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Cloud storage services such as Dropbox, Google Drive, and Microsoft OneDrive provide users with a convenient and reliable way to store and share data from anywhere, on any device, and at any time. The cornerstone of these services is the data synchronization (sync) operation which automatically maps the changes in users' local filesystems to the cloud via a series of network communications in a timely manner. If not designed properly, however, the tremendous amount of data sync traffic can potentially cause (financial) pains to both service providers and users.This paper addresses a simple yet critical question: Is the current data sync traffic of cloud storage services efficiently used? We first define a novel metric named TUE to quantify the Traffic Usage Efficiency of data synchronization. Based on both real-world traces and comprehensive experiments, we study and characterize the TUE of six widely used cloud storage services. Our results demonstrate that a considerable portion of the data sync traffic is in a sense wasteful, and can be effectively avoided or significantly reduced via carefully designed data sync mechanisms. All in all, our study of TUE of cloud storage services not only provides guidance for service providers to develop more efficient, trafficeconomic services, but also helps users pick appropriate services that best fit their needs and budgets.

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In naturally fractured reservoirs, fractures are the main flowing channels, while matrix is the dominant storage space. The oil/water relative permeability curve for the fracture in this kind of reservoir is very important for water-injection field development. In this study, we conducted experiments on the oil/water relative permeability of carbonate cores from Kenkiyak oil field and compared the differences in relative permeability curves between natural matrix cores and artificial-fractured cores. After the fracturing process, the two-phase flow area of tested cores becomes narrower, the permeability of the equal-permeability point gets higher, the relative permeability curve rises or drops more rapidly, and the displacement recovery efficiency decreases. The stress-sensitivity characteristics of the relative permeability curves were also studied on the basis of experiments on naturally fractured cores. With increasing effective confining pressure, the irreducible water saturation increases, the residual-oil saturation changes slightly, the equal-permeability point moves downward, and the displacement recovery efficiency declines. Numerical-simulation results indicate that for a given recovery factor, the water cut would increase more slowly but ultimate recovery factor would decrease using the relative permeability curve under higher confining pressure. Therefore, the water injection should be operated when the reservoir pressure is relatively higher to maintain formation pressure during waterflooding and lower the impact of stress sensitivity accordingly.

The large uncertainty in fracture characterization for shale gas reservoirs seriously affects the confidence in making forecasts, fracturing design, and taking recovery enhancement measures. This paper presents a workflow to characterize the complex fracture networks (CFNs) and reduce the uncertainty by integrating stochastic CFNs modeling constrained by core and microseismic data, reservoir simulation using a novel edge-based Green element method (eGEM), and assisted history matching based on Ensemble Kalman Filter (EnKF). In this paper, the geometry of CFNs is generated stochastically constrained by the measurements of hydraulic fracturing treatment, core, and microseismic data. A stochastic parameterization model is used to generate an ensemble of initial realizations of the stress-dependent fracture conductivities of CFNs. To make the eGEM practicable for reservoir simulation, a steady-state fundamental solution is applied to the integral equation, and the technique of local grid refinement (LGR) is applied to refine the domain grids near the fractures. Finally, assisted-history-matching based on EnKF is implemented to calibrate the DFN models and further quantify the uncertainties in the fracture characterization. The proposed technique is tested using a multi-stage fractured horizontal well from a shale gas field. After analyzing the history matching results, the proposed integrated workflow is shown to be efficient in characterizing fracture networks and reducing the uncertainties. The advantages are exhibited in several aspects. First, the eGEM-based Discrete-Fracture Model (DFM) is shown to be quite efficient in assisted history matching of large field applications because of eGEM’s high precision with coarse grids. This enables simulations of CFNs without upscaling the fractures using continuum approaches. In addition, CFNs geometry can be generated with the constraints of core and microseismic data, and a primary conductivity of CFNs can be generated using the hydraulic fracturing treatment data. Moreover, the uncertainties for CFNs characterization and EUR predictions can be further reduced with the application of EnKF in assimilating the production data. This paper provides an efficient integrated workflow to characterize the fracture networks in fractured unconventional reservoirs. This workflow, which incorporated several efficient techniques including fracture network modeling, simulation and calibration, can be readily used in field applications. In addition, various data sources could be assimilated in this workflow to reduce the uncertainty in fracture characterization, including hydraulic fracturing treatment, core, microseismic and production data.

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Applications of localization range from body tracking, gesture capturing, indoor plan construction to mobile health sensing. Technologies such as inertial sensors, radio frequency signals and cameras have been deeply excavated to locate targets. Among all the technologies, the acoustic signal gains enormous favor considering its comparatively high accuracy with common infrastructure and low time latency. Rangebased localization falls into two categories: absolute range and relative range. Different mechanisms, such as Time of Flight, Doppler effect and phase shift, are widely studied to achieve the two genres of localization. The subcategories show distinguishing features but also face diverse challenges. In this survey, we present a comprehensive overview on various indoor localization systems derived from the various mechanisms. We also discuss the remaining issues and the future work.