scholarly journals Summary on the application of the gravitational method to eliminate intercasing pressure

2021 ◽  
Vol 3 (3) ◽  
pp. 43-51
Author(s):  
K. T. Yershiev ◽  
D. А. Akhmetov ◽  
Y. K. Aitkulov ◽  
M. Koldey ◽  
A. Zh. Naukenov ◽  
...  

This article provides an analysis and summary of the experience of using the gravity method for eliminating annular pressure in the wells of the subsidiaries and dependent companies of JSC NC KazMunayGas. The analysis used the actual data obtained immediately after the elimination of annular pressure in wells using a compositional composition on a hydrocarbon basis by gravity replacement of annular fluid.

2020 ◽  
Vol 4 (1) ◽  
pp. 51-63
Author(s):  
Peter Neuhaus ◽  
Chris Jumonville ◽  
Rachel A. Perry ◽  
Roman Edwards ◽  
Jake L. Martin ◽  
...  

AbstractTo assess the comparative similarity of squat data collected as they wore a robotic exoskeleton, female athletes (n=14) did two exercise bouts spaced 14 days apart. Data from their exoskeleton workout was compared to a session they did with free weights. Each squat workout entailed a four-set, four-repetition paradigm with 60-second rest periods. Sets for each workout involved progressively heavier (22.5, 34, 45.5, 57 kg) loads. The same physiological, perceptual, and exercise performance dependent variables were measured and collected from both workouts. Per dependent variable, Pearson correlation coefficients, t-tests, and Cohen's d effect size compared the degree of similarity between values obtained from the exoskeleton and free weight workouts. Results show peak O2, heart rate, and peak force data produced the least variability. In contrast, far more inter-workout variability was noted for peak velocity, peak power, and electromyography (EMG) values. Overall, an insufficient amount of comparative similarity exists for data collected from both workouts. Due to the limited data similarity, the exoskeleton does not exhibit an acceptable degree of validity. Likely the cause for the limited similarity was due to the brief amount of familiarization subjects had to the exoskeleton prior to actual data collection. A familiarization session that accustomed subjects to squats done with the exoskeleton prior to actual data collection may have considerably improved the validity of data obtained from that device.


2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.


Author(s):  
Reinhold Steinacker

AbstractTime series with a significant trend, as is now being the case for the temperature in the course of climate change, need a careful approach for statistical evaluations. Climatological means and moments are usually taken from past data which means that the statistics does not fit to actual data anymore. Therefore, we need to determine the long-term trend before comparing actual data with the actual climate. This is not an easy task, because the determination of the signal—a climatic trend—is influenced by the random scatter of observed data. Different filter methods are tested upon their quality to obtain realistic smoothed trends of observed time series. A new method is proposed, which is based on a variational principle. It outperforms other conventional methods of smoothing, especially if periodic time series are processed. This new methodology is used to test, how extreme the temperature of 2018 in Vienna actually was. It is shown that the new annual temperature record of 2018 is not too extreme, if we consider the positive trend of the last decades. Also, the daily mean temperatures of 2018 are not found to be really extreme according to the present climate. The real extreme of the temperature record of Vienna—and many other places around the world—is the strongly increased positive temperature trend over the last years.


1978 ◽  
Vol 21 (2) ◽  
pp. 98-104
Author(s):  
W. L. Honig ◽  
C. R. Carlson
Keyword(s):  

2001 ◽  
Vol 15 (4) ◽  
pp. 11-28 ◽  
Author(s):  
John DiNardo ◽  
Justin L Tobias

We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to “simpler” parametric methods.


PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0163504 ◽  
Author(s):  
Xuechen Xiong ◽  
Chao Jin ◽  
Haile Chen ◽  
Li Luo

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