David Zhen Yin is the Research Scientist at Stanford Center for Earth Resources Forecasting. He develops data scientific approaches for prediction, uncertainty quantification and decision making in earth resources exploration and developments (including critical minerals, groundwater, and oil and gas).
David developed broad experiences in working with complex projects involving the academia and industry as well as broad knowledge about the fields. His research delivered several key technologies that have been transferred as in-house technologies in Chevron, Equinor, and KoBold. His research developments have been implemented to a broad spectrum of subjects, from Antarctica bed topography modeling, to critical mineral explorations in Canada/China/US, to North Sea and Gulf of Mexico projects.
Prior to joining Stanford, David was a Research Associate (Reservoir Geophysics) at Edinburgh Time-Lapse Project at Heriot-Watt University in Scotland, leading a research project in collaboration with Equinor from 2016 to 2018. He was also a Technology Consultant at Equinor's Research Center in Bergen, Norway. He was then a Chevron CoRE Postdoctoral Fellow at Stanford from 2018 to 2021.
David received his PhD in Geosciences from Heriot-Watt University, UK, in 2016, and B.Eng from China University of Petroleum in 2011. His research interests include data science for geosciences, geological uncertainty quantification, and decision making under uncertainty. He has authored tens of articles in peer-reviewed journals and international conferences on these topics.
Phys Sci Res Assoc, Geological Sciences
Research Scientist, Stanford University (2022 - Present)
Chevron CoRE Postdoctoral Fellow, Stanford University (2018 - 2021)
Research Associate, Heriot-Watt University (2016 - 2018)
Honors & Awards
Chevron CoRE (Center of Research Excellence) Fellowship, Chevron (2018)
Frans and Alice Hammons Award, SEG (2014)
Boards, Advisory Committees, Professional Organizations
Co-chair, Stanford Earth Postdoc Advisory Council (2019 - 2022)
- Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1) GEOSCIENTIFIC MODEL DEVELOPMENT 2022; 15 (4): 1477-1497
- Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier JOURNAL OF GLACIOLOGY 2021; 67 (261): 75–83
- A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping GEOSCIENCE FRONTIERS 2020; 11 (6): 2297–2308
- Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) GEOSCIENTIFIC MODEL DEVELOPMENT 2020; 13 (2): 651–72
- A Tree-Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling WATER RESOURCES RESEARCH 2020; 56 (2)
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
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
- 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
Joint interpretation of interwell connectivity by integrating 4D seismic with injection and production fluctuations
View details for DOI 10.2118/174365-MS
- A method to update fault transmissibility multipliers in the flow simulation model directly from 4D seismic JOURNAL OF GEOPHYSICS AND ENGINEERING 2014; 11 (2)