H23M:
Recent Advances in Data Assimilation and Optimization for Subsurface Flow Posters


Submit an Abstract to this Session


Session ID#: 52364

Session Description:
There are substantial uncertainties and major computational challenges associated with performance optimization of subsurface flow applications. As data, such as monitoring and seismic data, are collected, there are inevitably mismatches between the simulated and actual reservoir responses. This drives the need for data assimilation, which involves the solution of a computationally demanding inverse problem. Due to the complexity of reservoir responses and high costs associated with large-scale development, e.g., oil/gas production and CO2 sequestration, performance optimization is of great importance. The aim of this session is to improve the understanding of data assimilation and optimization in subsurface flow within each application community and to identify common key challenges to address in the future.

This session seeks abstracts in the following topics: 1) algorithms for inverse modeling and uncertainty reduction; 2) optimization problems in subsurface flow; 3) proxy and reduced-order modeling, proxy based optimization/inversion; 4) other subsurface flow modeling related topics.

Primary Convener:  Bailian Chen, Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States
Conveners:  Dylan R Harp, The Freshwater Trust, Portland, United States and Jianchun Xu, China University of Petroleum (East China), Department of Petroleum Engineering, Qingdao, China
Primary Liaisons:  Bailian Chen, Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States and Jianchun Xu, China University of Petroleum (East China), Department of Petroleum Engineering, Qingdao, China
Chairs:  Bailian Chen, Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States and Dylan R Harp, The Freshwater Trust, Portland, United States
OSPA Liaison:  Jianchun Xu, China University of Petroleum (East China), Department of Petroleum Engineering, Qingdao, China
Index Terms:

1816 Estimation and forecasting [HYDROLOGY]
1846 Model calibration [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Abstracts Submitted to this Session:

Reducing spin-up time for a 3-D surface-subsurface model (352452)
Daniel Erdal, University of Tübingen, Center for Applied Geoscience, Tübingen, Germany, Gabriele Baroni, Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, Eduardo Emilio Sanchez-Leon, University of Tübingen, Center for Applied Geosciences, Tübingen, Germany and Olaf A. Cirpka, University of Tübingen, Tübingen, Germany
Adaptive Multi-Fidelity Data Assimilation for Nonlinear Subsurface Flow Problems (353696)
Qiang Zheng1, Wenjie Xu2, Jiangjiang Zhang1, Laosheng Wu3 and Lingzao Zeng4, (1)Zhejiang University, Hangzhou, China, (2)Zhejiang university, Hangzhou, China, (3)University of California, Department of Environmental Sciences, Riverside, CA, United States, (4)Zhejiang University, College of Environmental and Resource Sciences, Hangzhou, China
Flow Path Tracking Strategy in a Data-Driven Interwell Numerical Simulation Model for Waterflooding History Matching and Performance Prediction with Infill Wells (Invited) (363375)
Hui Zhao1, Lingfei Xu2, Yukun Chen3, Xinping Yang3, Yuhui Zhou2, Lin Cao2 and Zhenyu Guo4, (1)Yangtze University, Petroleum Engineering, Jingzhou, China, (2)Yangtze University, Jingzhou, China, (3)Xinjiang Oilfield company, Kelamayi, China, (4)Occidental Petroleum Co, Houston, United States
History matching of hydraulically fractured tight oil reservoirs considering SRV using improved ES-MDA method (367353)
Jianchun Xu, China University of Petroleum (East China), Department of Petroleum Engineering, Qingdao, China and Wei Zhang, The Pennsylvania State University, State College, United States
Nested Particle Filters for Data Assimilation and Sequential Parameter Optimization (368885)
Maximilian Ramgraber1,2, Matteo Camporese3, Philippe Renard1 and Mario Schirmer1,2, (1)University of Neuchâtel, Centre for Hydrogeology and Geothermics, Neuchâtel, Switzerland, (2)Swiss Federal Institute of Aquatic Science and Technology (Eawag), Water Resources & Drinking Water, Duebendorf, Switzerland, (3)University of Padova, Department of Civil, Environmental and Architectural Engineering, Padova, Italy
History matching of a channelized gas reservoir using ES-MDA integrated with an unsupervised serial denoising autoencoder (373946)
Baehyun MIN1, Sungil Kim2, Seoyoon Kwon1 and Mingon Chu3, (1)Ewha Womans University, Department of Climate and Energy Systems Engineering, Seoul, Korea, Republic of (South), (2)Ewha Womans University, Seoul, South Korea, (3)Seoul National University, Department of Energy Systems Engineering, Seoul, Korea, Republic of (South)
Well Placement Design Using Extremal Optimization for Aberdeen, SD (376475)
Fleford Santos Redoloza, South Dakota School of Mines and Technology, Rapid City, United States and Liangping Li, South Dakota School of Mines and Technology, Rapid City, SD, United States
Reducing the uncertainty of risk assessment in geologic CO2 sequestration by conditional simulation of permeability fields and monitoring data assimilation (409938)
Bailian Chen1, Dylan R Harp2, Zhiming Lu1 and Rajesh Pawar3, (1)Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States, (2)The Freshwater Trust, Portland, United States, (3)Los Alamos National Laboratory, Los Alamos, United States
Evaluating Multiscale Regularization Framework for Well Production Optimization (411233)
Xiang Wang1, Yanfeng He1, Ronald Haynes2 and Qihong Feng3, (1)Changzhou University, Changzhou, China, (2)Memorial University of Newfoundland, St John's, Canada, (3)China University of Petroleum (East China), Qingdao, China
Adaptive Pressure Management in Geological CO2 Storage: Application to A Brine Extraction Field Experiment (414936)
Abdullah Cihan1, Jens T Birkholzer2, Ana Gonzalez-Nicolas3, Michael Commer4, Michael Wilt5, Jonny Rutqvist6, Evan S Um5, Robin Petrusak7, Robert C Trautz8, David E Riestenberg7 and Mike Godec9, (1)Lawrence Berkeley National Laboratory, Energy Geosciences Division, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Energy Geosciences Division, Berkeley, United States, (3)University of Stuttgart, Stochastic Simulation and Safety Research for Hydrosystems (IWS/SC SimTech), Stuttgart, Germany, (4)Lawrence Berkeley National Lab, Berkeley, United States, (5)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (6)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, CA, United States, (7)Advanced Resources International, Arlington, VA, United States, (8)Electric Power Research Institute Paolo Alto, Palo Alto, CA, United States, (9)Golden, CO, United States
Selection of a vertical infill well placement using a multi-modal convolutional neural network trained using reservoir simulation (439575)
Mingon Chu1, Baehyun MIN2, Seoyoon Kwon2 and Sungil Kim3, (1)Seoul National University, Department of Energy Systems Engineering, Seoul, Korea, Republic of (South), (2)Ewha Womans University, Department of Climate and Energy Systems Engineering, Seoul, Korea, Republic of (South), (3)Ewha Womans University, Center for Climate/Environment Change Prediction Research, Seoul, South Korea
A cloud computing approach to massively-parallel distributed stochastic optimization with application to geomechanical reservoir characterization (458885)
Alex Hanna1, Stephen M Moysey2, Larry C Murdoch3, Scott DeWolf3 and Leonid N Germanovich4, (1)Clemson University, Clemson, SC, United States, (2)East Carolina University, Greenville, NC, United States, (3)Clemson University, Environmental Engineering and Earth Sciences, Clemson, SC, United States, (4)Clemson University, Environmental Engineering and Earth Sciences, Clemson, United States

See more of: Hydrology