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Sensitivity study on hydraulic well testing inversion using simulated annealing.

S Nakao1, J Najita, K Karasaki

  • 1Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA. nakao@gsj.go.jp

Ground Water
|January 8, 2009
PubMed
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Cluster variable aperture simulated annealing models fluid flow in fractured formations using hydraulic test data. Optimal cluster size, 20-40% of spatial correlation range, improves model accuracy and estimates heterogeneity scale.

Area of Science:

  • Geosciences
  • Hydrogeology
  • Computational Modeling

Background:

  • Fluid flow in fractured formations is complex and challenging to model accurately.
  • Transient pressure data from hydraulic tests are crucial for understanding subsurface fluid dynamics.
  • Traditional inversion techniques often face non-uniqueness issues in hydraulic modeling.

Purpose of the Study:

  • To develop and apply a Cluster Variable Aperture (CVA) simulated annealing technique for fluid flow modeling in fractured formations.
  • To investigate the relationship between heterogeneity scale and optimal cluster size for improved inversion convergence.
  • To enhance the accuracy of fluid flow models using transient pressure data.

Main Methods:

  • Utilized Cluster Variable Aperture (CVA) simulated annealing as an inversion technique.

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  • Represented 2D fracture networks as lattices of discrete fracture elements with variable apertures.
  • Iteratively adjusted fracture element apertures within clusters to match observed pressure transients.

    • Main Results:

    • Identified an optimal cluster size range of 20% to 40% of the spatial correlation range for spatially correlated transmissivity fields.
    • Successfully applied the CVA method to invert hydraulic test data from the Raymond test site.
    • Estimated the practical range of spatial correlation at the Raymond test site to be 5 to 10 meters.

    Conclusions:

    • CVA simulated annealing is an effective technique for constructing fluid flow models of fractured formations.
    • Optimizing cluster size based on heterogeneity scale significantly enhances inversion convergence and model reliability.
    • The study provides a method for estimating the scale of heterogeneity in fractured reservoirs.