Petrophysical characterisation of tight sandstone gas reservoirs using nuclear magnetic resonance: a case study of the upper Paleozoic strata in the Kangning area,eastern margin of the Ordos Basin,China

Residual and movable porosity are significant parameters for characterising petrophysical properties, especially in tight reservoirs. Eight tight sandstone samples from the upper Paleozoic gas-bearing strata in the Kangning area, from the eastern margin of the Ordos Basin, were analysed using nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), petrography, and porosity and permeability tests. The lithology and pore types were identified and classified using petrography and SEM. The residual and movable porosity were obtained with NMR. In addition, NMR was used to visualise pore structure and pore size distribution. The results suggest that the upper Paleozoic sandstones in the study area mainly comprise feldspathic litharenite and litharenite. The sandstone porosity and permeability are low, with means of 5.9% and 0.549 mD, respectively. Four pore types exist in the tight sandstones: residual primary pores, grain dissolution pores, micropores (clay-dominated) and microfractures. The T₂ spectra under water-saturated conditions correlate with pore size and can be used to distinguish small and large pores based on the transverse relaxation time cutoff value of 10 ms, which corresponds to a pore diameter of 0.232 μm. Small pores account for 72% of the pores in the tight sandstones. The continuous bimodal T₂ spectra suggest good connectivity between small and large pores, despite the low porosity and permeability. In this study, the movable porosity of the major tight sandstone gas reservoirs is higher than the residual porosity, which confirms the effective evaluation of movable porosity to tight sandstone reservoirs, based on NMR experiments.     

Local site effect of a clay site in Shiraz based on seismic hazard of Shiraz Plain

Propagation of seismic waves through soil layers would drastically change the frequency content and amplitude-based features of ground motions at the surface. These alterations are known as seismic site effects. Computation of site effects of high-populated areas such as large cities is of great importance (e.g., it is used in development of seismic microzonation of a region). Shiraz is one of the most populous cities of Iran and is located in a high seismic hazardous region. A representative clay site in this city is selected to assess local site effects. The time series and random vibration theory procedure in the frequency domain are implemented to analyze the aforementioned site. Furthermore, the nonlinear dynamic soil behavior is simulated by the equivalent linear method and the nonlinear method via DEEPSOIL program. Three types of soil column uncertainties such as shear wave velocity, modulus reduction, and damping ratio of soil layers as well as depth of underlying rock half-space (D _bed) are considered herein. The mean amplification and standard deviation of natural logarithm of amplification factors are computed for a variety of analysis types. The results of the current study show that the computed mean and standard deviation of amplification factor in ln units by considering only V _S uncertainty are in good agreement with the corresponding ones by considering V _S and modulus reduction and damping ratio variabilities simultaneously for the studied site. Furthermore, it seems that the effect of bedrock depth in definition of spectral shapes of the Iranian seismic building code should be taken into account.     

Spatial and temporal trends of dust storms across desert regions of Iran

Dust storms are among natural and anthropogenic hazards for socioeconomic resources, especially in desert regions. In recent years, dust storms have become a serious problem, especially in desert regions of Iran. This study investigates temporal and spatial variation of dust storm frequency in desert regions of Iran. The number of dusty days (NDD) are collected from 22 stations across the region. The statistical analysis of NDD time series is carried out to show both spatial and seasonal pattern of dust storm occurrence in the region. The regional map of statistical characteristics indicates a north to south increasing dust storm frequency. The spatial map also reveals higher year-to-year variation in south eastern Iran. The seasonality of NDD shows the highest frequency for summer followed by the spring and autumn seasons. The popular Mann–Kendall and the bootstrap MK test to consider serial correlation are then applied for Trend assessment. Results showed both negative (across the north and northwestern regions) and positive trend (across south and south eastern regions) in the annual and seasonal NDD time series. This north-to-south gradient in the spatial and temporal frequency NDD may arise from harsh dry and gusty winds as well as intense land use change in the south eastern territories of Iran. However, more careful and detailed studies are required to connect environmental conditions to change in NDD frequency.     

Correction to: Assessing real options in urban surface water flood risk management under climate change

Windblown dust originating in China and Mongolia causes health effects and agricultural damage in its source areas and causes Asian dust events in Japan. An early warning system that could be combined with weather forecasts would be helpful in preventing serious damage. However, it is difficult to specify source areas of dust with current dust modeling systems because land surface information, including vegetation coverage and land surface soil water content, is inadequate. To find and monitor dust source regions, a semi-real-time dust erodibility map was developed based on MODIS satellite data that focuses particularly on the threshold wind speed in a target area of northeast Asia including China and Mongolia (35°–50°N, 75°–120°E). The mapping system incorporates satellite data on snow cover, areas of frozen soil, surface soil water content, and vegetation cover.     

Large wood transport modelling by a coupled Eulerian–Lagrangian approach

The paper discusses a model which predicts the trajectory of floating rigid bodies and may be applied to compute the motion of woody “debris” mobilized during floods. The model couples a Discrete Element (DE) Lagrangian approach for the calculation of motion of rigid bodies with the Eulerian solution of the shallow water equations (SWE), in order to simulate the transport of a cylinder in a two-dimensional stream. It differs from existing models since it is based on a dynamic approach, adapting the Basset–Boussinesq–Oseen equation. In a first step, forces are computed from flow and log velocities; then, the equations of dynamics are solved to model the planar roto-translation of the body. Model results and physical reliability are clearly affected by the values of the drag and side coefficients, especially since logs, modelled as cylinders, are able to change their orientation towards the flow. Experimental studies to evaluate drag and side coefficients can be found in the literature for a submerged cylinder, with various orientations. To extend such results to the case of a floating log, the authors performed a series of laboratory tests on partially submerged cylinders, implementing the outcomes in the proposed DE-SWE model. The coupled model is validated against existing laboratory data concerning spheres and wooden cylinder transport.     

Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.     

Uncertain human consequences in asteroid risk analysis and the global catastrophe threshold

This paper studies the risk of collision between asteroids and Earth. It focuses on uncertainty in the human consequences of asteroid collisions, with emphasis on the possibility of global catastrophe to human civilization. A detailed survey of the asteroid risk literature shows that while human consequences are recognized as a major point of uncertainty, the studies focus mainly on physical and environmental dimensions of the risk. Some potential human consequences are omitted entirely, such as the possibility of asteroid explosions inadvertently causing nuclear war. Other human consequences are modeled with varying degrees of detail. Direct medical effects are relatively well-characterized, while human consequences of global environmental effects are more uncertain. The latter are evaluated mainly in terms of a global catastrophe threshold, but such a threshold is deeply uncertain and may not even exist. To handle threshold uncertainty in asteroid policy, this paper adapts the concept of policy boundaries from literature on anthropogenic global environmental change (i.e., planetary boundaries). The paper proposes policy boundaries of 100 m asteroid diameter for global environmental effects and 1 m for inadvertent nuclear war. Other policy implications include a more aggressive asteroid risk mitigation policy and measures to avoid inadvertent nuclear war. The paper argues that for rare events like large asteroid collisions, the absence of robust data means that a wide range of possible human consequences should be considered. This implies humility for risk analysis and erring on the side of caution in policy.     

Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data

With the acceleration of urbanisation in China, preventing and reducing the economic losses and casualties caused by urban rainstorm waterlogging disasters have become a critical and difficult issue that the government is concerned about. As urban storms are sudden, clustered, continuous, and cause huge economic losses, it is difficult to conduct emergency management. Developing a more scientific method for real-time disaster identification will help prevent losses over time. Examining social media big data is a feasible method for obtaining on-site disaster data and carrying out disaster risk assessments. This paper presents a real-time identification method for urban-storm disasters using Weibo data. Taking the June 2016 heavy rainstorm in Nanjing as an example, the obtained Weibo data are divided into eight parts for the training data set and two parts for the testing data set. It then performs text pre-processing using the Jieba segmentation module for word segmentation. Then, the term frequency–inverse document frequency method is used to calculate the feature items weights and extract the features. Hashing algorithms are introduced for processing high-dimensional sparse vector matrices. Finally, the naive Bayes, support vector machine, and random forest text classification algorithms are used to train the model, and a test set sample is introduced for testing the model to select the optimal classification algorithm. The experiments showed that the naive Bayes algorithm had the highest macro-average accuracy.