A new approach to upscaling fracture network models while preserving geostatistical and geomechanical characteristics

A new approach to upscaling two-dimensional fracture network models is proposed for preserving geostatistical and geomechanical characteristics of a smaller-scale “source” fracture pattern. First, the scaling properties of an outcrop system are examined in terms of spatial organization, lengths, connectivity, and normal/shear displacements using fractal geometry and power law relations. The fracture pattern is observed to be nonfractal with the fractal dimension D ≈ 2, while its length distribution tends to follow a power law with the exponent 2 < a < 3. To introduce a realistic distribution of fracture aperture and shear displacement, a geomechanical model using the combined finite-discrete element method captures the response of a fractured rock sample with a domain size L = 2 m under in situ stresses. Next, a novel scheme accommodating discrete-time random walks in recursive self-referencing lattices is developed to nucleate and propagate fractures together with their stress- and scale-dependent attributes into larger domains of up to 54 m × 54 m. The advantages of this approach include preserving the nonplanarity of natural cracks, capturing the existence of long fractures, retaining the realism of variable apertures, and respecting the stress dependency of displacement-length correlations. Hydraulic behavior of multiscale growth realizations is modeled by single-phase flow simulation, where distinct permeability scaling trends are observed for different geomechanical scenarios. A transition zone is identified where flow structure shifts from extremely channeled to distributed as the network scale increases. The results of this paper have implications for upscaling network characteristics for reservoir simulation

Role of natural fractures in damage evolution around tunnel excavation in fractured rocks

This paper studies the role of pre-existing fractures in the damage evolution around tunnel excavation in fractured rocks. The length distribution of natural fractures can be described by a power law model, whose exponent a defines the relative proportion of large and small fractures in the system. The larger a is, the higher proportion of small fractures is. A series of two-dimensional discrete fracture networks (DFNs) associated with different length exponent a and fracture intensity P21 is generated to represent various scenarios of distributed pre-existing fractures in the rock. The geomechanical behaviour of the fractured rock embedded with DFN geometry in response to isotropic/anisotropic in-situ stress conditions and excavation-induced perturbations is simulated using the hybrid finite-discrete element method (FEMDEM), which can capture the deformation of intact rocks, the interaction of matrix blocks, the displacement of natural fractures, and the propagation of new cracks. An excavation damaged zone (EDZ) develops around the man-made opening as a result of reactivation of pre-existing fractures and propagation of wing cracks. The simulation results show that when a is small, the system which is dominated by large fractures can remain stable after excavation given that P21 is not very high; however, intensive structurally-governed kinematic instability can occur if P21 is sufficiently high and the fracture spacing is much smaller than the tunnel size. With the increase of a, the system becomes more dominated by small fractures, and the EDZ is mainly created by the coalescence of small fractures near the tunnel boundary. The results of this study have important implications for designing stable underground openings for radioactive waste repositories as well as other engineering facilities that are intended to generate minimal damage in the host rock mass

TUMK-ELM: A fast unsupervised heterogeneous data learning approach

© 2013 IEEE. Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information from heterogeneous data. However, most of them are with huge time consumption, which obstructs their further application in the big data analytics scenarios, where an enormous amount of heterogeneous data are provided but real-time learning are strongly demanded. In this paper, we address this problem by proposing a fast unsupervised heterogeneous data learning algorithm, namely two-stage unsupervised multiple kernel extreme learning machine (TUMK-ELM). TUMK-ELM alternatively extracts information from multiple sources and learns the heterogeneous data representation with closed-form solutions, which enables its extremely fast speed. As justified by theoretical evidence, TUMK-ELM has low computational complexity at each stage, and the iteration of its two stages can be converged within finite steps. As experimentally demonstrated on 13 real-life data sets, TUMK-ELM gains a large efficiency improvement compared with three state-of-the-art unsupervised heterogeneous data learning methods (up to 140 000 times) while it achieves a comparable performance in terms of effectiveness

Electron quantum interference in epitaxial antiferromagnetic NiO thin films

The electron reflectivity from NiO thin films grown on Ag(001) has been systematically studied as a function of film thickness and electron energy. A strong electron quantum interference effect was observed from the NiO film, which is used to derive the unoccupied band dispersion above the Fermi surface along the Γ-X direction using the phase accumulation model. The experimental bands agree well with first-principles calculations. A weaker electron quantum interference effect was also observed from the CoO film

Sketch Me That Shoe

This project received support from the European Union’s Horizon 2020 research and innovation programme under grant agreement #640891, the Royal Society and Natural Science Foundation of China (NSFC) joint grant #IE141387 and #61511130081, and the China Scholarship Council (CSC). We gratefully acknowledge the support of NVIDIA Corporation for the donation of the GPUs used for this research