Prediction Model and Application Case of Rock Drillability based on Acoustic Velocity

2020 ◽  
Author(s):  
Bo Zhou ◽  
Xun Chen ◽  
Ximo Qu ◽  
Jie Wang ◽  
Mingtao Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Acoustic Velocity ◽  
Rock Drillability

Study on Predicting Rock Drillability Based on the Method of Gray Theory

2012 ◽  
Vol 496 ◽  
pp. 423-426 ◽  
Author(s):  
Zhi Di Liu ◽  
Xiao Yan Tang
Keyword(s):  
Regression Analysis ◽  
Initial Data ◽  
Prediction Model ◽  
Well Logging ◽  
Theory Model ◽  
Oil Field ◽  
Data Process ◽  
Gray Theory ◽  
Data Regression ◽  
Rock Drillability

The conventional prediction model of rock drillability is based on regression analysis using well logging data. Regression analysis directly uses initial data to establish model, so its precision is not satisfied. By accumulating initial data, gray theory model (GM (0,N)) is able to weaken the random of initial data. Therefore, a practical approach to calculate the rock drillability, which base on GM(0,N) using well logging data, is presented in this paper. Based on the inherent relation of well logging information and rock drillability, a lot of logging parameters are selected closely to rock drillability, and the prediction model of rock drillability are established by GM(0,N). This method is applied to logging data process of Du4 well in SC oil field, the results show that it can improve predicting accuracy of rock drillability, and can easily frame rock drillability profile in some areas.

1575: External Validation of Outcome Prediction Model for Ureteral/Renal Calculi

2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Outcome Prediction ◽  
External Validation ◽  
Renal Calculi

Development of a headlamp dimming request prediction model

1975 ◽  
Author(s):  
Vivek D. Bhise ◽  
Thomas F. Swigart ◽  
Eugene I. Farber
Keyword(s):  
Prediction Model

Short-Version YLS-CMI: An Alternative Prediction Model for Juvenile Offenders at Intake

2009 ◽  
Author(s):  
Christina Campbell ◽  
Eyitayo Onifade ◽  
William Davidson ◽  
Jodie Petersen
Keyword(s):  
Juvenile Offenders ◽  
Prediction Model ◽  
Short Version

Career Choice Prediction--Model

2019 ◽  
Author(s):  
Zool Hilmi Mohamed Ashari ◽  
Norzaini Azman ◽  
Mohamad Sattar Rasul
Keyword(s):  
Prediction Model ◽  
Career Choice

Speed Prediction Model of Trucks on Gradient Sections Considering Vertical Curve

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Qianqian Liang ◽  
Xiaodong Zhang ◽  
Jinliang Xu ◽  
Yang Zhang
Keyword(s):  
Prediction Model ◽  
Speed Prediction

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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