A local-global pattern matching method for subsurface stochastic inverse modeling

Liangping Li, Sanjay Srinivasan, Haiyan Zhou, J. Jaime Gómez-Hernández

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Inverse modeling is an essential step for reliable modeling of subsurface flow and transport, which is important for groundwater resource management and aquifer remediation. Multiple-point statistics (MPS) based reservoir modeling algorithms, beyond traditional two-point statistics-based methods, offer an alternative to simulate complex geological features and patterns, conditioning to observed conductivity data. Parameter estimation, within the framework of MPS, for the characterization of conductivity fields using measured dynamic data such as piezometric head data, remains one of the most challenging tasks in geologic modeling. We propose a new local-global pattern matching method to integrate dynamic data into geological models. The local pattern is composed of conductivity and head values that are sampled from joint training images comprising of geological models and the corresponding simulated piezometric heads. Subsequently, a global constraint is enforced on the simulated geologic models in order to match the measured head data. The method is sequential in time, and as new piezometric head become available, the training images are updated for the purpose of reducing the computational cost of pattern matching. As a result, the final suite of models preserve the geologic features as well as match the dynamic data. This local-global pattern matching method is demonstrated for simulating a two-dimensional, bimodally-distributed heterogeneous conductivity field. The results indicate that the characterization of conductivity as well as flow and transport predictions are improved when the piezometric head data are integrated into the geological modeling.

Original languageEnglish (US)
Pages (from-to)55-64
Number of pages10
JournalEnvironmental Modelling and Software
Volume70
DOIs
StatePublished - Aug 1 2015

Fingerprint

Pattern matching
Statistics
conductivity
Geologic models
modeling
Groundwater resources
Remediation
Aquifers
Parameter estimation
geological feature
subsurface flow
groundwater resource
method
Costs
conditioning
resource management
remediation
aquifer
prediction
cost

All Science Journal Classification (ASJC) codes

  • Software
  • Environmental Engineering
  • Ecological Modeling

Cite this

Li, Liangping ; Srinivasan, Sanjay ; Zhou, Haiyan ; Gómez-Hernández, J. Jaime. / A local-global pattern matching method for subsurface stochastic inverse modeling. In: Environmental Modelling and Software. 2015 ; Vol. 70. pp. 55-64.
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A local-global pattern matching method for subsurface stochastic inverse modeling. / Li, Liangping; Srinivasan, Sanjay; Zhou, Haiyan; Gómez-Hernández, J. Jaime.

In: Environmental Modelling and Software, Vol. 70, 01.08.2015, p. 55-64.

Research output: Contribution to journalArticle

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