排序方式： 共有13条查询结果，搜索用时 46 毫秒
1.
Pore structure heterogeneity is a critical parameter controlling mechanical, electrical and flow transport behaviour of rock. Multi-fractal analysis method was used for a heterogeneity comparison of three-dimensional rock samples with different lithology. Six real digital samples, containing three sandstones and three carbonates, were used. Based on the mercury injection capillary pressure test on these samples, we found that the carbonate samples are more heterogeneous than sandstones, but primary results of multi-fractal behaviours for all samples were similar. We show that if multi-fractal is used to evaluate and compare heterogeneity of different samples, one needs to follow some considerations such as (1) all samples must have the same size in pixel, (2) samples volume must be bigger than representative volume element, (3) multi-fractal dimensions should be firstly normalized to a determined porosity value and (4) multi-fractal results should be interpreted based on resolution of the imaging tool (effects of fine scale sub-resolution pores are missed). Results revealed that using normalized fractal dimensions, the real samples were divided to less and high heterogeneous groups. Moreover, the study of scale effect also showed that porous structures of these samples are scale invariant in a wide range of scales (from one to eight times bigger). 相似文献
2.
When fluid flows in porous media under subsurface conditions, significant deformation can occur. Such deformation is dependent on structural and phase characteristics. In this paper, we investigate the effect of multiphase flow on the deformation of porous media at the pore scale by implementing a strongly coupled partitioned solver discretized with finite volume (FV) technique. Specifically, the role of capillary forces on grain deformation in porous media is investigated. The fluid and solid subdomains are meshed using unstructured independent grids. The model is applied for solving multiphase coupled equations and is capable of capturing pore scale physics during primary drainage by solving the Navier-Stokes equation and advecting fluid indicator function using volume of fluid (VOF) while the fluid is interacting with a nonlinear elastic solid matrix. The convergence of the coupled solver is accelerated by Aitken underrelaxation. We also reproduce geomechanical stress conditions, at the pore scale, by applying uniaxial stress on the solid while simultaneously solving the multiphase fluid-solid interaction problem to investigate the effect of external stress on fluid occupancy, velocity-field distribution, and relative permeability. We observe that the solid matrix exhibits elasto-capillary behavior during the drainage sequence. Relative permeability endpoints are shifted on the basis of the external stress exerted. 相似文献
3.
4.
Rezaee Hassan Marcotte Denis Tahmasebi Pejman Saucier Antoine 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(3):893-913
Stochastic Environmental Research and Risk Assessment - This study presents a new approach of generating a set of alternative training images (ATI) to use in patch-based multiple-point simulation.... 相似文献
5.
This article presents new neural network (NN) architecture to improve its ability for grade estimation. The main aim of this
study is to use a specific NN which has a simpler architecture and consequently achieve a better solution. Most of the commonly
used NNs have a fully established connection among their nodes, which necessitates a multivariable objective function to
be optimized. Therefore, the more the number of variables in the objective function, the more the complexity of the NN. This
leads the NN to trap in local minima. In this study, a new NN, in which the connections based on the final performance are
eliminated, is used. Toward this aim, several network architectures were tested, and finally a network which yielded the minimum
error was selected. This selected network has low complexity and connection among nodes which help the learning algorithm
to converge rapidly and more accurately. Furthermore, this network has this ability to deal with the small number of data
sets. For testing and evaluating this new method, a case study of an iron deposit was performed. Also, to compare the obtained
results, some common techniques for grade estimation, e.g., geostatistics and multilayer perceptron (MLP) were used. According
to the obtained results, this new NN architecture shows a better performance for grade estimation. 相似文献
6.
How does DEM resolution affect microtopographic characteristics,hydrologic connectivity,and modelling of hydrologic processes? 总被引：1，自引：0，他引：1 
下载免费PDF全文
下载免费PDF全文 The resolution of a digital elevation model (DEM) is a crucial factor in watershed hydrologic and environmental modelling. DEM resolution can cause significant variability in the representation of surface topography, which further affects quantification of hydrologic connectivity and simulation of hydrologic processes. The objective of this study is to examine the effects of DEM resolution on (1) surface microtopographic characteristics, (2) hydrologic connectivity, and (3) the spatial and temporal variations of hydrologic processes. A puddle‐to‐puddle modelling system was utilized for surface delineation and modelling of the puddle‐to‐puddle overland flow dynamics, surface runoff, infiltration, and unsaturated flow for nine DEM resolution scenarios of a field plot surface. Comparisons of the nine modelling scenarios demonstrated that coarser DEM resolutions tended to eliminate topographic features, reduce surface depression storage, and strengthen hydrologic connectivity and surface runoff. We found that reduction in maximum depression storage and maximum ponding area was as high as 97.56% and 76.36%, respectively, as the DEM grid size increased from 2 to 80 cm. The paired t‐test and fractal analysis demonstrated the existence of a threshold DEM resolution (10 cm for the field plot), within which the DEM‐based hydrologic modelling was effective and acceptable. The effects of DEM resolution were further evaluated for a larger surface in the Prairie Pothole Region subjected to observed rainfall events. It was found that simulations based on coarser resolution DEMs (>10 m) tended to overestimate ponded areas and underestimate runoff discharge peaks. The simulated peak discharge from the Prairie Pothole Region surface reduced by approximately 50% as the DEM resolution changed from 2 to 90 m. Fractal analysis results elucidated scale dependency of hydrologic and topographic processes. In particular, scale analysis highlighted a unique constant–threshold–power relationship between DEM scale and topographic and hydrologic parameters/variables. Not only does this finding allow one to identify threshold DEM but also further develop functional relationships for scaling to achieve valid topographic characterization as well as effective and efficient hydrologic modelling. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
7.
Natural Resources Research - Cleats/fractures play a vital role in gas extraction from coal bed methane reservoirs. As such, fractures are identified in microcomputed tomography images by... 相似文献
8.
An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel- and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory- and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements. 相似文献
9.
Prior to hydrologic modelling, topographic features of a surface are derived, and the surface is divided into sub‐basins. Surface delineation can be described as a procedure, which leads to the quantitative rendition of surface topography. Different approaches have been developed for surface delineation, but most of them may not be applicable to depression‐dominated surfaces. The main objective of this study is to introduce a new depression‐dominated delineation (D‐cubed) method and highlight its unique features by applying it to different topographic surfaces. The D‐cubed method accounts for the hierarchical relationships of depressions and channels by introducing the concept of channel‐based unit (CBU) and its connection with the concept of puddle‐based unit (PBU). This new delineation method implements a set of new algorithms to determine flow directions and accumulations for puddle‐related flats. The D‐cubed method creates a unique cascaded channel‐puddle drainage system based on the channel segmentation algorithm. To demonstrate the capabilities of the D‐cubed method, a small laboratory‐scale surface and 2 natural surfaces in North Dakota were delineated. The results indicated that the new method delineated different surfaces with and without the presence of depressional areas. Stepwise changes in depression storage and ponding area were observed for the 3 selected surfaces. These stepwise changes highlighted the dynamic filling, spilling, and merging processes of depressions, which need to be considered in hydrologic modelling for depression‐dominated areas. Comparisons between the D‐cubed method and other methods emphasized the potential consequences of use of artificial channels through the flats created by the depression‐filling process in the traditional approaches. In contrast, in the D‐cubed method, sub‐basins were further divided into a number of smaller CBUs and PBUs, creating a channel‐puddle drainage network. The testing of the D‐cubed method also demonstrated its applicability to a wide range of digital elevation model resolutions. Consideration of CBUs, PBUs, and their connection provides the opportunity to incorporate the D‐cubed method into different hydrologic models and improve their simulation of topography‐controlled runoff processes, especially for depression‐dominated areas. 相似文献
10.
Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity(UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning(ML) for analyzing wave signals and predict rock properties such as crack density(CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid(RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks(CNN) and Long Short-Term Memory(LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2> 96% with mean square error(MSE) < 25e-4(normalized values). Overall, we found that:(i) performance of both CNN and LSTM is highly promising,(ii) accuracy of the transmitted signals is slightly higher than the reflected signals,(iii) accuracy of 2D signals is marginally higher than 1D signals,(iv)accuracy of horizontal and vertical component signals are comparable,(v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling. 相似文献