# Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
## 5 Conclusions  
In this study, quantum computation is implemented for well placement optimization and their application to well placement optimization problem is investigated. Experimental results suggested that the QBA and QPSO algorithm can find a better solution than popular algorithms. It is true that due to strong attraction to local attractor QBA has a strong local search capacity that helped it to tackle this multimodal problem more efficiently. It can be inferred that quantum computation can bring improved results compared to other six algorithms. This study showcases that higher exploration rates are not necessary to optimize the well location. However, the correct exploration-exploitation ratio is should be carefully calculated in the task area. In general, a roughly balanced response indicates that the algorithm is more efficient. This seems to be one of the explanations for the successful results of The QPSO and QBA algorithm. The main conclusions of this paper are as follows:  
No global search algorithm can perform well on all kinds of reservoirs. Different reservoir requires different strategy. Quantum techniques increase exploration rate in the search technique, which helps to find better results. There is no statistical difference in the results of QBA and QPSO. The particles are volatile and diverse owning to the local attractor points. Quantum techniques are less susceptible to premature convergence and less likely to be stuck in local optima. High exploitation rate can be linked with lower NPV value. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. Future optimization studies should focus on using the global search algorithm with a local search approach because it may have the advantage of solving well placement optimization problem successfully. Lots of research papers have used proxy models in recent years to replace actual reservoir simulators, and these models have been found to minimize runtime. The accuracy of this alternative model, therefore, depends on its range of sampling. Future research in this area may focus on improving the reliability of this technique. **Author Contributions:** Conceptualization, J.I. ; methodology, J.I. ; software, J.I. ; validation, J.W., P.M.V. and B.M.N. ; formal analysis, J.I. ; investigation, J.I. ; resources, J.I. ; data curation, J.I. ; writing--original draft preparation, J.I. ; writing--review and editing, J.I. and J.W. ; visualization, N.I.H. ; supervision, P.M.V. and B.M.N. ; project administration, P.M.V. ; funding acquisition, B.M.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** The authors would like to thank the petroleum research fund (PRF--0153 AB-A33) for the funding of this research. The authors would like to thank and highly appreciate the project leader associate [PERSON] for supporting the research. The authors also recognize the contribution of the Center of Graduate Studies and Fundamental and Applied Science Department of Universiti Teknologi PETRONAS for their invaluable support. **Conflicts of Interest:** The authors declare no conflict of interest.