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2022 ◽  
Author(s):  
Glyn Roberts ◽  
Souvick Saha ◽  
Johanna Waldheim

Abstract This paper further develops an analysis of proppant distribution patterns in hydraulically fractured wells initially presented in SPE-199693-MS. A significantly enlarged database of in-situ perforation erosion measurements provides a more rigorous statistical basis allowing some previously reported trends to be updated, but the main objective of the paper is to present additional insights identified since the original paper was published. Measurements of the eroded area of individual perforations derived from downhole camera images again provide the input for this study. Entry hole enlargement during limited entry hydraulic fracturing provides strong and direct evidence that proppant was successfully placed into individual perforations. This provides a straightforward evaluation of cluster efficiency. Perhaps more importantly the volume of proppant placed into a perforation can also be inferred from the degree of erosion. Summing individual perforation erosion at cluster level allows patterns and biases to be identified and an understanding of proppant distribution across stages has been developed. Outcomes from an analysis of a database that now exceeds 50,000 eroded perforations are presented. Uniform reservoir stimulation is a key objective of fracture treatments but remains challenging to measure and report. The study therefore focused on understanding how uniformly proppant is distributed across more than 1,800 measured stages. Results demonstrate how proppant distribution within stages is influenced when treatment parameters change. Our approach was to vary one parameter, for example the stage length, while all other parameters were maintained at a consistent value. We investigated multiple parameters that can be readily controlled during treatment design and show how these can be manipulated to improve proppant distribution. These included stage length, cluster spacing, perforation count per cluster and perforation phase. Hydraulic fracturing is a complex, high energy process with numerous input parameters. At individual cluster and stage level outcomes can be unpredictable and diagnostic results are often quite variable. The approach taken here was to complete a statistical analysis of a sufficiently large dataset of in-situ measurements. This allowed common trends and patterns to be confidently identified and conclusions reached on how proppant distribution is affected by varying specific design parameters. This should be of interest and value to those designing hydraulic fracture treatments.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Samson I Ojo ◽  
Zachaeus K Adeyemo ◽  
Damilare O Akande ◽  
Rebecca O Omowaiye

White Space Detection (WSD) is a core operation in a Cognitive Radio System (CRS) to identify idle spectrum for maximum utilization. However, WSD is often affected by multipath effects resulting in poor detection rate. Cooperative WSD (CWSD) which is one of the existing techniques used to address the problem is characterized by long sensing time, energy and bandwidth inefficiency. Energy-Efficient CWSD (EECWSD) was proposed in previous work to solve the problem associated with CWSD. Hence, in this paper, the effect of clusters in EECWSD is carried out with Radiometry Detector (RD). The investigation is carried out using multiple clusters and each cluster contained multiple Secondary Users (SUs). The SUs are used to perform local sensing and the sensing results are combined at individual cluster using majority fusion rule. The sensing results from individual cluster are combined to obtain global sensing result using OR fusion rule. The system is simulated using MATLAB software. The system is evaluated using Probability of Detection (PD), Total Error Probability (TEP), Spectral Efficiency (SE) and Sensing Time (ST). At SNR of 20 dB, PD values of 0.7890, 0.8376 and 0.8787 are obtained for clusters 3, 4 and 5, respectively, while the corresponding TEP values are 0.2210, 0.1724 and 0.1313 for clusters 3, 4 and 5, respectively. At SNR of 16 dB, 13.2594 and 16.4341 are the SE values obtained for clusters 3 and 5, respectively, while the corresponding ST values obtained are 4.2487 and 2.6177 s for clusters 3 and 5, respectively. The results obtained revealed that, PD and SE increase as number of cluster increases, while ST and TEP reduce as cluster increases.  Keywords— Cognitive Radio, White Space, Spectrum Sensing, Probability of Detection, Spectral Efficiency.


2021 ◽  
Vol 10 (1) ◽  
pp. 128-134
Author(s):  
Sergey Yurievich Petrov ◽  
Sergey Vladimirovich Chumakov ◽  
Andrey Vasilievich Tolmachev ◽  
Anna Evgenievna Barabantsova ◽  
Lyubov Borisovna Pershina

This work includes additions to the existing annotated list of birds of the Shushensky Bor National Park, given in the book by ornithologist S.Yu. Petrov and senior state inspector S.V. Chumakov Birds of the Shushensky Bor National Park, published in 2020 in an electronic form. The paper provides general data on the avifauna of the national park, as well as the results of recent studies as of early 2021. The paper provides information on the sightings of 11 bird species that were not previously recorded in the park as a whole: Ptyonoprogne rupestris (Scopoli, 1769); Turdus merula (Linnaeus, 1758); Monticola saxatilis (Linnaeus, 1766); Ficedula hypoleuca (Pallas, 1764) аnd on individual cluster sites Mountain forestry: Podiceps cristatus (Linnaeus, 1758); Crex crex (Linnaeus, 1758); Charadrius dubius (Scopoli, 1786); Turdus obscurus J.F. Gmelin, 1789; Turdus iliacus (Linnaeus, 1766) and Perovsky forestry: Lanius borealis [excubitor] (Vieillot, 1807); Chloris chloris (Linnaeus, 1758), indicating the nature of their stay and biotopic distribution. The paper is illustrated with photographs taken on the territory of both clusters of the national park by employees of the Shushensky Bor National Park and by local birdwatchers.


2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Evgeniya Lupova-Henry ◽  
Sam Blili ◽  
Cinzia Dal Zotto

AbstractIn this article, we explore whether organized clusters can act as institutional entrepreneurs to create conditions favorable to innovation in their constituent members. We view self-aware and organized clusters as “context-embedded meta-organizations” which engage in deliberate decision- and strategy-making. As such, clusters are not only shaped by their environments, as “traditional” cluster approaches suggest but can also act upon these. Their ability to act as “change agents” is crucial in countries with high institutional barriers to innovation, such as most transition economies. Focusing on Russia, we conduct two cluster case studies to analyze the strategies these adopt to alter and shape their institutional environments. We find that clusters have a dual role as institutional entrepreneurs. First, these can act collectively to shape their environments due to the power they wield. Second, they can be mechanisms empowering their constituent actors, fostering their reflexivity and creativity, and allowing them to engage in institutional entrepreneurship. Moreover, both collective and individual cluster actors adopt “bricolage” approaches to institutional entrepreneurship to compensate for the lack of resources or institutional frameworks or avoid the pressures of ineffective institutions.


Author(s):  
Ekaterina M. Mishina ◽  
◽  

This article focuses on the analysis of the impact of socio-economic development indicators of Altai region and Oyrot autonomous region on the eve of the Great Purge (1935 — first half of 1937) on the regional intensity of repression. Employing statistical methods (regression analysis), the author verifies the hypothesis that in the areas with the highest level of well-being of the population, the level of repression was also higher. It is established that the turnover and expenditures per capita compared with other economic indicators had the greatest influence on repression levels in Altai and Oyrotia regions. Based on the results of the analysis of regional statistics, the author of the article puts forward a theory that the thesis proclaimed by the Bolsheviks to justify the failure of economic development by the actions of the “enemies” in practice seems untenable, since economically lagging regions were characterised by a relatively low level of repression. In the second part of the article, the author presents a typology of districts of Altai and Oyrotia regions based on the results of cluster analysis of various groups of socio-economic development indicators. Additionally, she substantiates the hypothesis about the influence of the spatial factor on the intensity of repression: the groups of regions of each individual cluster consist mainly of adjacent regions.


Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Luis Alberto Mancha ◽  
David Uriarte ◽  
Esperanza Valdés ◽  
Daniel Moreno ◽  
María del Henar Prieto

The efficient use of water in the vineyard requires knowledge of the crop’s response to irrigation in terms of production and quality and the interaction of the same with the environmental conditions. In this work, the behavior of a trellis system vineyard in cv. Tempranillo, located in the south-west of Spain, was analyzed for three years in relation to different irrigation strategies based on crop evapotranspiration (ETc), and with two levels of crop load established by early cluster thinning. The response of the vineyard to the same irrigation strategy varied depending on the characteristics of the year. The vineyard’s biomass production increased in a linear trend as annual water status improved. However, during pre-veraison, the water status had a more significant impact on the harvest by affecting bud fertility. The increase in individual cluster weight only partially compensated the loss of yield caused by cluster thinning. The year’s characteristics highly conditioned the response to the irrigation treatment and, together with cluster thinning, modified the characteristics of the musts, although the response was varied.


2020 ◽  
Author(s):  
Nicole Hill ◽  
Lay San Too ◽  
Matt Spittal ◽  
Jo Robinson

Aims:There is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms, and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: 1) determine the overlap in suicide clusters among each method; 2) compare the spatial and temporal parameters associated with different suicide clusters; and 3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members.Methods: Suicide data were obtained from the National Coronial Information System. N=3027 Australians, aged 10-24 who died by suicide in 2006-2015 were included. Suicide clusters were determined using: 1) poisson scan statistics; 2) a systematic search of coronial inquests; and 3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: 1) statistically significant; 2) perceived to be significant; and 3) characterised by social links among three or more suicide descendents. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members. Results: Eight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% (N=7) in clusters from the scan statistic; 32.5% (N=13) in clusters from coronial inquest; and 56.8% (N=21) in clusters from the descriptive network analysis.Conclusion: Each method identified markedly different suicide clusters. Evidence of social links between cluster members was largely limited to clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.


2020 ◽  
Vol 164 ◽  
pp. 10031
Author(s):  
Sergey Shulzhenko ◽  
Alexandr Zelentsov ◽  
Artur Grebennikov ◽  
Mikhail Danilochkin

This article shows the amount of construction and installation works at the regional level that are formed according to the individual cluster territories, considering the specialization of the production development. For their implementation, it is necessary either to change the specialization of contracting capacities, or to use them with the consideration of the movement mobility in development zones.


Author(s):  
N.T.M. Hill ◽  
L.S. Too ◽  
M.J. Spittal ◽  
J. Robinson

Abstract Aims There is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: (1) determine the overlap in suicide clusters among each method, (2) compare the spatial and temporal parameters associated with different suicide clusters and (3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members. Methods Suicide data were obtained from the National Coronial Information System. N = 3027 Australians, aged 10–24 who died by suicide in 2006–2015 were included. Suicide clusters were determined using: (1) poisson scan statistics, (2) a systematic search of coronial inquests and (3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: (1) statistically significant, (2) perceived to be significant and (3) characterised by social links among three or more suicide descendants. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members. Results Eight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% (N = 7) in clusters from the scan statistic, 32.5% (N = 13) in clusters from coronial inquest and 56.8% (N = 21) in clusters from the descriptive network analysis. Conclusion Each method identified markedly different suicide clusters. Evidence of social links between cluster members typically involved clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.


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