Pore–Scale Numerical Investigations of the Impact of Mineral Dissolution and Transport in Naturally Fractured Systems During CO2–Enriched Brine Injection

CO2 storage and sequestration are regarded as an effective approach to mitigate greenhouse gas emissions. While injecting an enormous amount of CO2 into carbonate–rich aquifers, CO2 dissolves in the formation brine under the large pressure, and the subsequently formed CO2–enriched brine reacts with the calcite. Reaction–induced changes in pore structure and fracture geometry alter the porosity and permeability, giving rise to concerns of CO2storage capacity and security. Especially in the reservoir or aquifer with natural fractures, the fractures provide a highly permeable pathways for fluid flow. This study aims to analyze the acid–rock interaction and subsequent permeability evolution in the systems with complex fracture configurations during CO2 injection by implementing a pore–scale DBS reactive transport model. The model has been developed by expanding the functionality of OpenFOAM, which is an open–source code for computational fluid dynamics. A series of partial differential equations are discretized by applying the Finite Volume Method (FVM) and sequentially solved. Different fracture configurations in terms of fracture length, density, connection, and mineral components have been considered to investigate their impacts on the dynamic porosity–permeability relationship, dissolution rate, and reactant transport characteristics during CO2 storage. The investigation revealed several interesting findings. We found that calcium (Ca) concentration was low in the poorly connected area at the initial time. As CO2–enriched brine saturated the system and reacted with calcite, Ca started being accumulated in the system. However, Ca barely flowed out of the poor–connected area, and the concentration became high. Lengths of branches mainly influenced the dissolution rates, while they had slight impacts on the porosity–permeability relationship. While fracture connectivity had an apparent influence on the porosity–permeability relationship, it showed a weak relevance on the dissolution rate. These microscopic insights can help enhance the CO2 sealing capacity and guarantee environmental security.

Morphology of Rain Clusters Influencing Rainfall Intensity over Hainan Island

Continuous observations from geostationary satellites can show the morphology of precipitation cloud systems in quasi-real-time, but there are still large deviations in the inversion of precipitation. We used binary-connected area recognition technology to identify meso-β-scale rain clusters over Hainan Island from 1 June 2000 to 31 December 2018, based on Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM data. We defined and statistically analyzed the parameters of rain clusters to reveal the typical morphological and precipitation characteristics of rain clusters, and to explore the relationship between the parameters and rainfall intensity of rain clusters. We found that the area and long axis of rain clusters over land were larger than those over the ocean, and that continental rain clusters were usually square in shape. Rain clusters with a larger area and longer axis were concentrated on the northern side of the mountains on Hainan Island and the intensity of rain was larger on the northern and eastern sides of the mountains. The variation of continental rain clusters over time was more dramatic than the variation of oceanic clusters. The area and long axis of rain clusters was larger between 14:00 and 21:00 from April to September and the long axis of the oceanic rain clusters increased in winter. There were clear positive correlations between the area, long axis and shape of the rain clusters and the maximum rain rate. The area and long axis of continental rain clusters had a higher correlation with the rain rate than those of oceanic clusters. The establishment of a relationship between the morphology of rain clusters and precipitation helps us to understand the laws of precipitation and improve the prediction of precipitation in this region.

On the Connectivity Measurement of the Fractal Julia Sets Generated from Polynomial Maps: A Novel Escape-Time Algorithm

In this paper, a novel escape-time algorithm is proposed to calculate the connectivity’s degree of Julia sets generated from polynomial maps. The proposed algorithm contains both quantitative analysis and visual display to measure the connectivity of Julia sets. For the quantitative part, a connectivity criterion method is designed by exploring the distribution rule of the connected regions, with an output value Co in the range of [0,1]. The smaller the Co value outputs, the better the connectivity is. For the visual part, we modify the classical escape-time algorithm by highlighting and separating the initial point of each connected area. Finally, the Julia set is drawn into different brightnesses according to different Co values. The darker the color, the better the connectivity of the Julia set. Numerical results are included to assess the efficiency of the algorithm.

CELLULAR AUTOMATON WITH PERCOLATION AS A DYNAMIC SYSTEM: ENTROPY APPROACH

A model of information confrontation based on a two-dimensional percolation-cellular automaton on a closed oriented surface is constructed and implemented programmatically. Numerical experiments were performed. A theorem on the finiteness of a completely positive topological entropy of a given cellular automaton is formulated and proved. As an applied application of the constructed automaton, a retrospective forecast of the results of the Russian Presidential election in 2018 was made both in Russia as a whole and in two regions of Russia. The General logic of using the automaton was as follows. At the preparatory stage, a model of the region was built, which is a field of a cellular automaton. In the case of regions, anamorphic mapping was used, in which each territorial-administrative unit is represented by a certain number of cells that occupy a connected area of the field of the automaton; the areas of these areas are proportional to the number of voters, and if possible, geographical neighbors are preserved. The color of the cell corresponds to a certain political position; for example, if a certain city has 60% support for the President, then this percentage of cells in that city is red. As initial data for the calculation, the results of sociological surveys on support for the current President conducted 6-12 months before the election were taken; the dynamics of the system is modeled using a cellular automaton; for the formed stationary solution, the shares of cells corresponding to the number of supporters and opponents of the President, as well as non-appearance, are calculated; these shares are taken as a forecast of election results. Those constructed in this way showed significantly lower accuracy than forecasts made using standard sociological methods in the last days before the election. However, they can be used for early forecasting. The reason for this difference is that the opinions of voters immediately prior to elections are determined by campaign, and long before the election - the prevalence and embeddedness of value orientations, which is incorporated in the model.

Segmentation of Cerebrovascular Anatomy from TOF-MRA Using Length-Strained Enhancement and Random Walker

Cerebrovascular rupture can cause a severe stroke. Three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) is a common method of obtaining vascular information. This work proposes a fully automated segmentation method for extracting the vascular anatomy from TOF-MRA. The steps of the method are as follows. First, the brain is extracted on the basis of regional growth and path planning. Next, the brain’s highlighted connected area is explored to obtain seed point information, and the Hessian matrix is used to enhance the contrast of image. Finally, a random walker combined with seed points and enhanced images is used to complete vascular anatomy segmentation. The method is tested using 12 sets of data and compared with two traditional vascular segmentation methods. Results show that the described method obtains an average Dice coefficient of 90.68%, and better results were obtained in comparison with the traditional methods.

Do stream water solute concentrations reflect when connectivity occurs in a small, pre-Alpine headwater catchment?

Expansion of the hydrologically connected area during rainfall events causes previously disconnected areas to contribute to streamflow. If these newly contributing areas have a different hydrochemical composition compared to the previously connected contributing areas, this may cause a change in stream water chemistry that cannot be explained by simple mixing of rainfall and baseflow. Changes in stormflow composition are, therefore, sometimes used to identify when transiently connected areas (or water sources) contribute to stormflow. We identified the dominant sources of streamflow for a steep 20 ha pre-Alpine headwater catchment in Switzerland and investigated the temporal changes in connectivity for four rainfall events based on stream water concentrations and groundwater level data. First, we compared the isotopic and chemical composition of stormflow at the catchment outlet to the composition of rainfall, groundwater and soil water. Three-component end-member mixing analyses indicated that groundwater dominated stormflow during all events, and that soil water fractions were minimal for three of the four events. However, the large variability in soil and groundwater composition compared to the temporal changes in stormflow composition inhibited the determination of the contributions from the different groundwater sources. Second, we estimated the concentrations of different solutes in stormflow based on the mixing fractions derived from two-component hydrograph separation using a conservative tracer (δ2H) and the measured concentrations of the solutes in baseflow and rainfall. The estimated concentrations differed from the measured stormflow concentrations for many solutes and samples. The deviations increased gradually with increasing streamflow for some solutes (e.g. iron and copper), suggesting increased contributions from riparian and hillslope groundwater with higher concentrations of these solutes and thus increased hydrological connectivity. The findings of this study show that solute concentrations partly reflect the gradual changes in hydrologic connectivity, and that it is important to quantify the variability in the composition of different source areas.

Do streamwater solute concentrations reflect when connectivity occurs in a small pre-alpine headwater catchment?

Expansion of the hydrologically connected area during rainfall events causes previously disconnected areas to contribute to streamflow. If these contributing areas have a different hydrochemical composition than the permanently connected areas, this may cause a change in streamwater chemistry that can not be described by simple mixing of rainfall and baseflow. Changes in stormflow composition are therefore sometimes used to identify when transiently disconnected areas (or water sources) contribute to stormflow. We identified the dominant sources of streamflow for four rainfall events in a steep 20-ha pre-alpine headwater catchment in Switzerland to investigate the temporal changes in connectivity. First, we compared the isotopic and chemical composition of stormflow at the catchment outlet to the composition of rainfall, groundwater, and soil water. Three-component end-member mixing analyses indicated that groundwater dominated stormflow for three of the four events, and that soil water fractions were minimal for two events. Then, we tested whether conservative mixing of rainfall and baseflow could describe the chemical composition of stormflow. To this end, we estimated the concentrations of different solutes in stormflow based on the mixing fractions derived from a conservative tracer (δ2H) and the concentration of the solutes in baseflow and rainfall. Then, we compared these estimated concentrations to the measured concentrations. We found that the estimated concentrations differed from the measured stormflow concentrations for many solutes and samples. The deviations increased gradually with streamflow for some solutes (e.g., iron and copper), suggesting increased hydrologic connectivity. However, the large variability in soil and groundwater composition compared to the changes in stormflow inhibited the determination of the contributions from the different sources. Our findings show that solute concentrations can be helpful for investigating hydrologic connectivity, and that it is important to quantify the variability in the composition of different source areas.

FPGA-Based Connected Component Algorithm for Vegetation Segmentation

In the process of automatic trees recognition and tracking, image target is captured by RGB camera mounted on a UAV, in processing step image captured is subjected to threshold and extract selected information, This techniques may be applied to recognize objects with different shapes and sizes. In the case of remote sensing vegetation, the image usually contains multiple connected areas or overlapped trees; the proposed system uses the shape characteristics of the image target to self-identify the suspicious overlapped features. This technique allows distinguish, analyze and detect different features in images by assigning a unique label to all pixels that refers to the same entity or object. In the process of automatically recognizing and tracking the target of an image, it is first segmented and extracted. The resulting binary image usually contains several connected regions. The system uses the shape characteristics of the target in the image to automatically identify the suspected overlapped trees. Therefore, it is necessary to detect and evaluate each connected area block separately, in this paper, the improved FPGA-specific rapid marking algorithm is used to detect and extract each connected domain.