({}_{2}\)LM. Therefore, the deep ensemble super learner could predict NMR T\({}_{2}\)LM when there are money and time constraints.
6. The deep ensemble super learner is robust and flexible enough to perform better on its own without feature selection.
7. Field deployment of the proposed methodology and deep ensemble super learner was able to successfully estimate the wettability of 3 different reservoir intervals. This result was validated using relative permeability plots.  
Conceptualization, D.A.O.; methodology, D.A.O.; software, D.A.O.; validation, D.A.O. and M.A.A.M.; formal analysis, D.A.O.; investigation, D.A.O.; resources, D.A.O.; data curation, R.G.; writing--original draft preparation, D.A.O.; writing--review and editing, D.A.O., T.O.A.G., M.A.A.M., R.G. and Z.M.A.M.; visualization, D.A.O.; supervision, T.O.A.G., M.A.A.M., R.G. and Z.M.A.M.; project administration, D.A.O.; funding acquisition, T.O.A.G. and M.A.A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Y-UTP grant number [015 LCO-105].  
Not applicable.  
The data and Jupyter Notebooks used in this study is hosted at [[https://github.com/ascotjinr/A-Deep-Ensemble-Super-Learner-to-Predict-Reservoir-Wettability](https://github.com/ascotjinr/A-Deep-Ensemble-Super-Learner-to-Predict-Reservoir-Wettability)]([https://github.com/ascotjinr/A-Deep-Ensemble-Super-Learner-to-Predict-Reservoir-Wettability](https://github.com/ascotjinr/A-Deep-Ensemble-Super-Learner-to-Predict-Reservoir-Wettability)).  
The authors express their sincere appreciation to University Teknologi Petronas and the Centre of Research in Enhanced Oil recovery for financially supporting this work through Y-UTP grant (015 LCO-105).  
The authors declare no conflict of interest.