Linear Regression Approach for Porosity and Permeability Calculations from Well Logs: A Case Study in NW Bonaparte Basin, Australia

2015 ◽  
Author(s):  
Dr. Ahmed Mohamed Ahmed Salim ◽  
◽  
Osama Akhtar Janjua ◽  
Ali Wahid ◽  
Aruba Zaheer ◽  
...  
Keyword(s):  
Linear Regression ◽  
Well Logs ◽  
Regression Approach ◽  
Porosity And Permeability

scholarly journals A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

2013 ◽  
Vol 4 (3) ◽  
pp. 25-31 ◽  
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Tahmineh Haddadi Bonab ◽  
Seyyed Reza Khaze
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Trading Volume ◽  
Regression Approach ◽  
Stock Market Trading

IMPROVED LOG EVALUATION IN A LITHOLOGICALLY COMPLEX, GLAUCONITIC RESERVOIR—A CASE STUDY

1997 ◽  
Vol 37 (1) ◽  
pp. 786
Author(s):  
Y.J. Zhang ◽  
P.A. Lollback ◽  
H.A. Salisch
Keyword(s):  
Well Logs ◽  
Improved Method ◽  
Response Characteristics ◽  
Porosity And Permeability ◽  
Subsequent Elimination ◽  
Variable Content ◽  
Environmental Corrections ◽  
Permeability Evaluation

This paper describes a case study of the application of an improved method of formation evaluation from well logs in a pilot area of the Mardie Greensand reservoirs in the Carnarvon Basin of Western Australia. They are lithologically complex reservoirs with a high and highly variable content of glauconite and extensive micro- porosity. These facts, in addition to the presence of other lithological components, make traditional log analysis, in particular the estimation of log-derived values of permeability, difficult if not impossible. The aim of this project was mainly to determine electrofacies and evaluate porosity and permeability from conventional well logs in this area. The sequential steps in the log evaluation of these glauconite-rich reservoirs were as follows: log quality control (borehole environmental corrections and depth matching); analysis of the log response characteristics; determination of litho- parameters used to identify the electrofacies; identification of the so-called hard streaks and their subsequent elimination for the purpose of reading log responses largely unaffected by these horizons; electrofacies identification and classification; porosity and permeability evaluation. The paper presents examples from several wells in the pilot area of the Mardie Greensand to illustrate this study.

An Epidemic Avian Influenza Prediction Model Based on Google Trends

2019 ◽  
Vol 16 (4) ◽  
pp. 303-310 ◽  
Author(s):  
Yi Lu ◽  
Shuo Wang ◽  
Jianying Wang ◽  
Guangya Zhou ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Avian Influenza ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Google Trends ◽  
Health Providers ◽  
Human Beings ◽  
Success Rates ◽  
New Model ◽  
Regression Approach

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

scholarly journals Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1723
Author(s):  
Ana Gonzalez-Nicolas ◽  
Marc Schwientek ◽  
Michael Sinsbeck ◽  
Wolfgang Nowak
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Model ◽  
Data Series ◽  
Additional Parameter ◽  
Sampling Strategies ◽  
Discharge Rates ◽  
Regression Approach ◽  
Event Based ◽  
Stochastic Time Series

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs.

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