Dejen Teklu Asfha

Mr. Dejen Teklu Asfha

Center for Subsurface Imaging, Universiti Teknologi Petronas, Ipoh, Perak, Malaysia
orcid
Ph.D. candidate

Biography

Dejen earned his B.Sc. in Petroleum Engineering with great distinction in 2013 from Mekelle University, Ethiopia. Following his graduation, he joined the same university as an assistant lecturer, where he served for four years. During this time, he was actively involved in teaching and conducting research related to petroleum exploration and production. In 2020, Dejen completed his Master's degree in Petroleum Geoscience (Geophysics) by research at the Center of Excellence in Subsurface Seismic Imaging and Hydrocarbon Prediction (CSI) within the Department of Geosciences at Universiti Teknologi PETRONAS (UTP). Upon earning his Master's degree, he was promoted to Lecturer in the Department of Petroleum Engineering at Mekelle University. Dejen is deeply passionate about applying geomechanics and geophysics to tackle critical challenges in the oil and gas industry. His research primarily focuses on understanding and predicting subsurface conditions, including pore pressure prediction and modeling, overpressure generation mechanisms, shallow water flow geohazard detection, and sand production monitoring. Additionally, he is exploring the potential of Distributed Acoustic Sensing (DAS) seismic technology for monitoring sand production, which provides real-time insights into wellbore conditions and enables timely interventions to mitigate production losses. Currently, Dejen is pursuing his Ph.D. at the Center for Subsurface Seismic Imaging at Universiti Teknologi PETRONAS .

Affiliations

Universiti Teknologi Petronas

Ipoh, Perak, Malaysia

Center for Subsurface Imaging

Honors and Awards

honor

Publication Achievement Recognition 2024

Universiti Teknologi PETRONAS

In 2024, I published two impactful papers: 1. Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring. (https://link.springer.com/article/10.1007/s13202-024-01860-1 ) 2. Predictive pore pressure modeling using well-log data in the West Baram Delta, offshore Sarawak Basin, Malaysia. (rdcu.be/d4Ek1)

Experience

No experience has been shared yet.