# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 5 Conclusions  
We have discussed an integrated approach of predictive modeling for binary and multi- facies classification using a blob-based and labeling method. We conclude that U-Net is able to classify and automate geobodies with a smaller annotated dataset despite using  
Figure 10: The results for multi- facies classification in A Field. (**a**) Two random sections of blended spectral decomposition cubes passing through three key wells showing log pattern analysis in the channelized reservoir interval. Two different time slices in (**b**) display comparisons between multi-attributes as the input (sweetness attributes shown as the example). (**c**) The labeled data prepared by a human interpreter. Results from multi-facies classification (**d**) show that the trained neural network is capable of capturing Class III facies, as indicated in the ellipse, despite dealing with the challenge of facies imbalance.  
different ground truth selection. The blob-based method shall minimize the laborious effort needed by the interpreters from manual labeling work. However, it provides flexibility for a field with less well information, low quality of seismic data, and limited interpreted horizons. U-Net remains versatile for different geological complexities without having the need to change the network architecture extensively.  
SEAM successfully validated synthetic deepwater channel predictions with mean IoU of 85% using the blob-based method. Further validation was cascaded to a real dataset in binary and multi-facies prediction with high accuracy in multi-facies classes (87 to 90% accuracies) using the labeling tool, with lesser cost and further time saving in prediction and automating geobodies, compared to the conventional method. Key hyperparameters such as dropout slow down the learning rate but yield performance improvements during testing when increased gradually from 0 to 0.5. Selection of smaller learning rate and accurate batch size are also the main keys to improving network accuracy. In the future, imbalance facies distribution or data occlusions in a real field can be reduced by exposing more data representation in vertical or horizontal sections, orthogonal slices, stacked sections, or as a volume, together with a customized time warping approach.  
Conceptualization, S.M.S., N.W.T., M.S., E.A.J.J. and A.H.A.L.; methodology, S.M.S., N.W.T. and M.S.; software, S.M.S., N.W.T. and M.S.; validation, S.M.S. and N.W.T.; formal analysis, S.M.S. and N.W.T.; investigation, S.M.S. and N.W.T.; resources, S.M.S., N.W.T. and M.S.; data curation, S.M.S., N.W.T. and M.S.; writing--original draft preparation, S.M.S.; writing--review and editing, S.M.S., N.W.T. and M.S.; visualization, S.M.S.; supervision, E.A.J.J. and A.H.A.L.; project administration, S.M.S.; funding acquisition, E.A.J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Petroleum Research Fund, PETRONAS.  
Not applicable.  
The data used in this study are confidential and cannot be released.  
The authors are grateful to PETRONAS for providing the required data and funding for this research work. We would like to thank the ARCC Department, PETRONAS, and all colleagues from the Geoimaging Technology Department for their support and constructive input. We would also wish to thank [PERSON], [PERSON], and [PERSON] as internal reviewers, whose valuable input improved this work.  
The authors declare no conflict of interest.