**4. Conclusions**

In this study, the productivity of fishbone wells was determined using three artificial intelligence techniques. The fishbone performance was estimated using a neural network model, fuzzy logic system and radial basis network. The models were developed and validated using more than 250 data sets. The following conclusions can be drawn from this work;


**Author Contributions:** Conceptualization, A.H., S.E. and A.A.; methodology, A.H. and S.E.; software, A.H.; validation, A.H. and S.E.; formal analysis, A.H.; data curation, A.H. and S.E.; writing—original draft preparation, A.H.; writing—review and editing, S.E.; visualization, A.H., A.A. and S.E.; supervision, A.A., and S.E.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors wish to acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for utilizing the various facilities in carrying out this research. Many thanks are due to the anonymous referees for their detailed and helpful comments.

**Conflicts of Interest:** The authors declare no conflict of interest.
