David Zhen Yin is the Postdoctoral Researcher working on Stanford-Chevron CoRE Project at SCERF (Stanford Center for Earth Resources Forecasting). He develops Bayesian Evidential Learning for predictions and uncertainty quantification of oil/gas reservoirs.
Prior to joining SCERF, David was a Research Associate at Edinburgh Time-Lapse Project at Heriot-Watt Institute of Petroleum Engineering in Scotland, leading a research project in collaboration with Statoil from 2016 to 2018. He developed the “WELL2SEIS” technique that efficiently integrate 4D seismic with reservoir engineering data to improve reservoir models. This technique has been applied to seven North Sea projects, and has become Statoil’s standard in-house technology. During this period, he was also a Reservoir Technology Consultant at Statoil Research Center in Bergen, Norway. David developed broad experience in working with complex projects involving the industry as well as broad knowledge about the fields.
David obtained his PhD in Reservoir Geophysics from Heriot-Watt University, UK, in 2016, and B.Eng (Petroleum Engineering) from China University of Petroleum in 2011. His research interests include Bayesian Evidential Learning, reservoir modelling and updating, uncertainty quantification, time-lapse reservoir monitoring. David has authored more than 10 publications in peer-reviewed journals and international conferences. He was awarded the SEG Frans and Alice Hammons Award in 2014.
Honors & Awards
Frans and Alice Hammons Award, SEG (2014)
Bachelor of Engineering, Univ of Petroleum East China (2011)
Doctor of Philosophy, Heriot-Watt University (2016)
Improving 4D Seismic Interpretation and Seismic History Matching Using the Well2seis Technique
First EAGE Workshop on Practical Reservoir Monitoring
View details for DOI 10.3997/2214-4609.201700035
Joint interpretation of interwell connectivity by integrating 4D seismic with injection and production fluctuations
View details for DOI 10.2118/174365-MS
A workflow for building surface-based reservoir models using NURBS curves, coons patches, unstructured tetrahedral meshes and open-source libraries
Computers & Geosciences
2018; 121: 11
View details for DOI 10.1016/j.cageo.2018.09.001
- Evaluation of inter-well connectivity using well fluctuations and 4D seismic data JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2016; 145: 533–47
- Enhancement of dynamic reservoir interpretation by correlating multiple 4D seismic monitors to well behavior Interpretation-A Journal of Subsurface Characterization 2015; 3 (2): SP35–SP52
- A method to update fault transmissibility multipliers in the flow simulation model directly from 4D seismic JOURNAL OF GEOPHYSICS AND ENGINEERING 2014; 11 (2)