Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO's exploration phase is enhanced, and the SHO's hunting mechanisms are modified with PSO's velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.
The discovery of high-risk breast cancer susceptibility genes, such as Breast cancer associated gene 1 (BRCA1) and Breast cancer associated gene 2 (BRCA2) has led to accurate identification of individuals for risk management and targeted therapy. The rapid decline in sequencing costs has tremendously increased the number of individuals who are undergoing genetic testing world-wide. However, given the significant differences in population-specific variants, interpreting the results of these tests can be challenging especially for novel genetic variants in understudied populations. Here we report the characterization of novel variants in the Malaysian and Singaporean population that consist of different ethnic groups (Malays, Chinese, Indian, and other indigenous groups). We have evaluated the functional significance of 14 BRCA2 variants of uncertain clinical significance by using multiple in silico prediction tools and examined their frequency in a cohort of 7840 breast cancer cases and 7928 healthy controls. In addition, we have used a mouse embryonic stem cell (mESC)-based functional assay to assess the impact of these variants on BRCA2 function. We found these variants to be functionally indistinguishable from wild-type BRCA2. These variants could fully rescue the lethality of Brca2-null mESCs and exhibited no sensitivity to six different DNA damaging agents including a poly ADP ribose polymerase inhibitor. Our findings strongly suggest that all 14 evaluated variants are functionally neutral. Our findings should be valuable in risk assessment of individuals carrying these variants.