sweet spot
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2022 ◽  
Vol 9 ◽  
Author(s):  
Hongjun Fan ◽  
Xiaoqing Zhao ◽  
Xu Liang ◽  
Quansheng Miao ◽  
Yongnian Jin ◽  
...  

The identification of the “sweet spot” of low-permeability sandstone reservoirs is a basic research topic in the exploration and development of oil and gas fields. Lithology identification, reservoir classification based on the pore structure and physical properties, and petrophysical facies classification are common methods for low-permeability reservoir classification, but their classification effect needs to be improved. The low-permeability reservoir is characterized by low rock physical properties, small porosity and permeability distribution range, and strong heterogeneity between layers. The seepage capacity and productivity of the reservoir vary considerably. Moreover, the logging response characteristics and resistivity value are similar for low-permeability reservoirs. In addition to physical properties and oil bearing, they are also affected by factors such as complex lithology, pore structure, and other factors, making it difficult for division of reservoir petrophysical facies and “sweet spot” identification. In this study, the logging values between low-porosity and -permeability reservoirs in the Paleozoic Es3 reservoir in the M field of the Bohai Sea, and between natural gamma rays and triple porosity reservoirs are similar. Resistivity is strongly influenced by physical properties, oil content, pore structure, and clay content, and the productivity difference is obvious. In order to improve the identification accuracy of “sweet spot,” a semi-supervised learning model for petrophysical facies division is proposed. The influence of lithology and physical properties on resistivity was removed by using an artificial neural network to predict resistivity R0 saturated with pure water. Based on the logging data, the automatic clustering MRGC algorithm was used to optimize the sensitive parameters and divide the logging facies to establish the unsupervised clustering model. Then using the divided results of mercury injection data, core cast thin layers, and logging faces, the characteristics of diagenetic types, pore structure, and logging response were integrated to identify rock petrophysical facies and establish a supervised identification model. A semi-supervised learning model based on the combination of “unsupervised supervised” was extended to the whole region training prediction for “sweet spot” identification, and the prediction results of the model were in good agreement with the actual results.


2021 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Varun Shenoy Gangoli ◽  
Chris J. Barnett ◽  
James D. McGettrick ◽  
Alvin Orbaek White ◽  
Andrew R. Barron

We report the effect of annealing, both electrical and by applied voltage, on the electrical conductivity of fibers spun from carbon nanotubes (CNTs). Commercial CNT fibers were used as part of a larger goal to better understand the factors that go into making a better electrical conductor from CNT fibers. A study of thermal annealing in a vacuum up to 800 °C was performed on smaller fiber sections along with a separate analysis of voltage annealing up to 7 VDC; both exhibited a sweet spot in the process as determined by a combination of a two-point probe measurement with a nanoprobe, resonant Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). Scaled-up tests were then performed in order to translate these results into bulk samples inside a tube furnace, with similar results that indicate the potential for an optimized method of achieving a better conductor sample made from CNT fibers. The results also help to determine the surface effects that need to be overcome in order to achieve this.


2021 ◽  
Vol 7 (50) ◽  
Author(s):  
Célia Lacaux ◽  
Thomas Andrillon ◽  
Céleste Bastoul ◽  
Yannis Idir ◽  
Alexandrine Fonteix-Galet ◽  
...  
Keyword(s):  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Andreas Holtermann ◽  
Charlotte Lund Rasmussen ◽  
David M. Hallman ◽  
Ding Ding ◽  
Dorothea Dumuid ◽  
...  

Abstract“Sit less–move more” has been the univocal advice to adults for better health. Predominantly, this advice is based on research of physical behaviors during leisure-time. A recent study among > 100,000 adults indicates a u-shaped association between leisure-time physical activity and risk for cardiovascular disease and mortality among adults in physically active occupations. This may be explained by the considerable difference in 24-h physical behaviors between adults in sedentary and physically active occupations. Thus, the advice “sit less–move more” might not be the best for health among adults in physically active occupations. To provide a scientific approach and encourage research on 24-h physical behaviors and health for those in physically active occupations, we propose the “Sweet-Spot Hypothesis.” The hypothesis postulates that the “Sweet-Spot” of 24-h physical behaviors for better health differs between adults, depending on their occupation. Specifically, the hypothesis claims that the advice “sit less–move more” does not bring adults in physically active occupations toward their “Sweet-Spot” of 24-h physical behaviors for better health. The purpose of our paper is to encourage researchers to test this proposed hypothesis by describing its origin, its theoretical underpinning, approaches to test it, and practical implications. To promote health for all, and decrease social health inequalities, we see a great need for empirically testing the “Sweet-Spot Hypothesis.” We propose the “Sweet-Spot Hypothesis” to encourage discussion, debates, and empirical research to expand our collective knowledge about the healthy “24-h physical behavior balance” for all.


2021 ◽  
pp. 100095
Author(s):  
Thorben Pape ◽  
Anna Maria Hunkemöller ◽  
Philipp Kümpers ◽  
Hermann Haller ◽  
Sascha David ◽  
...  

2021 ◽  
Vol 17 (11) ◽  
pp. e1009611
Author(s):  
Alex McAvoy ◽  
Andrew Rao ◽  
Christoph Hauert

In many models of evolving populations, genetic drift has an outsized role relative to natural selection, or vice versa. While there are many scenarios in which one of these two assumptions is reasonable, intermediate balances between these forces are also biologically relevant. In this study, we consider some natural axioms for modeling intermediate selection intensities, and we explore how to quantify the long-term evolutionary dynamics of such a process. To illustrate the sensitivity of evolutionary dynamics to drift and selection, we show that there can be a “sweet spot” for the balance of these two forces, with sufficient noise for rare mutants to become established and sufficient selection to spread. This balance allows prosocial traits to evolve in evolutionary models that were previously thought to be unconducive to the emergence and spread of altruistic behaviors. Furthermore, the effects of selection intensity on long-run evolutionary outcomes in these settings, such as when there is global competition for reproduction, can be highly non-monotonic. Although intermediate selection intensities (neither weak nor strong) are notoriously difficult to study analytically, they are often biologically relevant; and the results we report suggest that they can elicit novel and rich dynamics in the evolution of prosocial behaviors.


2021 ◽  
Author(s):  
Michael Chamunorwa ◽  
Lisa-Maria Müller ◽  
Tjado Ihmels ◽  
Dennis Diekmann ◽  
Heiko Müller ◽  
...  
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