The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ rates

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l

The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ r

This study investigated the healing effects of topical application of zerumbone, a well‐known anti‐inflammatory compounds loaded on nanostructured lipid carrier gel (Carbopol 940) (ZER‐NLCG) on excisional wounds in streptozotocin‐induced diabetic rats. Diabetic rats with inflicted superficial skin wound were topically treated with ZER‐NLCG, empty NLCG, and silver sulfadiazine cream (SSDC) once daily for 21 days. Wound tissue samples were analyzed for proinflammatory cytokines, namely, interleukin‐6 (IL‐6), interleukin‐1 β (IL‐1β), and tumor necrosis factor‐α (TNF‐α), hydroxyproline contents, catalase,

Background A case-control study was performed to explore eight pro-inflammatory and anti-inflammatory cytokines, namely interleukin (IL)-1α, IL-1Ra (IL-1 receptor antagonist), IL-12, IL-17A, IL-31, IL-33, CXCL10 (C-X-C motif chemokine ligand 10), and CXCL16, with the aim to understand their role in ankylosing spondylitis (AS) pathogenesis and evaluate their utility as markers to differentiate between diseased and healthy individuals. Among these cytokines, IL-31 and CXCL16 have not been well studied in AS. Patients and methods The study included 94 male patients with AS and 91 age-matched control males. Interleukin and chemokine levels were measured using ELISA kits. Results Serum levels of IL-17A, CXCL10, and CXCL16 were significantly ele