HR: 11:05h
AN: H42B-04    [Abstracts]
TI: Multiple-Point Geostatistics and Near-Surface Geophysics for Modeling Heterogeneity in a Coastal Aquifer
AU: * Trainor, W J
EM: wtrainor@stanford.edu
AF: Program of Earth, Energy, & Environmental Science, 397 Panama Mall, Stanford, CA 94305, United States
AU: Knight, R J
EM: rknight@stanford.edu
AF: Geophysics Department, 397 Panama Mall, Stanford, CA 94305, United States
AU: Caers, J K
EM: jcaers@stanford.edu
AF: Energy Resources Engineering, 367 Panama Mall, Stanford, CA 94305, United States
AB: In order to effectively manage groundwater resources, water agencies have begun to incorporate precipitation, temperature, stream-gauge, land-cover and groundwater level data into their aquifer models. For Western States in particular, stored groundwater is an important provider for agriculture and human consumption. But the estimates of groundwater quantity are arguably the most uncertain in the water balance equation. Current practice in constructing subsurface models relies on substandard and incomplete data due due, in large part, to budgetary constraints. Once a final model has been developed, the possible inaccuracies in the geological scenarios are rarely examined or investigated. How wrong can the subsurface model be while still giving accurate prediction results? How sensitive is the model response to perturbations in the subsurface parameters and long-term irrigation, precipitation and recharge conditions? This study examines these questions through a sensitivity analysis. The "working" aquifers of California's agricultural central coast were used as analog systems for the construction of this sensitivity study. The fluvial geologic interpretations of these coastal aquifer systems were used in Boolean (object-oriented) and multiple-point geostatistical algorithms to create many alternative permeability fields, reflecting the uncertainty in the spatial distribution and geological scenario of the subsurface permeability field. Two sets of models were created using SNESIM, a multiple-point geostatistical algorithm. SNESIM is able to generate a stochastic facies realization using a training image (TI -- a conceptual idea of geologic system) with rotation and affinity maps. The first set of models are higher entropy, representing less continuous clay layers. These were created from a TI of clay ellipses (which was created using GSLIB Ellipsim program). The second set of models are more heterogeneous by using a fluvial TI within SNESIM. All the realizations were conditioned to the same synthetic drillers' logs. Drillers' log were the most commonly used data in creating the flow models used by water managers. This illustrates the wide range of different subsurface scenarios that can be fit to the incomplete and interpreted lithology information in drillers' logs. The second phase of the study investigates how different temporal and spatial pumping schemes for different permeability fields result in varying seawater intrusion rates and the efficiency of artificial recharge. Transient, multiphase flow simulations were performed in Eclipse on all subsurface realizations. These results emphasize the importance of understanding the heterogeneity at the regional scale. Furthermore, simulation results are shown for different aquitard/aquifer geometries at the coastline, which heavily influence seawater intrusion rates. The third phase of the study uses geophysical forward models to determine if near-surface geophysical techniques are able to differentiate between the different geometries of heterogeneity. This lays the foundation for a cost-benefit analysis of the acquisition of additional subsurface information. Considering the sensitivity study results, what is the benefit of acquiring either well-log information or surface geophysical data? Would this information help in prediction or planning for water-shortage (or water-management) for the different scenarios?
DE: 0925 Magnetic and electrical methods (5109)
DE: 1805 Computational hydrology
DE: 1829 Groundwater hydrology
DE: 1869 Stochastic hydrology
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting