## 5 Conclusions  
This research work proposes a superior workflow to automate the fault detection and extraction process. It is built on 3D fault-oriented semblance, and hence can identify the discontinuities even where the traces are locally continuous across the faults. Its application on a faulted reservoir successfully identified and extracted the numerous faults. The results are promising and can play a pivotal role in prospect generation by providing detailed structural and stratigraphic mapping. Equipped with the dip and azimuth arguments, accuracy and efficiency in characterizing and developing mature reservoirs have also been significantly enhanced through this innovative workflow technology. It is fast and robust and is expected to reduce the fault interpretation efforts and time significantly, potentially from days to hours. This ensures that the interpreter utilizes more time on other exploration activities. Some limitations and caution should be kept in mind while executing the workflow: (1) The quality of the results depends upon the true understanding of each step of the workflow in the context of the final objective. This enables the interpreter to identify the problems, find solutions and apply them in a suitable manner. (2) The workflow is computationally intensive and may take a longer time on large volumes. The execution time can be reduced significantly if the computation is distributed over multi-node clusters called GPU-enabled processing.  
Conceptualization, Q.S.I, and M.K.; methodology, Q.S.I, and M.K.; software, Q.S.I, and M.K.; validation, N.A.S., M.K., Y.B., K.Q., and A.A.-S.-M.; formal analysis, Q.S.I, Y.B., N.A. and M.J.; investigation, Q.S.I, M.K., and A.A.-S.-M.; resources, A.H.A.L.; data curation, A.H.A.L.; writing--original draft preparation, Q.S.I; writing--review and editing, Q.S.I, N.A.S., Y.B., and M.K.; visualization, Q.S.I, M.K., and K.Q.; supervision, N.A.S., A.H.A.L., Y.B., M.K., and K.Q.; project administration, A.H.A.L.; funding acquisition, N.A.S., and A.H.A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded and supported through the following projects/grants: Identifying and evaluating the low resistivity reservoir in Malay Basin (YUTP) with cost center (015 LC01-161), Integrated Elastic Properties Prediction via Deep Learning with cost center (015 MD0-059), Artificial  
Figure 13: (**a**) The fault framework, also called fault model in the 3D view, which is the outcome of this research work. The inset (**b**) shows the framework in the planner view. The fault model is ready to be incorporated into static modeling.  
intelligence on seismic attributes with cost center (015 MD0-057), YUTP with cost center (015 LCO-220) and Petroleum Research Fund (PRF) (Grant No.0153 AB-A33).  
Not applicable.  
The data used in this study are confidential and cannot be released.  
The authors are grateful to Universiti Teknologi Petronas, Centre for Subsurface Imaging (CSI), and PETRONAS, Malaysia, for providing the required data and funding for this research work. We would like to acknowledge Schlumberger and the dGB Earth Sciences for donating the academic licenses of Petrel and OpendTect to the Center for Subsurface Seismic Imaging (CSI), Universiti Teknologi PETRONAS, Malaysia. We would like to thank the Geoscience Department, Universiti Teknologi PETRONAS, and all colleagues for their support and constructive input. We also wish to thank the anonymous reviewers whose valuable input improved this work.  
The authors declare no conflict of interest.