ESTIMATING PARAMETERS FOR HIERARCHICAL PERMEABILITY CORRELATION MODELS

2004 ◽  
pp. 41-54 ◽  
Author(s):  
ZHENXUE DAI ◽  
ROBERT W. RITZI ◽  
DAVID F. DOMINIC
Keyword(s):  
Correlation Models

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

scholarly journals The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Halit Cinarka ◽  
Mehmet Atilla Uysal ◽  
Atilla Cifter ◽  
Elif Yelda Niksarlioglu ◽  
Aslı Çarkoğlu
Keyword(s):  
Case Reports ◽  
Time Varying ◽  
Conditional Correlation ◽  
Web Based ◽  
Specific Symptom ◽  
Sliding Windows ◽  
Dynamic Conditional Correlation ◽  
The United Kingdom ◽  
Correlation Models ◽  
Google Search

AbstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

scholarly journals Do the Determinants of Non-Performing Loans Have a Different Effect over Time? A Conditional Correlation Approach

2021 ◽  
Vol 14 (1) ◽  
pp. 21
Author(s):  
Mariagrazia Fallanca ◽  
Antonio Fabio Forgione ◽  
Edoardo Otranto
Keyword(s):  
Correlation Coefficients ◽  
Bank Loan ◽  
Time Varying ◽  
Conditional Correlation ◽  
Subprime Mortgage ◽  
Macroeconomic Variables ◽  
Correlation Models ◽  
Mortgage Crisis ◽  
The Us ◽  
Macroeconomic Indicators

Several studies have explored the linkage between non-performing loans and major macroeconomic indicators, using a wide variety of methodologies, sometimes with different results. This occurs, we argue, because these relationships are generally derived in terms of correlation coefficients evaluated in certain time spans, which represent a sort of average level of correlations. However, such correlations are necessarily time-varying, because the relationships between bank loan indicators and macroeconomic variables could be stronger during particular periods or in correspondence with important economic events. We propose an empirical exercise using dynamic conditional correlation models, with constant and time-varying parameters. Applying these models to quarterly delinquency rates and an array of macroeconomic variables for the US, for the period 1985–2019, we find that the correlation is often negligible in this period except during periods of economic crises, in particular the early 1990 crisis and the subprime mortgage crisis.

Correlation models in flood risk analysis

2012 ◽  
Vol 105 ◽  
pp. 64-72 ◽  
Author(s):  
F.L.M. Diermanse ◽  
C.P.M. Geerse
Keyword(s):  
Risk Analysis ◽  
Flood Risk ◽  
Correlation Models ◽  
Flood Risk Analysis

DEEP DIELECTRIC-BASED WATER SATURATION IN FRESHWATER AND MIXED SALINITY ENVIRONMENTS

2021 ◽  
Author(s):  
Ping Zhang ◽  
◽  
Wael Abdallah ◽  
Gong Li Wang ◽  
Shouxiang Mark Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Dielectric Permittivity ◽  
Water Saturation ◽  
Low Frequency ◽  
Single Frequency ◽  
Model Simulations ◽  
Petrophysical Parameters ◽  
Effective Medium Model ◽  
Correlation Models ◽  
Resistivity Logs

It is desirable to evaluate the possibility of developing a deeper dielectric permittivity based Sw measurement for various petrophysical applications. The low frequency, (< MHz), resistivity-based method for water saturation (Sw) evaluation is the desired method in the industry due to its deepest depth of investigation (DOI, up to 8 ft). However, the method suffers from higher uncertainty when formation water is very fresh or has mixed salinity. Dielectric permittivity and conductivity dispersion have been used to estimate Sw and salinity. The current dielectric dispersion tools, however, have very shallow DOI due to their high measurement frequency up to GHz, which most likely confines the measurements within the near wellbore mud-filtrate invaded zones. In this study, effective medium-model simulations were conducted to study different electromagnetic (EM) induced-polarization effects and their relationships to rock petrophysical properties. Special attention is placed on the complex conductivity at 2 MHz due to its availability in current logging tools. It is known that the complex dielectric saturation interpretation at the MHz range is quite difficult due to lack of fully understood of physics principles on complex dielectric responses, especially when only single frequency signal is used. Therefore, our study is focused on selected key parameters: water-filled porosity, salinity, and grain shape, and their effects on the modeled formation conductivity and permittivity. To simulate field logs, some of the petrophysical parameters mentioned above are generated randomly within expected ranges. Formation conductivity and permittivity are then calculated using our petrophysical model. The calculated results are then mixed with random noises of 10% to make them more realistic like downhole logs. The synthetic conductivity and permittivity logs are used as inputs in a neural network application to explore possible correlations with water-filled porosity. It is found that while the conductivity and permittivity logs are generated from randomly selected petrophysical parameters, they are highly correlated with water-filled porosity. Furthermore, if new conductivity and permittivity logs are generated with different petrophysical parameters, the correlations defined before can be used to predict water-filled porosity in the new datasets. We also found that for freshwater environments, the conductivity has much lower correlation with water-filled porosity than the one derived from the permittivity. However, the correlations are always improved when both conductivity and permittivity were used. This exercise serves as proof of concept, which opens an opportunity for field data applications. Field logs confirm the findings in the model simulations. Two propagation resistivity logs measured at 2 MHz are processed to calculate formation conductivity and permittivity. Using independently estimated water-filled porosity, a model was trained using a neural network for one of the logs. Excellent correlation between formation conductivity and permittivity and water-filled porosity is observed for the trained model. This neural network- generated model can be used to predict water content from other logs collected from different wells with a coefficient of correlation up to 96%. Best practices are provided on the performance of using conductivity and permittivity to predict water-filled porosity. These include how to effectively train the neural network correlation models, general applications of the trained model for logs from different fields. With the established methodology, deep dielectric-based water saturation in freshwater and mixed salinity environments is obtained for enhanced formation evaluation, well placement, and reservoir saturation monitoring.

APPLICATION OF A MULTIDIMENSIONAL DETERMINISTIC-STATISTICAL NUMERICAL CORRELATION MODEL TO REFINE THE STRUCTURE OF THE AS11 HORIZON OF THE ZAPADNO-KAMYNSKOYE FIELD

2021 ◽  
Vol 7 (3) ◽  
pp. 123-135
Author(s):  
Anatoly M. NIKASHKIN ◽  
Alexey A. KLIMOV
Keyword(s):  
Oil And Gas ◽  
Geological Structure ◽  
Correlation Model ◽  
Gas Reservoirs ◽  
Work Related ◽  
Correlation Models ◽  
Complex Geological Structure ◽  
Geophysical Information ◽  
Well Correlation ◽  
Clay Layers

One of the primary and significant tasks in the construction of geological models of oil and gas reservoirs and development facilities is the problem of correlation of productive layers. This task, as a rule, is reduced to the identification and areal tracing of presumably even-aged oil and gas strata, horizons, and layers characterized by clear boundaries between sand strata and clay layers overlapping them. The practice of work related to modeling the structure of oil and gas horizons, layers and strata indicates that the correlation is not always unambiguous. The ambiguity is especially noticeable when correlating strata characterized by a clinoform structure, one of the examples is the Achimov strata. The most reliable basis for well correlation is GIS materials and lithological features of the interlayers forming individual layers. Clay interlayers and clay strata separating productive deposits provide valuable information when choosing a correlation model in sedimentary sections. These interlayers are characterized by the greatest consistency in area and are most clearly displayed on geophysical diagrams by the nature of the drawings of GIS curves. However, even in this case, i. e. when using the entire accumulated volume of the most diverse lithological and field-geophysical information, the correlation models of the sections turn out to be different and often even opposite. In this paper, the authors had to face a similar situation when correlating the horizon AS11 of the Zapadno-Kamynskoye field. The paper describes a method for clarifying the position of the chops of the productive horizon of oil and gas deposits using a multidimensional deterministic-statistical numerical model of the correlation of sedimentary strata. The proposed approach allows us to uniquely determine the positions of the chops in the conditions of a complex geological structure of the object, high thin-layered heterogeneity. A concrete example shows the advantages of the proposed approach in comparison with the traditional one.

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