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Journal of Porous Media
IF: 1.49 5-Year IF: 1.159 SJR: 0.43 SNIP: 0.671 CiteScore™: 1.58

ISSN Print: 1091-028X
ISSN Online: 1934-0508

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Journal of Porous Media

DOI: 10.1615/JPorMedia.v10.i1.50
pages 71-92

Identification of the Hydraulic Properties of Heterogeneous Rocks from Laboratory Flow-Pump Experiments

Daniel Lesnic
Department of Applied Mathematics, University of Leeds, Leeds LS2 9JT, UK
Simon D. Harris
Rock Deformation Research, School of Earth Sciences, University of Leeds, Leeds, LS2 9JT, UK
Lionel Elliott
Department of Applied Mathematical Studies, The University of Leeds, Leeds LS2 9JT, West Yorkshire, England.
Radu Mustata
Department of Applied Mathematics, University of Leeds, Leeds, LS2 9JT, UK
M. Ben Clennell
Rock Deformation Research, School of Earth Sciences, University of Leeds, Leeds, LS2 9JT, UK
Derek B. Ingham
Centre for CFD, Department of Applied Mathematical Studies, The University of Leeds, Leeds, LS2 9JT, UK; Energy-2050, Faculty of Engineering, University of Sheffield, Sheffield, S10 2TN, UK

ABSTRACT

Transient flow analysis has numerous geophysical and geotechnical applications and it is necessary for the understanding and development of laboratory measurement techniques in rock and soil mechanics, particularly in the determination of their permeabilities. In this paper, we model a laboratory flow-pump permeability test and formulate an inversion technique in order to retrieve homogeneous/spacewise dependent/discontinuous properties of materials. The direct problem is solved using boundary element or finite difference methods, while the recovery of the hydraulic properties of rocks is sought through a genetic algorithm (GA) optimization approach. Well-known constrained optimization routines are often trapped in a local optimum due to a poor initial guess, and further require the calculation of the gradient of a least-squares type functional that is assumed to be differentiable. However, GAs find the global optimum and require neither the calculation of the gradient nor the assumption that the functional be differentiable. Both exact and simulated noisy data are incorporated at optimally selected instants in time through the test, the data measurements used being consistent with the sensitivity analysis that is performed prior to inversion.


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