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Journal articles on the topic 'And Correlations'

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Author: Grafiati

Published: 27 July 2024

Last updated: 28 July 2024

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1

Prevost, A. Toby, Dan Mason, Simon Griffin, Ann-Louise Kinmonth, Stephen Sutton, and David Spiegelhalter. "Allowing for correlations between correlations in random-effects meta-analysis of correlation matrices." Psychological Methods 12, no. 4 (December 2007): 434–50. http://dx.doi.org/10.1037/1082-989x.12.4.434.

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2

Szapudi, I., A. S. Szalay, and P. Boschan. "Cluster correlations from N-point correlation amplitudes." Astrophysical Journal 390 (May 1992): 350. http://dx.doi.org/10.1086/171286.

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3

Anand, Vijayakumar, Tomas Katkus, Soon Hock Ng, and Saulius Juodkazis. "Review of Fresnel incoherent correlation holography with linear and non-linear correlations [Invited]." Chinese Optics Letters 19, no. 2 (2021): 020501. http://dx.doi.org/10.3788/col202119.020501.

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4

Auletta, Gennaro. "Correlations and Hyper-Correlations." Journal of Modern Physics 02, no. 09 (2011): 958–61. http://dx.doi.org/10.4236/jmp.2011.29114.

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5

Hu, Zheng-Da, Jicheng Wang, Yixin Zhang, and Ye-Qi Zhang. "Dynamics of Nonclassical Correlations with an Initial Correlation." Journal of the Physical Society of Japan 83, no. 11 (November 15, 2014): 114004. http://dx.doi.org/10.7566/jpsj.83.114004.

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6

Walther, Andreas, and Christof Faller. "Interaural correlation discrimination from diffuse field reference correlations." Journal of the Acoustical Society of America 133, no. 3 (March 2013): 1496–502. http://dx.doi.org/10.1121/1.4790473.

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7

Joe, Harry. "Generating random correlation matrices based on partial correlations." Journal of Multivariate Analysis 97, no. 10 (November 2006): 2177–89. http://dx.doi.org/10.1016/j.jmva.2005.05.010.

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8

Cope, Leslie, Daniel Q. Naiman, and Giovanni Parmigiani. "Integrative correlation: Properties and relation to canonical correlations." Journal of Multivariate Analysis 123 (January 2014): 270–80. http://dx.doi.org/10.1016/j.jmva.2013.09.011.

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9

Neff, T., and H. Feldmeier. "Tensor correlations in the unitary correlation operator method." Nuclear Physics A 713, no. 3-4 (January 2003): 311–71. http://dx.doi.org/10.1016/s0375-9474(02)01307-6.

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10

Kermarrec, Gaël, and Steffen Schön. "Taking correlations into account: a diagonal correlation model." GPS Solutions 21, no. 4 (September 6, 2017): 1895–906. http://dx.doi.org/10.1007/s10291-017-0665-y.

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11

Andreev, I. "Correlation length versus radius in Bose-Einstein correlations." Nuclear Physics A 525 (April 1991): 527–30. http://dx.doi.org/10.1016/0375-9474(91)90377-i.

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12

Lipa, P., P. Carruthers, H. C. Eggers, and B. Buschbeck. "The correlation integral as probe of multiparticle correlations." Physics Letters B 285, no. 3 (July 1992): 300–308. http://dx.doi.org/10.1016/0370-2693(92)91468-o.

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13

Ehling, Paul, and Christian Heyerdahl-Larsen. "Correlations." Management Science 63, no. 6 (June 2017): 1919–37. http://dx.doi.org/10.1287/mnsc.2015.2413.

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14

Guo, Zhihua, Huaixin Cao, and Zhengli Chen. "Distinguishing classical correlations from quantum correlations." Journal of Physics A: Mathematical and Theoretical 45, no. 14 (March 23, 2012): 145301. http://dx.doi.org/10.1088/1751-8113/45/14/145301.

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15

Khrennikov, Andrei. "Quantum correlations from classical Gaussian correlations." Journal of Russian Laser Research 30, no. 5 (September 2009): 472–79. http://dx.doi.org/10.1007/s10946-009-9095-9.

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16

Eliazar, Iddo. "From micro-correlations to macro-correlations." Annals of Physics 374 (November 2016): 138–61. http://dx.doi.org/10.1016/j.aop.2016.07.027.

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17

Hamilton, A. J. S., and J. R. ,. III Gott. "Cluster-cluster correlations and constraints on the correlation hierarchy." Astrophysical Journal 331 (August 1988): 641. http://dx.doi.org/10.1086/166587.

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18

Arias de Saavedra, F., and E. Buendía. "σz-dependent correlations with other correlation mechanisms in liquidHe3." Physical Review B 46, no. 21 (December 1, 1992): 13934–41. http://dx.doi.org/10.1103/physrevb.46.13934.

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19

Høstmark, Arne Torbjørn. "DISTRIBUTION DEPENDENT CORRELATIONS: A MATHEMATICAL PRINCIPLE UTILIZED IN PHYSIOLOGY, OR CORRELATION BIAS?" International Journal of Research -GRANTHAALAYAH 8, no. 11 (November 27, 2020): 63–75. http://dx.doi.org/10.29121/granthaalayah.v8.i11.2020.1470.

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In many studies, we may raise the question of whether relative amounts of particular variables are positively or negatively associated, but investigations specifically focusing upon this issue seem hard to find. Previously, we reported some general rules for associations between relative amounts of positive scale variables. The main research question of the present work was: How are correlations between percentages of the same sum brought about? One particular feature of such correlations seemed to be that distributions (ranges) of the variables were crucial for obtaining either positive or negative correlations, and for their strength, suggesting the name Distribution Dependent Correlations (DDC). Certainly, such correlations might cause bias. However, previous findings raise the question of whether DDC might have a physiological relevance as well. In the current work, we extend and systematize theoretical considerations, and show results of computer experiments to test the hypotheses. Finally, we briefly mention a couple of examples from physiology. The results seem to support the idea that true, within-person distributions of the variables are crucial for obtaining positive or negative correlations between their relative amounts, raising the question of whether evolution might utilize DDC to regulate metabolism.

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20

Keating, J. P., and D. J. Smith. "Twin prime correlations from the pair correlation of Riemann zeros." Journal of Physics A: Mathematical and Theoretical 52, no. 36 (August 13, 2019): 365201. http://dx.doi.org/10.1088/1751-8121/ab3521.

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21

Ariunbold, Gombojav O., Yuri V. Rostovtsev, Vladimir A. Sautenkov, and Marlan O. Scully. "Intensity correlation and anti-correlations in coherently driven atomic vapor." Journal of Modern Optics 57, no. 14-15 (April 15, 2010): 1417–27. http://dx.doi.org/10.1080/09500341003777905.

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22

Iskhakov, R. S., V. A. Ignatchenko, S. V. Komogortsev, and A. D. Balaev. "Study of magnetic correlations in nanostructured ferromagnets by correlation magnetometry." Journal of Experimental and Theoretical Physics Letters 78, no. 10 (November 2003): 646–50. http://dx.doi.org/10.1134/1.1644310.

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23

Fataftah, Hiba, and Wael Karain. "Detecting protein atom correlations using correlation of probability of recurrence." Proteins: Structure, Function, and Bioinformatics 82, no. 9 (April 18, 2014): 2180–89. http://dx.doi.org/10.1002/prot.24574.

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24

Ryu, J., Y. i. Jo, and S. H. Lee. "Correlation between Signal Correlations and Noise Correlations among Local Cortical Populations Reveals the Functional Architecture of Early Visual Cortex." Journal of Vision 12, no. 9 (August 10, 2012): 1306. http://dx.doi.org/10.1167/12.9.1306.

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25

Nuzum, C. Thomas. "Morphological Correlations." Science 229, no. 4712 (August 2, 1985): 428. http://dx.doi.org/10.1126/science.229.4712.428.b.

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26

Nicolae, Moroianu, and Moroianu Daniela. "Inflations Correlations." Annales Universitatis Apulensis Series Oeconomica 3, no. 8 (July 31, 2006): 109–12. http://dx.doi.org/10.29302/oeconomica.2006.8.3.19.

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27

Latham, Peter E. "Correlations demystified." Nature Neuroscience 20, no. 1 (December 27, 2016): 6–8. http://dx.doi.org/10.1038/nn.4455.

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28

Li, Wentian, and Kunihiko Kaneko. "DNA correlations." Nature 360, no. 6405 (December 1992): 635–36. http://dx.doi.org/10.1038/360635b0.

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29

Munson, Peter J., Ronald C. Taylor, and George S. Michaels. "DNA correlations." Nature 360, no. 6405 (December 1992): 636. http://dx.doi.org/10.1038/360636a0.

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30

Olkin, Ingram, and Jeremy D. Finn. "Correlations redux." Psychological Bulletin 118, no. 1 (July 1995): 155–64. http://dx.doi.org/10.1037/0033-2909.118.1.155.

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31

Wesson, John. "Tricky correlations." Physics World 29, no. 5 (May 2016): 22. http://dx.doi.org/10.1088/2058-7058/29/5/34.

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32

Aspden, H. "Constant correlations." American Journal of Physics 54, no. 11 (November 1986): 967. http://dx.doi.org/10.1119/1.14829.

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33

Zahn, Rainer. "Core correlations." Nature 371, no. 6495 (September 1994): 289–90. http://dx.doi.org/10.1038/371289a0.

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34

Balucani, Umberto, M. Howard Lee, and Valerio Tognetti. "Dynamical correlations." Physics Reports 373, no. 6 (January 2003): 409–92. http://dx.doi.org/10.1016/s0370-1573(02)00430-1.

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35

NUZUM, C. T. "Morphological Correlations." Science 229, no. 4712 (August 2, 1985): 428. http://dx.doi.org/10.1126/science.229.4712.428-a.

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36

Baba, Kunihiro, and Ritei Shibata. "Multiplicative Correlations." Annals of the Institute of Statistical Mathematics 58, no. 2 (March 15, 2006): 311–26. http://dx.doi.org/10.1007/s10463-006-0036-x.

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37

Luo, Shun-Long, and Nan Li. "Quantum Correlations Reduce Classical Correlations with Ancillary Systems." Chinese Physics Letters 27, no. 12 (December 2010): 120304. http://dx.doi.org/10.1088/0256-307x/27/12/120304.

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38

Wang, Min, Fang Chen, Tao Lu, and Jianping Dong. "Bayesian t-tests for correlations and partial correlations." Journal of Applied Statistics 47, no. 10 (November 21, 2019): 1820–32. http://dx.doi.org/10.1080/02664763.2019.1695760.

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39

Copi, Craig J., James Gurian, Arthur Kosowsky, Glenn D. Starkman, and Hezi Zhang. "Exploring suppressed long-distance correlations as the cause of suppressed large-angle correlations." Monthly Notices of the Royal Astronomical Society 490, no. 4 (October 24, 2019): 5174–81. http://dx.doi.org/10.1093/mnras/stz2962.

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ABSTRACT The absence of large-angle correlations in the map of cosmic microwave background temperature fluctuations is among the well-established anomalies identified in full-sky and cut-sky maps over the past three decades. Suppressed large-angle correlations are rare statistical flukes in standard inflationary cosmological models. One natural explanation could be that the underlying primordial density perturbations lack correlations on large distance scales. To test this idea, we replace Fourier modes by a wavelet basis with compact spatial support. While the angular correlation function of perturbations can readily be suppressed, the observed monopole- and dipole-subtracted correlation function is not generally suppressed. This suggests that suppression of large-angle temperature correlations requires a mechanism that has both real-space and harmonic-space effects.

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40

Rosnow, Ralph L., Robert Rosenthal, and Donald B. Rubin. "Contrasts and Correlations in Effect-Size Estimation." Psychological Science 11, no. 6 (November 2000): 446–53. http://dx.doi.org/10.1111/1467-9280.00287.

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This article describes procedures for presenting standardized measures of effect size when contrasts are used to ask focused questions of data. The simplest contrasts consist of comparisons of two samples (e.g., based on the independent t statistic). Useful effect-size indices in this situation are members of the g family (e.g., Hedges's g and Cohen's d) and the Pearson r. We review expressions for calculating these measures and for transforming them back and forth, and describe how to adjust formulas for obtaining g or d from t, or r from g, when the sample sizes are unequal. The real-life implications of d or g calculated from t become problematic when there are more than two groups, but the correlational approach is adaptable and interpretable, although more complex than in the case of two groups. We describe a family of four conceptually related correlation indices: the alerting correlation, the contrast correlation, the effect-size correlation, and the BESD (binomial effect-size display) correlation. These last three correlations are identical in the simple setting of only two groups, but differ when there are more than two groups.

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41

Visscher, Peter M. "On the Sampling Variance of Intraclass Correlations and Genetic Correlations." Genetics 149, no. 3 (July 1, 1998): 1605–14. http://dx.doi.org/10.1093/genetics/149.3.1605.

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Abstract Widely used standard expressions for the sampling variance of intraclass correlations and genetic correlation coefficients were reviewed for small and large sample sizes. For the sampling variance of the intraclass correlation, it was shown by simulation that the commonly used expression, derived using a first-order Taylor series performs better than alternative expressions found in the literature, when the between-sire degrees of freedom were small. The expressions for the sampling variance of the genetic correlation are significantly biased for small sample sizes, in particular when the population values, or their estimates, are close to zero. It was shown, both analytically and by simulation, that this is because the estimate of the sampling variance becomes very large in these cases due to very small values of the denominator of the expressions. It was concluded, therefore, that for small samples, estimates of the heritabilities and genetic correlations should not be used in the expressions for the sampling variance of the genetic correlation. It was shown analytically that in cases where the population values of the heritabilities are known, using the estimated heritabilities rather than their true values to estimate the genetic correlation results in a lower sampling variance for the genetic correlation. Therefore, for large samples, estimates of heritabilities, and not their true values, should be used.

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42

He, Yan, Chao Tang, and Dongsheng Chen. "Evaluation of heat transfer correlations for two-phase flow boiling in twisted tapes inserted tubes." Journal of Physics: Conference Series 2758, no. 1 (April 1, 2024): 012016. http://dx.doi.org/10.1088/1742-6596/2758/1/012016.

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Abstract Many experimental researches were conducted in the twisted tapes inserted tubes, and many correlations were proposed and evaluated. However, most of the evaluations were based on specific experimental data, and the number of correlations evaluated was limited in each paper. This paper conducted a review of correlations and experimental investigations of the two-phase flow boiling heat transfer coefficient for twisted tapes inserted tubes, the key forms of the 5 correlations were reviewed, and the prediction accuracy of the 5 correlations was evaluated against the 508 published experimental data. This paper proposed a new correlation which has a MAD of 17.2%, 10.2% lower than the best existing correlation.

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43

Joe, George W., and Jorge L. Mendoza. "The Internal Correlation: Its Applications in Statistics and Psychometrics." Journal of Educational Statistics 14, no. 3 (September 1989): 211–26. http://dx.doi.org/10.3102/10769986014003211.

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The internal correlation, a measure of dependency in a set of variables, is discussed and generalized. This coefficient is an upper bound to the product moment correlations, multiple correlations, and canonical correlations that can be defined in a set of variables. Applications of the internal correlation coefficient and its generalizations are given for a number of data-analytic situations. Where appropriate, we discuss tests of significance. We illustrate the internal correlation and expand the concept to a series of additional indices: local internal, up-internal, and down-internal correlations. Uses of these indices are illustrated in several areas: multicollinearity, ridge regression, factor analysis, principal components analysis, and test reliability.

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44

Liang, Ke, Sihang Zhou, Meng Liu, Yue Liu, Wenxuan Tu, Yi Zhang, Liming Fang, Zhe Liu, and Xinwang Liu. "Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 8733–41. http://dx.doi.org/10.1609/aaai.v38i8.28719.

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Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlations underlying crime are ignored, thus hindering the performance of previous models. Specifically, there are two spatial correlations and one temporal correlation, i.e., (1) co-occurrence of different types of crimes (type spatial correlation), (2) the closer to the crime center, the more dangerous it is around the neighborhood area (neighbor spatial correlation), and (3) the closer between two timestamps, the more relevant events are (hawkes temporal correlation). To this end, we propose Hawkes-enhanced Spatial-Temporal Hypergraph Contrastive Learning framework (HCL), which mines the aforementioned correlations via two specific strategies. Concretely, contrastive learning strategies are designed for two spatial correlations, and hawkes process modeling is adopted for temporal correlations. Extensive experiments demonstrate the promising capacities of HCL from four aspects, i.e., superiority, transferability, effectiveness, and sensitivity.

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45

Nyberg, Nils T., Jens Ø. Duus, and Ole W. Sørensen. "Heteronuclear Two-Bond Correlation: Suppressing Heteronuclear Three-Bond or Higher NMR Correlations while Enhancing Two-Bond Correlations Even for Vanishing2JCH." Journal of the American Chemical Society 127, no. 17 (May 2005): 6154–55. http://dx.doi.org/10.1021/ja050878w.

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46

Shah, Mirza M. "Further Study and Development of Correlations for Heat Transfer during Subcooled Boiling in Plain Channels." Fluids 8, no. 9 (August 31, 2023): 245. http://dx.doi.org/10.3390/fluids8090245.

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The author’s published correlations for subcooled boiling in channels are further studied and developed in this work. The areas explored include choice of equivalent diameters for annuli and partially heated channels, effects of flow direction, micro-gravity, and orientation of heated surface. A new correlation is developed, which is a modification of the author’s earlier correlation. The author’s previous correlations and the new correlation are compared with a very wide range of test data for round tubes, rectangular channels, and annuli. Several other correlations are also compared with the same data. The authors’ correlations provide good agreement with data, the new correlation giving the least deviation. The data included hydraulic diameters from 0.176 to 22.8 mm, reduced pressure from 0.0046 to 0.922, subcooling from 0 to 165 K, mass flux from 59 to 31,500 kgm−2s−1, all flow directions, and terrestial to micro gravity. The new correlation has mean absolute deviation (MAD) of 13.3% with 2270 data points from 49 sources. Correlations by others had MAD of 18% to 116%. The results are presented and discussed.

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47

Elghannay, Husam A., and Yousef M. F. El Hasadi. "DEVELOPMENT OF DRAG COEFFICIENT CORRELATIONS FOR CIRCULAR CYLINDER USING SYMBOLIC REGRESSION." Al-Mukhtar Journal of Engineering Research 7, no. 1 (May 10, 2024): 20–28. http://dx.doi.org/10.54172/wadd2q70.

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The current paper provides a symbolic regression-based correlation for the drag coefficient for circular cylinder. The correlation is intended to be applicable over a wide range of flow regimes namely that range from the creeping flow regime up to the turbulent flow regime. Demo version of TuringBot symbolic regression software was used to develop different correlations using different sets of data. Experimental set of data was used in one run whereas steady numerical results for Reynolds number up to ~ 25 were used in generating a second set of formulas. In a different run data generated using Sucker and Brauwer (Wärme-und Stoffübertragung, 1975. 8: p. 149-158) correlation with uniform distribution in each order of magnitude was used to obtain a different set of correlations. The data was generated across five orders of magnitude of change of Re. Among all suggested formulas in the three cases, four correlations are considered for their simplicity and accuracy. The predictions of the correlations ranged from reasonably good to very good as compared to existing data and correlations. The relative error of the four developed correlations ranged between 9% and 16% when compared to experimental data for Reynolds numbers ranging from 1-105. In particular, one correlations was able to capture all the qualitative and quantitative changes in the drag coefficient over the different flow regimes for 0.15 < Re < 105. The relative error of this correlation was comparable to Sucker and Brauwer correlation.

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48

von Frese, Ralph R. B., Michael B. Jones, Jeong Woo Kim, and Jeong‐Hee Kim. "Analysis of anomaly correlations." GEOPHYSICS 62, no. 1 (January 1997): 342–51. http://dx.doi.org/10.1190/1.1444136.

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Recognizing correlations between data sets is the basis for rationalizing geophysical interpretation and theory. Procedures are presented that constitute an effective process for identifying correlative features between two or more digital data sets. The procedures include the development of normalization factors from the mean and variance properties of the data sets. Using these factors, the data sets may be transformed so that they have common amplitude ranges, means, and variances, thereby allowing a common graphical representation of the data sets that facilitates the visualization of feature correlations. Anomaly features that show direct, inverse, or no correlations between data sets may be separated by the application of correlation filters in the frequency domains of the data sets. The correlation filter passes or rejects wavenumbers between coregistered data sets based on the correlation coefficient between common wavenumbers as given by the cosine of their phase difference. Standardizing and summing the filtered outputs where directly correlative features have been enhanced yields local favorability indices that optimize the perception of these features. Differencing the standardized outputs where inversely correlative features have been enhanced, on the other hand, provides favorability indices that improve the perception of the inverse correlations. This study includes a generic example, as well as magnetic and gravity anomaly profile examples that illustrate the usefulness of these procedures for extracting correlative features between digital data sets.

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49

Al-Shammasi, A. A. "A Review of Bubblepoint Pressure and Oil Formation Volume Factor Correlations." SPE Reservoir Evaluation & Engineering 4, no. 02 (April 1, 2001): 146–60. http://dx.doi.org/10.2118/71302-pa.

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Summary This paper evaluates published correlations and neural-network models for bubblepoint pressure (pb) and oil formation volume factor (Bo) for their accuracy and flexibility in representing hydrocarbon mixtures from different locations worldwide. The study presents a new, improved correlation for pb based on global data. It also presents new neural-network models and compares their performances to numerical correlations. The evaluation examines the performance of correlations with their original published coefficients and with new coefficients calculated based on global data, data from specific geographical locations, and data for a limited oil-gravity range. The evaluation of each coefficient class includes geographical and oil-gravity grouping analysis. The results show that the classification of correlation models as most accurate for a specific geographical area is not valid for use with these two fluid properties. Statistical and trend performance analysis shows that some published correlations violate the physical behavior of hydrocarbon fluid properties. Published neural-network models need more details to be reproduced. New developed models perform better but suffer from stability and trend problems. Introduction Solutions to reservoir performance problems at various stages of reservoir life require knowledge of the physical properties of reservoir fluids at elevated pressures and temperatures. The pressure/volume/temperature (PVT) properties for reservoir hydrocarbon mixtures are usually obtained from laboratory analysis of preserved or recombined reservoir fluid samples. Because experimental facilities are not always available, other means for estimating PVT properties have been developed; during the last 50 years, many correlations have been developed for this purpose. PVT properties are a function of temperature, pressure, composition of the hydrocarbon mixture, and the presence of paraffins and impurities. The performance of an empirical model depends mainly on how accurately a correlation model represents this mixture under specific conditions. The purpose of this paper is to study the performance of models available in the literature, based on published experimental data. The study was carried out to model the pb and the Bo at and below the pb. Both empirical correlations and neural-network models were considered to reach a clearer understanding about what model to use and what to expect. A large global database gathered for this study was used to develop correlation models that predict oil properties better than existing models. Literature Review Since the 1940's, engineers in the United States have realized the importance of developing empirical correlations for PVT properties. Studies carried out in this field resulted in the development of new correlations. Several studies of this kind were published by Katz,1 Standing,2 Lasater,3 and Cronquist.4 For several years, these correlations were the only source available for estimating PVT properties when experimental data were unavailable. In the last 20 years there has been an increasing interest in developing new correlations for crude oils obtained from various regions in the world. Vazquez and Beggs,5 Glaso,6 Al-Marhoun,7,8 and Abdul-Majeed and Salman9 carried out some of the recent studies. The following presents a review of the best-known correlation models published in the literature. A summary of these published correlation models is provided in the Appendix (Tables 1 and 2), including the forms of correlation used, errors reported by each author, and details of the data used for each development. Empirical Correlations. In 1942, Katz1 published a graphical correlation for predicting Bo. Katz1 used U.S. midcontinent crude to develop his correlations. The graphical correlation uses reservoir temperature, pressure, solution gas/oil ratio (GOR), oil gravity, and gas gravity. The correlations were presented only in graphical form and were hard to use because they required the use of graphs and calculations in combination. In 1947, Standing2,10,11 published his correlations for pb and for Bo. The correlations were based on laboratory experiments carried out on 105 samples from 22 different crude oils in California, U.S.A. The correlations treated the pb and the Bo as a function of the reservoir temperature, GOR, oil gravity, and gas gravity. Standing's2,10,11 correlations were the first to use these four parameters, which now are commonly used to develop correlations. In fact, these correlations are the most widely used in the oil industry. Lasater3 in 1958 presented a new correlation model based on 158 samples from 137 reservoirs in Canada, the U.S., and South America. His correlation was only for pb. It is based on standard physical chemical equations of solutions. It uses Henry's law constant and the observation that the bubblepoint ratio at different temperatures is equal to the absolute temperatures ratio for hydrocarbon systems not close to the critical point. The correlation was presented in graphical form and was used as a lookup chart. An advantage of Lasater's3 correlation is the wide variety of data sources used to develop the correlation. In 1972, Cronquist4 presented a ratio correlation based on 80 data points from 30 Gulf Coast reservoirs. The correlation is useful for the analysis of depletion-drive reservoirs when PVT analysis is not available. The method was presented in graphical form and requires an estimation of average reservoir properties. In 1976, Vazquez and Beggs5 published correlations for GOR and Bo. They started categorizing oil mixtures into two categories, above 30°API gravity and below 30°API gravity. They also pointed out the strong dependence on gas gravity and developed a correlation to normalize the gas-gravity measurement to a reference separation pressure of 100 psi. This eliminated its dependence on separation conditions. More than 6,000 data points from 600 laboratory measurements were used in developing the correlations. Glaso6 in 1978 developed correlations for pb, formation volume factor, GOR, and oil viscosity for North Sea hydrocarbon mixtures. The main feature of Glaso's6 correlations is that they account for paraffinicity by correcting the flash stock-tank-oil gravity to an equivalent corrected value with reservoir temperature and oil viscosity. They also account for the presence of nonhydrocarbons on saturation pressure by using correction factors for the presence of CO2, N2, and H2S in the total surface gases. A total of 45 oil samples, most of which came from the North Sea region, were used in the development of these correlations. In 1988, Al-Marhoun8 published new correlations for estimating pb and Bo for Middle East oils. A total of 160 data sets from 69 Middle Eastern reservoirs were available for the correlation development. Al-Marhoun's7,8 correlations were the first to be developed for Middle East reservoirs.

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GALKA, ANDREAS, and GERD PFISTER. "DYNAMICAL CORRELATIONS ON RECONSTRUCTED INVARIANT DENSITIES AND THEIR EFFECT ON CORRELATION DIMENSION ESTIMATION." International Journal of Bifurcation and Chaos 13, no. 03 (March 2003): 723–32. http://dx.doi.org/10.1142/s0218127403006881.

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Abstract:

We investigate the structure of dynamical correlations on reconstructed attractors which were obtained by time-delay embedding of periodic, quasi-periodic and chaotic time series. Within the specific sampling of the invariant density by a finite number of vectors which results from embedding, we identify two separate levels of sampling, corresponding to two different types of dynamical correlations, each of which produces characteristic artifacts in correlation dimension estimation: the well-known trajectory bias and a characteristic oscillation due to periodic sampling. For the second artifact we propose random sampling as a new correction method which is shown to provide improved sampling and to reduce dynamical correlations more efficiently than it has been possible by the standard Theiler correction. For accurate numerical analysis of correlation dimension in a bootstrap framework both corrections should be combined. For tori and the Lorenz attractor we also show how to construct time-delay embeddings which are completely free of any dynamical correlations.

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