Theses
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2024
- Sa da Fonseca, J. (2024). Probabilistic assessment of pore pressure prediction with Bayesian Geophysical Basin Modeling [PhD, Stanford University]. https://searchworks.stanford.edu/view/in00000069359
2023
- Wang, Y. (2023). A beautiful marriage between POMDPs and subsurface applications : decision making for subsurface systems [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000019458
- Kanfar, R. (2023). Stochastic geomodelling and analysis of karst morphology [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000033193
- Kashefi, K. (2023). Deep learning algorithms for computational mechanics on irregular geometries [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14732781
- Hall, T. (2023). Efficient greenfield mineral exploration [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000019459
- Wang, L. (2023). Integrating data and models for sustainable decision-making in hydrology [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14641244
2022
- Miltenberger, A. (2022). A measure-theoretic approach to Bayesian hypothesis testing and inversion with geophysical data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14233991
2021
- Yang, L. (2021). Quantifying and visualizing uncertainty of 3D geological structures with implicit methods [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14641244
- Petrov, S. (2021). Seismic image segmentation with deep learning [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/qf836dh0076
- Athens, N. (2021). Stochastic inversion of gravity data in fault-controlled geothermal systems [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13826071
2020
- Pollack, A. (2020). Quantifying Geological Uncertainty and Optimizing Technoeconomic Decisions for Geothermal Reservoirs [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13680060
- Pradhan, A. (2020). Statistical learning and inference of subsurface properties under complex geological uncertainty with seismic data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13753880
2019
- Nesvold, E. (2019). Building informative priors for the subsurface with generative adversarial networks and graphs [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13423377
- Muradov, R. (2019). Inference of Sub-Resolution Stacking Patterns from Seismic Data in Spatially Coupled Models [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/km001pf4033
- Al Ibrahim, M. (2019). Petroleum System Modeling of Heterogeneous Organic-Rich Mudrocks [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13250212
- Park, J. (2019). Uncertainty quantification and sensitivity analysis of geoscientific predictions with data-driven approaches [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13250154
2018
- Mendes, J. (2018). Morphdynamic Analysis and Statistical Synthesis of Geomorphic Data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12746435
- Dutta, G. (2018). Value of Information Analysis for Time-Lapse Seismic Data in Reservoir Development [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12742067
2017
- Li, L. (2017). A Bayesian Approach to Causal and Evidential Analysis for Uncertainty Quantification throughout the Reservoir Forecasting Process [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12137330
- Aydin, O. (2017). A Bayesian Framework for Quantifying Fault Network Uncertainty Using a Marked Point Process Model [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11950552
- Grujić, O. (2017). A Subsurface Modeling with Functional Data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12212152
- Yang, G. (2017). Holistic Strategies for Prediction Uncertainty Quantification of Contaminant Transport and Reservoir Production in Field Cases [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12123097
2016
- Tong, Y. (2016). Basin and Petroleum System Modeling with Uncertainty Quantification: a Case Study on the Piceance Basin, Colorado [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11685974
- Luebbert, L. (2016). Quantitative Analysis of Dissimilarities Between Different Methods of Seismic Inversion to Facies Realizations [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/11956907
- Shin, Y. (2016). Reservoir Modeling with Multiple Geological Scenarios for Deformation of Reservoir Structure and Evolution of Reservoir Properties [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11616857
- Xue, C. (2016). The Application of OPTSPACE Algorithm and Comparison with LMAFIT Algorithm in Three dimensional Seismic Data Reconstruction via Lowrank Matrix Completion [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/11700910
2015
- Lee, J. (2015). Joint Integration of Time-Lapse Seismic, Electromagnetic, and Production Data for Reservoir Monitoring and Management [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11391864
- Satija, A. (2015). Reservoir Forecasting Based on Statistical Functional Analysis of Data and Prediction Variables [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11061336
- Wang, Y. (2015). Rule-Based Reservoir Modeling by Integration of Multiple Information Sources: Learning Time-Varying Geologic Processes [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/11085541
2014
- Xu, S. (2014). Integration of geomorphic experiment data in surface-based modeling: from characterization to simulation [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/10403736
- Jeong, C. (2014). Quantitative Reservoir Characterization Integrating Seismic Data and Geological Scenario Uncertainty [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/10530921
2013
- Suman, A. (2013). Joint inversion of production and time-lapse seismic data: application to Norne field [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9959027
2011
- Bertoncello, A. (2011). Conditioning surface-based models to well and thickness data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9274420
- Park, K. (2011). Modeling Uncertainty in Metric Space [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9100156
- Honarkhah, M. (2011). Stochastic simulation of patterns using distance-based pattern modeling [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9238345
2010
- Wang, J. (2010). A Metropolis sampling method to assess uncertainty of seismic impedance inverted from seismic amplitude data [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/ch090ff5484
- Haugen, M. (2010). Exploring direct sampling and iterative spatial resampling in history matching [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/dn958js5816
- Jia, B. (2010). Linking geostatistics with basin and petroleum system modeling: Assessment of spatial uncertainties [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/mv127hj3223
- Trainor-Guitton, W. (2010). On the value of information for spatial problems in the Earth sciences [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/8652432
- Kuralkhanov, D. (2010). Study of pattern correlation between time lapse seismic data and saturation changes [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/xj886by3185
2009
- Leiva, A. (2009). Construction of hybrid geostatistical models combining surface based methods with object-based simulation: use of flow direction and drainage area [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/8390172
- Suman, A. (2009). Uncertainties in rock pore compressibility and effects on seismic history matching [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/8390194
2008
- Polyakova, E. (2008). A general theory for evaluating joint data interaction when combining diverse data sources [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/7860490
- Fadaei, S. (2008). Streamline assisted history matching of naturally fractured reservoirs using the probability perturbation method [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/7814802