Su Wang |c | Best Research Article Award

Posted on by ScienceFather

Mr. Su Wang | Rock Mechanics | Best Research Article Award

Student, China University of Petroleum, Beijing, China

Su Wang is a dedicated researcher at the China University of Petroleum in Beijing, specializing in fiber-optic monitoring for hydraulic fracturing. Under the supervision of Mian Chen, Wang has made significant contributions to understanding the complexities of hydraulic fracture dynamics. His innovative research employs advanced fiber-optic technologies to monitor strain and deformation in geological formations, providing critical insights that enhance the efficiency and safety of hydraulic fracturing processes.

Profile

Orcid

Best Research Article Award Application: Su Wang

Su Wang, a researcher from the China University of Petroleum in Beijing, has demonstrated significant expertise in the field of fiber-optic monitoring for hydraulic fracturing. His contributions are pivotal, particularly in understanding the evolution characteristics of fiber-optic strain and its implications for hydraulic fracture behavior. Notably, his publication β€œStudy on the Evolution Characteristics of Fiber-Optic Strain Induced by the Propagation of Bedding Fractures in Hydraulic Fracturing” showcases innovative methodologies that enhance our understanding of fracture dynamics, positioning him as a leader in this emerging research area. Wang’s work is characterized by rigorous experimental design and insightful analyses, highlighting the interplay between geological structures and fiber-optic technology.

Despite these strengths, there are areas for potential improvement. Expanding the scope of research to include multi-dimensional data analysis and integrating machine learning techniques could further enhance the predictive capabilities of his studies. Additionally, fostering interdisciplinary collaborations may provide broader insights into the application of fiber-optic monitoring in various geological settings.

In conclusion, Su Wang’s research represents a valuable contribution to the field of hydraulic fracturing and fiber-optic monitoring. His innovative approach and rigorous methodology make him a strong candidate for the Best Research Article Award. By addressing the identified areas for improvement, he has the potential to elevate his research impact even further, paving the way for advancements in both academic and practical applications within the petroleum industry.

Publications Top Notes

Wang S, Chen M, Lv J, et al. Study on the Evolution Characteristics of Fiber-Optic Strain Induced by the Propagation of Bedding Fractures in Hydraulic Fracturing [J]. Petroleum Science, 2024.

Wang S, Chen M, Hao Y, et al. Evolution Mechanism of Deviated Well Fiber-optic Strain Induced by Single-fracture Propagation during Fracturing in Horizontal Wells [J]. Engineering Fracture Mechanics, 2024. In press.

Wang S, Chen M, Chang Z, et al. Experimental Study on Indoor Multi-cluster Fracturing Based on Distributed Fibre-optical Monitoring [C]. International Geomechanics Symposium, 2023.

Wang S, Chen M, Lv J. Experimental Study on Fiber Strain Evolution Induced by Multi-fracture Propagation in Hydraulic Fracture [J]. China Petroleum Machinery, 2024, 52(8): 101-107.

Wang S, Chen M, Lv J, et al. Characteristics of Fiber-Optic Strain Evolution in Vertical Adjacent Well Induced by Hydraulic Fracture Propagation in Horizontal Well [J]. Journal of Northeast Petroleum University, 2024, 48(4): 1-11.

Zhang K, Ku H, Wang S, et al. Distributed Acoustic Sensing: A Promising Tool for Finger-band Anomaly Detection [J]. Photonics, 2024. In press.

Wang Q, Chen M, Wang S. Dynamic Fracture Conductivity Considering Stress-deformation Behavior of Proppants in Shale Formation [C]. ARMA, 2022.

Hao Y, Chen M, Wang S, et al. Monitoring the Propagating Process of Hydraulic Fracture with Fiber Optic Strain and Strain Rate Technology [C]. ARMA, 2024.

Fang Z, Chen M, Wang S, et al. Geometry of Hydraulic Fractures in Fractured Horizontal Wells in Shale Reservoirs of Jimsar Sag, Junggar Basin [J]. Xinjiang Petroleum Geology, 2024, 45(01): 72-80.

Zhang K, Chen M, Zhao C, Wang S, et al. A Continuous and Long-Term In-Situ Stress Measuring Method Based on Fiber Optic. Part I: Theory of Inverse Differential Strain Analysis [J]. Petroleum Science, 2024, 21(2): 1171-1189.

Zhang Q, Hou B, Chang Z, Wang S, et al. Experimental Study on True Triaxial Hydraulic Fracturing Based on Distributed Fibre-Optical Monitoring [C]. International Geomechanics Symposium, 2022.

Hanan Samadi | Geotechnics | Excellence in Research Award

Posted on by ScienceFather

Ms. Hanan Samadi | Geotechnics | Excellence in Research Award

Master of science in the filed of engineering geology, University of Tehran, Iran

Ms. Hanan Samadi is an emerging scholar in the field of geotechnical engineering, specializing in tunneling and underground structures. With a robust academic background and significant contributions to research in machine learning applications in geotechnics, she has established herself as a promising expert in her field.

πŸŽ“ Education:

Ms. Samadi obtained her M.Sc. in Engineering Geology from the University of Tehran in 2021, where she graduated with a stellar GPA of 4.0/4.0 for her major. Her thesis, supervised by Dr. Jafar Hassanpour and Dr. Jamal Rostami, focused on the use of artificial intelligence to investigate EPB operating parameters. She completed her B.Sc. in Geology Science from Payame Noor University in 2018.

πŸ‘¨β€πŸ« Experience:

Ms. Samadi has served as a course mentor and workshop mentor at various prestigious institutions, including Amirkabir University, Shahid Beheshti University, and the Iranian Mining Engineering Organization. Her mentorship has covered topics such as machine learning in tunneling, mechanized excavation, and the application of artificial neural networks in construction.

πŸ”¬ Research Interests:

Her research interests encompass tunneling, mechanized excavation, rock mechanics, soil mechanics, and geotechnics. She is particularly focused on developing machine learning and deep learning algorithms to enhance geotechnical and tunneling processes.

πŸ† Awards:

Ms. Samadi has received numerous accolades, including the Young Scientists Festival award for developing new tunneling software and multiple scholarships for her academic achievements. She was also recognized for her exceptional thesis at the University of Tehran.

πŸ“š Publications Top Notes:

Estimation of settlement of pile group in clay using soft computing techniques, Geotechnical and Geological Engineering, 2024. Cited by 28.

Maximum surface settlement prediction in EPB TBM tunneling using soft computing techniques, Journal of Physics: Conference Series, 2021. Cited by 14.

Tunnel wall convergence prediction using optimized LSTM deep neural network, Geomech and Eng, 2022. Cited by 7.

Developing the empirical models for predicting the EPB operating parameters in strong Limestone, Iranian Journal of Engineering Geology, 2022. Cited by 6.

Soil Classification Modelling Using Machine Learning Methods, CCITAI, 2022. Cited by 5.

Utilization of rock mass parameters for performance prediction of rock TBMs using machine learning algorithms (publication details incomplete).