Automated Author ProfileThanudcha Khunmek
Thanudcha Khunmek
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
In this study, reservoir simulation is applied to predict the performance of the fluid rate and bottom-hole pressure. The vertical lift performance is used to estimate the discharge pressure required to lift fluid to the surface. The results from reservoir simulation together with vertical lift performance are used to design the number of pump stages and compare with industrial practice. Water and solution-gas drive reservoir were considered to investigate the pressure behavior and determine the number of pump stages and the pump model. From the results, it was found that the specific gravity of fluid mixture has a significant influent in number of pump stage. In the case of water drive reservoir has proved that the larger aquifer will require a higher number of pump stages than smaller aquifer. For solution-gas drive reservoir, once the solution gas vaporizes as a free gas, it has a significant effect of reducing fluid density, resulting in number of pump stage reduction. Finally, the number of pump stages calculated from conventional design with 10%, 25% and 50% reduction factor was compared with the proposed method. The numbers of pump stages calculated from conventional design with all reduction factors are underestimated when compared with the results from the simulation in the fixed speed application. In the variable speed application, only 50% reduction factor in the conventional design can satisfy the requirement when compared to simulation results. However, overestimation of pump stages happens in many cases when 50% reduction factor is used.
Authors
- Thanudcha Khunmek