Experimental Study of Water Displacement Rates on Remaining Oil Distribution and Oil Recovery in 2D Pore Network Model

An amount of oil remains in oil reservoirs even at the high water-cut stage of produced liquid from oil wells. To reveal the mechanism of displacement rates to affect the remaining oil in pore scales, a two-dimensional (2D) glass etching pore network model and real-time visual system were set up to observe the characteristics of oil distribution from water flooding and study the influence of displacement rates on oil recovery. It was found that the geometry of remaining oil in the pore network is diverse and dynamically changed at the high water-cut stage. Three geometric representative parameters were defined for the classification of five types of remaining oil (contiguous, branching, film, dropwise, bar columnar type), and controlling mechanisms for each type of remaining oil were analyzed. The experimental results show that the remaining oil saturation decreases from 21.2% to 6.5% when water injection rates increase from 0.05 to 0.5 mL/min. The increase in displacement rate improves the displacement efficiency of four types of remaining oil in the range of 55.00% to 93.67% except for dropwise type. The experimental data also indicate that the reduction in continuous residual oil and branched residual oil mainly contributes to the improvement of oil recovery of the whole network model. With the increase in displacement rate (from 0.05 to 0.1, 0.2, 0.3, 0.4, and 0.5 mL/min), the areas of five types of representative local residual oil reduce step by step. This research validates that the increase in water flooding rate in porous media leads to reduction in oil saturation, and it will improve oil recovery in oil reservoirs by enhancing water injection rates

Experimental Study of Water Displacement Rates on Remaining Oil Distribution and Oil Recovery in 2D Pore Network Model

An amount of oil remains in oil reservoirs even at the high water-cut stage of produced liquid from oil wells. To reveal the mechanism of displacement rates to affect the remaining oil in pore scales, a two-dimensional (2D) glass etching pore network model and real-time visual system were set up to observe the characteristics of oil distribution from water flooding and study the influence of displacement rates on oil recovery. It was found that the geometry of remaining oil in the pore network is diverse and dynamically changed at the high water-cut stage. Three geometric representative parameters were defined for the classification of five types of remaining oil (contiguous, branching, film, dropwise, bar columnar type), and controlling mechanisms for each type of remaining oil were analyzed. The experimental results show that the remaining oil saturation decreases from 21.2% to 6.5% when water injection rates increase from 0.05 to 0.5 mL/min. The increase in displacement rate improves the displacement efficiency of four types of remaining oil in the range of 55.00% to 93.67% except for dropwise type. The experimental data also indicate that the reduction in continuous residual oil and branched residual oil mainly contributes to the improvement of oil recovery of the whole network model. With the increase in displacement rate (from 0.05 to 0.1, 0.2, 0.3, 0.4, and 0.5 mL/min), the areas of five types of representative local residual oil reduce step by step. This research validates that the increase in water flooding rate in porous media leads to reduction in oil saturation, and it will improve oil recovery in oil reservoirs by enhancing water injection rates

Multi-Dimensional Evaluation of Ecosystem Health in China’s Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm

China’s Loess Plateau (CLP) is a typical semi-arid region and is very sensitive to climate and human activity. Under the ecological restoration project, vegetation coverage increased significantly, but the limitation of climate and other factors has meant that vegetation mortality was relatively high. Therefore, it is of great significance to evaluate the ecosystem health in the CLP in terms of the sustainability of ecological restoration projects. The aim of this study is to propose a multi-dimensional assessment method to investigate vegetation health changes in the CLP based on BFAST and BFAST01 algorithms. To achieve this, we constructed local dimension health indexes (the number of disturbances and recovery rate) and overall dimension health indexes (trend types) based on the gross primary productivity (GPP) and vegetation evapotranspiration (Ec) data of the study area from 2001 to 2020 which was collected from the Google Earth Engine (GEE) platform. The result revealed the following. More than 90% of disturbance pixels of GPP and Ec in the short-term change only once and more than 60% of pixels recover after disturbance. However, the recovery rate after disturbance is slow, and the interval with the largest proportion is 0–0.00015. The long-term trend mostly exhibited a monotonic increasing trend. These results indicate that the function of the ecosystem on the CLP has been improved, but the resilience of vegetation is weak. In conclusion, the combination of the local dimension and overall dimension analysis can comprehensively reveal information about the CLP’s vegetation health in the past two decades, and that the method will open new perspectives and generate new knowledge about vegetation health in the CLP

An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series

Large-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore the impacts of afforestation expansion on fragile ecosystems. However, distinguishing planted forests from natural forests using remote sensing technology is not a trivial task due to their strong spectral similarities, even when assisted by phenological variables. In this study, we developed an object- and shapelet-based (OASB) method for mapping the planted forests of the Ningxia Hui Autonomous Region (NHAR), China in 2020 and for tracing the planting years between 1991 and 2020. The novel method consists of two components: (1) a simple non-iterative clustering to yield homogenous objects for building an improved time series; (2) a shapelet-based classification to distinguish the planted forests from the natural forests and to estimate the planting year, by detecting the temporal characteristics representing the planting activities. The created map accurately depicted the planted forests of the NHAR in 2020, with an overall accuracy of 87.3% (Kappa = 0.82). The area of the planted forest was counted as 0.56 million ha, accounting for 67% of the total forest area. Additionally, the planting year calendar (RMSE = 2.46 years) illustrated that the establishment of the planted forests matched the implemented ecological restoration initiatives over the past decades. Overall, the OASB has great potential for mapping the planted forests in the NHAR or other arid and semi-arid regions, and the map products derived from this method are conducive to evaluating forestry eco-engineering projects and facilitating the sustainable development of forest ecosystems

An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series

Large-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore the impacts of afforestation expansion on fragile ecosystems. However, distinguishing planted forests from natural forests using remote sensing technology is not a trivial task due to their strong spectral similarities, even when assisted by phenological variables. In this study, we developed an object- and shapelet-based (OASB) method for mapping the planted forests of the Ningxia Hui Autonomous Region (NHAR), China in 2020 and for tracing the planting years between 1991 and 2020. The novel method consists of two components: (1) a simple non-iterative clustering to yield homogenous objects for building an improved time series; (2) a shapelet-based classification to distinguish the planted forests from the natural forests and to estimate the planting year, by detecting the temporal characteristics representing the planting activities. The created map accurately depicted the planted forests of the NHAR in 2020, with an overall accuracy of 87.3% (Kappa = 0.82). The area of the planted forest was counted as 0.56 million ha, accounting for 67% of the total forest area. Additionally, the planting year calendar (RMSE = 2.46 years) illustrated that the establishment of the planted forests matched the implemented ecological restoration initiatives over the past decades. Overall, the OASB has great potential for mapping the planted forests in the NHAR or other arid and semi-arid regions, and the map products derived from this method are conducive to evaluating forestry eco-engineering projects and facilitating the sustainable development of forest ecosystems

Integrating satellite-based passive microwave and optically sensed observations to evaluating the spatio-temporal dynamics of vegetation health in the red soil regions of southern China

Attentions over the health of evergreen vegetation are increasing owing to frequently occurrence of recent disturbance events (i.e. soil erosion, logging activities, and afforestation). However, vegetation indices that characterize canopy greenness have limitations in spectral saturation for representing the growth states of densely vegetated areas, and the continuous acquisition of satellite-derived vegetation functional metrics depends on the availability of clear image observations. This study investigated the vegetation health dynamics (1993–2012) in the red soil regions of southern China using satellite observations based task-oriented metrics, including the Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Aboveground Biomass Carbon (ABC). The results indicated that the total number of pixels with significant changes (SC) was 214, 1,186, and 794 for the NDVI, VWC, and ABC indices, respectively. More than 90% of the SC pixels in the three metrics exhibited increasing trends, which were primarily observed in mountainous areas. Pixels that exhibited a continuously declining trend were discretely distributed throughout the entire study area. Among the SC pixels, vegetation in major parts of the study area was disturbed by abrupt events. In the NDVI, VWC, and ABC, the frequency of abrupt changes increased after 2000, coinciding with the launch of the Natural Forest Conservation Program (NFCP) in 2000–2001. For regions with abrupt changes, four patterns were further observed based on the indices: the continued increases (pattern-1), sustained decreases (pattern-2), recovery growth after an initial decline (pattern-3), and significant decreases after initial growth (pattern-4). Pattern-1 appeared more frequently than the other three patterns. This study indicates that vegetation in most areas was optimally developed and exhibited a healthier tendency compared with previous growth states. Notably, the presence of an increasingly unhealthy vegetation state was observed in the northeastern region of the study area. Satellite derived datasets and synergetic use of indicators contribute to understanding the changes in the vegetation health in the entire red soil regions in southern China. Thus, this study acts as a reference for forest management and soil erosion control

Power transformer oil temperature prediction based on empirical mode decomposition-bidirectional long short-term memory

Power transformers are crucial components of power transmission and transformation networks. Their operational status has a direct impact on the reliability of power supply systems. As such, the security and stability of power systems depend heavily on the state of transformers within them. The oil temperature of a transformer is a critical indicator of its working condition. Accurately and rapidly predicting transformer oil temperature is therefore of significant practical importance for ensuring the safe and effective operation of power systems. To address this prediction problem, this article proposes a transformer oil temperature prediction method based on empirical mode decomposition-bidirectional long short-term memory (EMD-BiLSTM). The time series of oil temperature is first cleaned before being processed. Next, the EMD algorithm is used to decompose the time series into relatively stable components. The BiLSTM neural network is then utilized to predict the complex nonlinear long-term series. The proposed method is evaluated using the open data set Electricity Transformer Temperature (ETT)-small. Experimental results show that the EMD-BiLSTM model outperforms traditional LSTM, BiLSTM, EMD-BP, and Wavelet Transform-Bidirectional Long Short-Term Memory (WT-BiLSTM) methods, demonstrating that it is an effective and accurate prediction method for transformer oil temperature

Assessing Suitability of Human Settlements in High-Altitude Area Using a Comprehensive Index Method: A Case Study of Tibet, China

With the steady advancement of the United Nations Sustainable Development Goals (SDGs), how to build a sustainable environment for human settlements has become a hot topic of research for scholars from various countries. Rational space utilization and resource allocation are the keys to enhancing human well-being and achieving sustainable human settlements. A comprehensive human settlement environment evaluation system, which includes 14 indicators from the natural environment, infrastructure, and public services, was established in this study. The results showed that the habitat suitability area only accounted for 1.61% (2.05% after removing the nature reserve) and all centered on cities and radiated to the surrounding areas. A belt-like suitability distribution pattern of “Yi Jiang Liang He” (i.e., Brahmaputra, Lhasa, and Nianchu Rivers) is formed, and a point-like suitability distribution pattern of the Chamdo Karub District, Nagqu Seni District, and Ngari Shiquanhe Town are formed. The results of the driving factor analysis indicate that the level of public health development in infrastructure and various indicators in public services are the main factors influencing human settlement. There is not much difference in the natural environment in the populated regions, so the suitability of the natural environment is not a significant driving factor. In addition, the reliability of the assessment results was verified by a questionnaire survey of residents in the three regions, and the subjective satisfaction of the residents agreed with the ranking results of the objective evaluation. The evaluation results of this study provide theoretical and directional guidance for the improvement of human settlements on the Qinghai–Tibet Plateau. It will be a useful tool for evaluating human settlements in the region and has a reference significance for the formulation of macro-policy in high-altitude regions

Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region

A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km2), while the highest negative effects were observed in 2017 (54.54 km2). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures

Ex vivo susceptibilities of Plasmodium vivax isolates from the China-Myanmar border to antimalarial drugs and association with polymorphisms in Pvmdr1 and Pvcrt-o genes.

BackgroundVivax malaria is an important public health problem in the Greater Mekong Subregion (GMS), including the China-Myanmar border. Previous studies have found that Plasmodium vivax has decreased sensitivity to antimalarial drugs in some areas of the GMS, but the sensitivity of P. vivax to antimalarial drugs is unclear in the China-Myanmar border. Here, we investigate the drug sensitivity profile and genetic variations for two drug resistance related genes in P. vivax isolates to provide baseline information for future drug studies in the China-Myanmar border.Methodology/principal findingsA total of 64 P. vivax clinical isolates collected from the China-Myanmar border area were assessed for ex vivo susceptibility to eight antimalarial drugs by the schizont maturation assay. The medians of IC50 (half-maximum inhibitory concentrations) for chloroquine, mefloquine, pyronaridine, piperaquine, quinine, artesunate, artemether, dihydroartemisinin were 84.2 nM, 34.9 nM, 4.0 nM, 22.3 nM, 41.4 nM, 2.8 nM, 2.1 nM and 2.0 nM, respectively. Twelve P. vivax clinical isolates were found over the cut-off IC50 value (220 nM) for chloroquine resistance. In addition, sequence polymorphisms in pvmdr1 (P. vivax multidrug resistance-1), pvcrt-o (P. vivax chloroquine resistance transporter-o), and difference in pvmdr1 copy number were studied. Sequencing of the pvmdr1 gene in 52 samples identified 12 amino acid substitutions, among which two (G698S and T958M) were fixed, M908L were present in 98.1% of the isolates, while Y976F and F1076L were present in 3.8% and 78.8% of the isolates, respectively. Amplification of the pvmdr1 gene was only detected in 4.8% of the samples. Sequencing of the pvcrt-o in 59 parasite isolates identified a single lysine insertion at position 10 in 32.2% of the isolates. The pvmdr1 M908L substitutions in pvmdr1 in our samples was associated with reduced sensitivity to chloroquine, mefloquine, pyronaridine, piperaquine, quinine, artesunate and dihydroartemisinin.ConclusionsOur findings depict a drug sensitivity profile and genetic variations of the P. vivax isolates from the China-Myanmar border area, and suggest possible emergence of chloroquine resistant P. vivax isolates in the region, which demands further efforts for resistance monitoring and mechanism studies