バイオグラフィー
Yaxin Liu is a Well Control Product Analyst at Schlumberger Technology Corporation. He obtained his Ph.D. from the University of Tulsa, where he served as a teaching assistant who supports the professor in planning and presenting lessons and teaches undergraduate students basic petroleum engineering lab experiments.
His research is on investigation of counter-current flow during bullheading operations, transient modelling of two-phase flow, fluid mechanics, and well control. He is focusing both on advanced modelling as well as numerical analysis (computational fluid dynamics, machine learning etc.). Yaxin Liu has authored and coauthored more than 25 technical papers. He holds the bachelor’s degree in petroleum engineering and master’s degree in oil and gas well engineering from the Southwest Petroleum University.
研究の興味
Drilling fluids; Rheology; Non-Newtonian; Fluid Mechanics; CFD; Well Control; Two-Phase Flow Modelling; Drilling; Multiphase Flow; Managed Pressure Drilling
Reviewer
仕事内容
Doctor
University of Tulsa
Department of Petroleum Engineering
United States
トピック分野別の貢献
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
現在トレンドになっている記事はどれですか?
研究論文
- Risks and Effects of Medicinal Plants as an Adjuvant Treatment in Mental Disorders during Pregnancy
- The Antioxidant and Antidepressant Properties of Dietary Proteins Derived from Egg and Bean Extracts and Their Acute Toxicity: A Journey from Nutrition to Pharmacognosy
- Unveiling the Hidden Beat: Heart Rate Variability and the Vagus Nerve as an Emerging Biomarker in Breast Cancer Management
- EB Naevi-like Lesion in Infant Bullous Pemphigoid
- Clustering of Three-dimensional (3-D) Objects by Means of Phase- only Digital Holographic Information using Machine Learning
- Malliavin Calculus as Stochastic Backpropagation for Gaussian Latent Models: A Variance-Optimal Hybrid Framework
Advertisement


