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2019 SCERF 2nd Annual Affiliates Meeting

Event Details:

Wednesday, May 8, 2019 - Thursday, May 9, 2019

Location

Quadrus Conference Center
2400 Sand Hill Road
Menlo Park, CA 94025
United States

20149 SCERF 2nd Annual Affiliate Meeting


View & download all reports HERE

*Please note: the second half of the day one video is not available due to AV issues. We apologize for any inconvenience this may cause

01. Welcome and Overview Tapan Mukerji

02. Bayesian Evidential Learning & Direct Forecasting: review and progress Jef Caers

03. Application of Bayesian Evidential Learning (BEL) to a real volcanic gas reservoir Céline Scheidt, Kazunari Watanabe, Isami Sumikawa (JAPEX)

04. Automated quantification and updating of geological model uncertainty with borehole data David Zhen Yin

05. Joint uncertainty quantification of global and spatial hydrogeological variables: Application to well catchment prediction in Danish buried valley system Lijing Wang

06. Seismic Bayesian evidential learning: Estimation and uncertainty quantification of sub-resolution reservoir properties Anshuman Pradhan

07. A Monte Carlo based framework for geothermal risk assessments Noah Athens

08. Uncertainty qualification of a naturally fractured buried-hill reservoir using Bayesian evidential learning Junling Wang

09. Characterization of Porous Media Using Graph Laplacians Erik Nesvold

10. Linking Seismic Stratigraphy to Surface Processes in Deltas: Lessons from a Flume Experiment Alex Miltenberger

11. Geomodeling Using Generative Adversarial Networks and Satellite Imagery Erik Nesvold

12. Integrating geostatistical modelling with machine learning for production forecast in shale reservoirs: Case Study From Eagle Ford Alex Bakay

13. Tree-based direct sampling method for surface and subsurface hydrology Chen Zuo

14. Inference problems in complex models and applications to earth sciences Miguel Bosch

15. Geologist-level wireline log shape identification with recurrent neural networks Suihong Song

16. Risk assessment of water injection under fracturing conditions Markus Zechner

17. The probability perturbation method: an old method for a new problem Jef Caers

 

18. Probability perturbation optimizations to create lithological domains from borehole data Francky Fouedjio

19. Conditioning categorical models to hard data using a Gibbs sampling of a truncated multi-variate Gaussian model Francky Fouedjio

20. Uncertainty quantification of implicit geological surfaces: Application to stratigraphy modeling of an iron ore deposit in Western Australia Liang Yang

21. Geologic  uncertainty  quantification  in  offshore  Nile  delta  using  seismic data and Bayesian Evidential Learning Anshuman Pradhan

22. Inference of sub-resolution stacking patterns from seismic data in spatially coupled HMMs Riyad Muradov

23. Uncertain generation and entrapment of CO2 for the exploration of petroleum: prior uncertainty Marcelo Silka

24. Optimization of Decision-Making in the Context of CO2 Storage Monitoring Clothilde Venereau

25. Developing the self-driving car of uncertainty quantification Jef Caers & Yizheng Wang

26 Quantifying uncertainty in unsaturated flow for nuclear repository design – Yucca Mountain Project: Initial thoughts Thu Bui

27. Uncertainty quantification of structural models based on granular simulation Noah Athens

28. Introducing the Patua geothermal field optimization project Noe Pollack

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