共查询到10条相似文献，搜索用时 125 毫秒
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Sung-Hoon Ji Kyung Woo Park Doo-Hyun Lim Chunsoo Kim Kyung Su Kim William Dershowitz 《Hydrogeology Journal》2012,20(7):1341-1353
The development and implementation of a hybrid discrete fracture network/equivalent porous medium (DFN/EPM) approach to groundwater flow at the Gyeong-Ju low- and intermediate-level radioactive waste (LILW) disposal site in the Republic of Korea is reported. The geometrical and hydrogeological properties of fractured zones, background fractures and rock matrix were derived from site characterization data and implemented as a DFN. Several DFN realizations, including the deterministic fractured zones and the stochastic background fractures, whose statistical properties were verified by comparison with in-situ fracture and hydraulic test data, were suggested, and they were then upscaled to continuums using a fracture tensor approach for site-scale flow simulations. The upscaled models were evaluated by comparison to in-situ pressure monitoring data, and then used to simulate post-closure hydrogeology for the LILW facility. Simulation results demonstrate the importance of careful characterization and implementation of fractured zones. The study highlighted the importance of reducing uncertainty regarding the properties and variability of natural background fractures, particularly in the immediate vicinity of repository emplacement. 相似文献
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Estimating the hydraulic properties of fractured aquifers is challenging due to the complexity of structural discontinuities that can generally be measured at a small scale, either in core or in outcrop, but influence groundwater flow over a range of scales. This modeling study uses fracture scanline data obtained from surface bedrock exposures to derive estimates of permeability that can be used to represent the fractured rock matrix within regional scale flow models. The model is developed using PETREL, which traditionally benefits from high resolution data sets obtained during oil and gas exploration, including for example seismic data, and borehole logging data (both lithological and geophysical). The technique consists of interpreting scanline fracture data, and using these data to generate representative Discrete Fracture Network (DFN) models for each field set. The DFN models are then upscaled to provide an effective hydraulic conductivity tensor that represents the fractured rock matrix. For each field site, the upscaled hydraulic conductivities are compared with estimates derived from pumping tests to validate the model. A hydraulic conductivity field is generated for the study region that captures the spatial variability of fracture networks in pseudo-three dimensions from scanline data. Hydraulic conductivities estimated using this approach compare well with those estimated from pumping test data. The study results suggest that such an approach may be feasible for taking small scale fracture data and upscaling these to represent the aquifer matrix hydraulic properties needed for regional groundwater modeling. 相似文献
3.
Shriram Srinivasan Jeffrey Hyman Satish Karra Daniel O’Malley Hari Viswanathan Gowri Srinivasan 《Computational Geosciences》2018,22(6):1515-1526
We propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. The method allows for the level of accuracy to be a user-controlled parameter. 相似文献
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We present a discussion of the state-of-the-art on the use of discrete fracture networks (DFNs) for modelling geometrical characteristics, geomechanical evolution and hydromechanical (HM) behaviour of natural fracture networks in rock. The DFN models considered include those based on geological mapping, stochastic generation and geomechanical simulation. Different types of continuum, discontinuum and hybrid geomechanical models that integrate DFN information are summarised. Numerical studies aiming at investigating geomechanical effects on fluid flow in DFNs are reviewed. The paper finally provides recommendations for advancing the modelling of coupled HM processes in fractured rocks through more physically-based DFN generation and geomechanical simulation. 相似文献
5.
Upscaling methods such as the dual porosity/dual permeability (DPDP) model provide a robust means for numerical simulation of fractured reservoirs. In order to close the DPDP model, one needs to provide the upscaled fracture permeabilities and the parameters of the matrix-fracture mass transfer for every fractured coarse block in the domain. Obtaining these model closures from fine-scale discrete fracture-matrix (DFM) simulations is a lengthy and computationally expensive process. We alleviate these difficulties by pixelating the fracture geometries and predicting the upscaled parameters using a convolutional neural network (CNN), trained on precomputed fine-scale results. We demonstrate that once a trained CNN is available, it can provide the DPDP model closures for a wide range of modeling parameters, not only those for which the training dataset has been obtained. The performance of the DPDP model with both reference and predicted closures is compared to the reference DFM simulations of two-phase flows using a synthetic and a realistic fracture geometries. While the both DPDP solutions underestimate the matrix-fracture transfer rate, they agree well with each other and demonstrate a significant speedup as compared to the reference fine-scale solution.
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Groundwater flow in fractured rocks is modeled using a coupled model based on the domain decomposition method. In the model, the fractured porous medium is divided into two non-overlapping sub-domains. One is the rock matrix, in which the medium is described using a continuum model. The other consists of deep fractures and fissure zones, where the medium is described using a discrete fracture network (DFN) model. The two models are coupled through the continuity of the hydraulic heads and fluxes on the common boundaries. The coupled model is used to simulate groundwater flow in a hydropower station. The results show that the model simulates groundwater levels that are in agreement with the measured groundwater levels. Furthermore, the model’s parameters relating to deep fractures and fissure zones are verified by comparing three different models (the continuum model, coupled model, and DFN model). The results show that the coupled model can capture and duplicate the hydrogeological conditions in the study domain, whereas the continuum model overestimates and the DFN model underestimates the measured hydraulic heads. A sensitivity analysis shows that fracture aperture has a considerable effect on the groundwater level. So, when the fracture aperture is large, the coupled model or DFN model is more appropriate than the continuum model in the fracture domain. 相似文献
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L. Y. Hu Y. Liu C. Scheepens A. W. Shultz R. D. Thompson 《Mathematical Geosciences》2014,46(2):227-240
The multiple-point simulation (MPS) method has been increasingly used to describe the complex geologic features of petroleum reservoirs. The MPS method is based on multiple-point statistics from training images that represent geologic patterns of the reservoir heterogeneity. The traditional MPS algorithm, however, requires the training images to be stationary in space, although the spatial distribution of geologic patterns/features is usually nonstationary. Building geologically realistic but statistically stationary training images is somehow contradictory for reservoir modelers. In recent research on MPS, the concept of a training image has been widely extended. The MPS approach is no longer restricted by the size or the stationarity of training images; a training image can be a small geometrical element or a full-field reservoir model. In this paper, the different types of training images and their corresponding MPS algorithms are first reviewed. Then focus is placed on a case where a reservoir model exists, but needs to be conditioned to well data. The existing model can be built by process-based, object-based, or any other type of reservoir modeling approach. In general, the geologic patterns in a reservoir model are constrained by depositional environment, seismic data, or other trend maps. Thus, they are nonstationary, in the sense that they are location dependent. A new MPS algorithm is proposed that can use any existing model as training image and condition it to well data. In particular, this algorithm is a practical solution for conditioning geologic-process-based reservoir models to well data. 相似文献