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Title: Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics With Level-Set Transformation

Abstract

The facies–based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble–based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. Here, we introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability–based geostatistics. A level–set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies–based permeability field. The first example is a two–dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three–dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor inmore » Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [4]; ORCiD logo [5]; ORCiD logo [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Florida State Univ., Tallahassee, FL (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Florida State Univ., Tallahassee, FL (United States)
  4. Jilin Univ., Changchun (China)
  5. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1542938
Alternate Identifier(s):
OSTI ID: 1504520
Grant/Contract Number:  
AC05‐76RL01830; SC0019438; AC02‐05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 55; Journal Issue: 4; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; facies delineation; data assimilation; Kalman filter; level set; inverse modeling; spatial continuity

Citation Formats

Song, Xuehang, Chen, Xingyuan, Ye, Ming, Dai, Zhenxue, Hammond, Glenn, and Zachara, John M. Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics With Level-Set Transformation. United States: N. p., 2019. Web. doi:10.1029/2018WR023262.
Song, Xuehang, Chen, Xingyuan, Ye, Ming, Dai, Zhenxue, Hammond, Glenn, & Zachara, John M. Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics With Level-Set Transformation. United States. doi:10.1029/2018WR023262.
Song, Xuehang, Chen, Xingyuan, Ye, Ming, Dai, Zhenxue, Hammond, Glenn, and Zachara, John M. Mon . "Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics With Level-Set Transformation". United States. doi:10.1029/2018WR023262.
@article{osti_1542938,
title = {Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics With Level-Set Transformation},
author = {Song, Xuehang and Chen, Xingyuan and Ye, Ming and Dai, Zhenxue and Hammond, Glenn and Zachara, John M.},
abstractNote = {The facies–based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble–based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. Here, we introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability–based geostatistics. A level–set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies–based permeability field. The first example is a two–dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three–dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor in Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.},
doi = {10.1029/2018WR023262},
journal = {Water Resources Research},
number = 4,
volume = 55,
place = {United States},
year = {2019},
month = {3}
}

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