Abstract
Original language  English 

Qualification  Doctor Degree 
Awarding Institution 

Award date  17 Jun 1994 
Place of Publication  Espoo 
Publisher  
Print ISBNs  9513846229 
Publication status  Published  1994 
MoE publication type  G4 Doctoral dissertation (monograph) 
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Keywords
 ground water
 flow
 models
 simulation
 fractures
 mathematical models
 numerical methods
 probability theory
 analyzing
 stochastic processes
 Monte Carlo method
 numerical analysis
 computation
 hydrology
 permeability
 crystalline rocks
 heterogeneity
 radioactive wastes
Cite this
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Modeling flow in fractured medium. Uncertainty analysis with stochastic continuum approach : Dissertation. / Niemi, Auli.
Espoo : VTT Technical Research Centre of Finland, 1994. 206 p.Research output: Thesis › Dissertation › Monograph
TY  THES
T1  Modeling flow in fractured medium. Uncertainty analysis with stochastic continuum approach
T2  Dissertation
AU  Niemi, Auli
N1  Project code: YKI4311, Project code: YKI4315
PY  1994
Y1  1994
N2  For modeling groundwater flow in formationscale fractured media, no general method exists for scaling the highly hete rogeneous hydraulic conductivity data to model parameters. The deterministic approach is limited in representing the heterogeneity of a medium and the application of fracture network models has both conceptual and practical limitations as far as sitescale studies are concerned. This study investigates the applicability of stochastic continuum modeling at the scale of data support. No scaling of the field data is involved, and the original variability is preserved throughout the modeling. Contributions of various aspects to the total uncertainty in the modeling prediction can also be determined with this approach. Data from five crystalline rock sites in Finland are analyzed. The issues considered include stochastic versus deterministic nature of the data, statistical similarities and differences between various data sets, types of theoretical distributions, distribution parameters and their confidence limits, spatial trends and autocorrelation structures, role of measuring equipment detection limit in these analyses, and needs for ergodicity assumptions. A stochastic treatment is applied for data from which significant fracture zones are excluded. With the statistical properties determined, groundwater flow in selected regions is modeled by Monte Carlo simulation to obtain estimates of uncertainties in the flow computations, given the amount and location of hydrological data available. For each problem several hundred permeability realizations are generated, for which the flow equation is solved. Theoretical verification simulations comparing the results with analytically derived expressions demonstrate the insignificant role of numerical inaccuracies. Simulations describing realistic bedrock crosssections are carried out both with and without spatial correlation. Uncertainty in the head prediction is higher, and more widely spread, when autocorrelation is taken into account. Predicted flow rates through a twodimensional example crosssection can vary over three orders of magnitude. Conditioning with borehole data in the middle of this section reduces the uncertainty by one order of magnitude. This effect is significant compared to the corresponding reduction achieved by increasing the data base with no regard to the location of this data. The effect of the formula used for interpreting the original well test is also found to be a significant factor in the total analysis.
AB  For modeling groundwater flow in formationscale fractured media, no general method exists for scaling the highly hete rogeneous hydraulic conductivity data to model parameters. The deterministic approach is limited in representing the heterogeneity of a medium and the application of fracture network models has both conceptual and practical limitations as far as sitescale studies are concerned. This study investigates the applicability of stochastic continuum modeling at the scale of data support. No scaling of the field data is involved, and the original variability is preserved throughout the modeling. Contributions of various aspects to the total uncertainty in the modeling prediction can also be determined with this approach. Data from five crystalline rock sites in Finland are analyzed. The issues considered include stochastic versus deterministic nature of the data, statistical similarities and differences between various data sets, types of theoretical distributions, distribution parameters and their confidence limits, spatial trends and autocorrelation structures, role of measuring equipment detection limit in these analyses, and needs for ergodicity assumptions. A stochastic treatment is applied for data from which significant fracture zones are excluded. With the statistical properties determined, groundwater flow in selected regions is modeled by Monte Carlo simulation to obtain estimates of uncertainties in the flow computations, given the amount and location of hydrological data available. For each problem several hundred permeability realizations are generated, for which the flow equation is solved. Theoretical verification simulations comparing the results with analytically derived expressions demonstrate the insignificant role of numerical inaccuracies. Simulations describing realistic bedrock crosssections are carried out both with and without spatial correlation. Uncertainty in the head prediction is higher, and more widely spread, when autocorrelation is taken into account. Predicted flow rates through a twodimensional example crosssection can vary over three orders of magnitude. Conditioning with borehole data in the middle of this section reduces the uncertainty by one order of magnitude. This effect is significant compared to the corresponding reduction achieved by increasing the data base with no regard to the location of this data. The effect of the formula used for interpreting the original well test is also found to be a significant factor in the total analysis.
KW  ground water
KW  flow
KW  models
KW  simulation
KW  fractures
KW  mathematical models
KW  numerical methods
KW  probability theory
KW  analyzing
KW  stochastic processes
KW  Monte Carlo method
KW  numerical analysis
KW  computation
KW  hydrology
KW  permeability
KW  crystalline rocks
KW  heterogeneity
KW  radioactive wastes
M3  Dissertation
SN  9513846229
T3  VTT Publications
PB  VTT Technical Research Centre of Finland
CY  Espoo
ER 