Investigate a Gas Well Performance Using Nodal Analysis

Gas condensate well has unique reservoir characteristics and ups and downs in well behaviour affect the production rate significantly. A proper optimization can reduce the operating cost, maximize the hydrocarbon recovery and increase the net present value. Well level optimization can be achieved through optimizing well parameters, such as wellhead, tubing size, and skin factor. All of these factors have been investigated using a real field of Thrace Basin and PROSPER simulation program. The history matching data are validated to identify the future performance prediction for the same reservoir deliverability following the period changes. Therefore, predicted results are compared and validated with measured field data to provide the best production practices. Moreover, the results show that the skin factor has a large influence on the production rate by 45% reduction. The reduction in the reservoir pressure declines the production rate dramatically resulted in 70% decline. While manipulating the wellhead pressure shows minor decline compare to tubing size that does not show any significant change to production rate

Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: Raw nest beach monitoring data gathered at sites outside of the national park will be made available upon request to the board of Renatura Congo ([email protected]). Raw nest beach monitoring data gathered at sites inside Conkouati-Douli National Park and code to run models are available from the Dryad Digital Repository: doi:10.5061/dryad.prr4xgxp3Monitoring how populations respond to sustained conservation measures is essential to detect changes in their population status and determine the effectiveness of any interventions. In the case of sea turtles, their populations are difficult to assess because of their complicated life histories. Ground-derived clutch counts are most often used as an index of population size for sea turtles; however, data are often incomplete with varying sampling intensity within and among sites and seasons. To address these issues, we: (1) develop a Bayesian statistical modelling framework that can be used to account for sampling uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback turtle [Dermochelys coriacea] and olive ridley turtle [Lepidochelys olivacea]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo’s single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented.Darwin InitiativeDepartment for Environment, Food and Rural Affairs (Defra)Research Englan