## 3 Results and Discussion
### Evaluation of Deep Ensemble Super Learner in Predicting NMR T\({}_{2}\)Lm  
Considering the cost of running an NMR log and the success of machine learning models in reservoir characterisation, a deep ensemble super learner was used to characterise the Coniston well in terms of NMR T\({}_{2}\)LM from wireline logs. The results in Figure 14 specify that in comparison to all selected individual base learners, the deep ensemble super learner exhibited an improved accuracy in NMR T\({}_{2}\)LM prediction of the holdout data. Based on this alone, it is critical to note that even though none of the models overfitted when tested on the holdout data, the super learner relatively performed more robustly. Therefore, further analysis was carried out to evaluate the prediction errors of all the models. Figure 13: Evaluation of the performances of top features using the Gradient Boosting Regressor. All the models' precision and consistency were evaluated based on the error measurements, as illustrated in Figure 15. RMSE, MPD, MAE and MAPE results indicate the Deep Ensemble Super Learner's superiority over all the base learners, indicating its consistency and precision in predicting NMR T\({}_{2}\)LM. However, it is noteworthy to point out that the newly engineered features, based on further sensitivity analysis, improved the prediction performances of all the models. When the top 9 features were used in training the deep ensemble super learner model, it achieved a test R\({}^{2}\) of 0.998, MAE of 2.73, RMSE of 4.878, MAPE of 0.192 and MPD of 0.496. The second best model was the optimised Bagging regressor. This model achieved a test R\({}^{2}\) of 0.0.846, MAE of 14.7423, RMSE of 39.4848, MAPE of 0.45 and MPD of 9.288. When the Bagging is compared to the Deep Ensemble Super Learner, it shows a gargantuan disparity in terms of all the metrics. The superiority of the Deep Ensemble Super Learner is mainly attributed the stacking nature of the base learners where each error is passed through the base learners iteratively until the last model. The results are then averaged and returned as the final model prediction. Based on all the evaluation criteria, it can be concluded that the new features 1, 2, 3, 5 and 7 were highly relevant and improved model efficiency. Figure 14: Comparison of correlation coefficient results of all machine learning models used in this study. Figure 16 below illustrates the kernel density estimate of predicted and actual NMR T\({}_{2}\)LM data. The predicted values are shown in blue, and the actual values are represented in green. From the observation on test data, the deep ensemble super learner predicted NMR T\({}_{2}\)LM are much closer to the actual data. This analysis indicates the ability of the super learner to capture the diverse range of values better than the other models hence being appropriate for deployment in predicting NMR T\({}_{2}\)LM prediction for other wells. The sensitivity analysis, therefore, confirms the evaluation metrics discussed above. The kernel density plot results is further analysed by comparing the actual data points to the predicted data points. Further analysis shown in Figure 17 compares the predicted and actual data points with the above data points representing the Coniston reservoir. Based on the results, it was observed that the Deep Ensemble Super Learner tends to predict reasonably accurate NMR T\({}_{2}\)LM data between 0 and 200 ms. This range refers to the water and oil phases. NMR T\({}_{2}\)LM data points above 250 ms were slightly under-predicted hence contributing to the error evaluations discussed above. Ranges above 250 are typical of the gas phase in a reservoir. Since gas tends to be non-wetting, this under-prediction is relatively inconsequential. The main contributing factor to this under-prediction is the insufficient data within the gas interval of the Coniston well as well as the other reservoir.