小利兰·斯坦福大学(Leland Stanford Junior University)

常直接称为斯坦福大学(Stanford University),为一所坐落于美国加利福尼亚州斯坦福的私立研究型大学,因其学术声誉和创业氛围而获评为世界上最知名的高等学府之一。 自上世纪七十年代,斯坦福成为了美国SLAC国家加速器实验室的所在地,及其中一个高等研究计划署网络(互联网雏形)的起源地。 学校的校园位于硅谷的西北方,邻近帕罗奥图。斯坦福为一所拥有高住宿率及高选择性的大学,当中的研究生课程较本科的多元化。该校也是马丁路德金手写原稿的保存地。 斯坦福培养了不少著名人士。其校友涵盖30名富豪企业家及17名太空员,亦为培养最多美国国会成员的院校之一。斯坦福校友创办了众多著名的公司机构,如:谷歌、雅虎、惠普、耐克、昇阳电脑等,这些企业的资金合计相等于全球第十大经济体系。共81名诺贝尔奖得主现或曾于该校学习或工作。

地球科学科研

 

一、课题方向

Reservoir Geophysics

储层地球物理学

Petroleum Engineering

石油工程

Bayesian Evidential Learning

贝叶斯证据学习

Reservoir modelling and updating

储层建模与更新

Uncertainty quantification

不确定性定量

Time-lapse reservoir monitoring

时移储层监测

 

二、导师背景

地质科学博士后研究员

地球物理博士

 

三、科研内容参考

Bayesian evidential learning

Building informative priors and fast simulation methods for subsurface flow applications

Characterizing heterogeneity of resource plays

Combining data science methods with flow simulation in shale resources development

Global sensitivity analysis for multi-phase flow in the presence of spatially distributed parameters

Groundwater management in Denmark

Immersive visualization techniques for communicating geological uncertainty

Joint quantification of spatial and global uncertainty: application to mitigating agricultural run-off in Denmark

Learning from big data for rapid uncertainty quantification of exploratory ore deposits

Modeling subsurface heterogeneity with surface processes data from flume experiments

Monitoring low-enthalpy heating systems using time-lapse ERT

Optimization under uncertainty for enhanced geothermal systems

Practice of bayesian evidential learning in reservoir uncertainty quantification

Predicting favorable locations for geothermal development

Quantifying uncertainty using level sets with stochastic motion

Recognition of sub-resolution stacking patterns from seismic data

Seismic reservoir property estimation and uncertainty quantification with statistical learning techniques

Stochastic fracture network simulation constrained by geophysical data

Uncertainty quantification and global sensitivity analysis for reactive transport models, application to uranium remediation

Using level sets with stochastic motion for uncertainty visualization and risk assessment for mineral resources

Value of information of time-lapse seismic data in reservoir development

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