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Prediction Model Based on an Artificial Neural Network for Rock Porosity
Hany Gamal
,
Salaheldin Elkatatny
*
*
Corresponding author for this work
Department of Petroleum Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
26
Scopus citations
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Dive into the research topics of 'Prediction Model Based on an Artificial Neural Network for Rock Porosity'. Together they form a unique fingerprint.
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Earth and Planetary Sciences
Porosity
100%
Artificial Neural Network
100%
Model
100%
Rock
100%
Parameter
33%
Accuracy
22%
Investigation
22%
Ratio
22%
Average
22%
Drilling
22%
Error
22%
Rate
22%
Petrography
11%
Estimating
11%
Sandstone
11%
Heavy Oil
11%
Correlation Coefficient
11%
Hydrocarbon Reserve
11%
Machine Learning
11%
Model Validation
11%
Reserve Estimation
11%
Economics
11%
Actuator
11%
Research
11%
Surface Pressure
11%
Time
11%
Building
11%
Approach
11%
Bicarbonate
11%
Data Set
11%
Horizon
11%
Bit
11%
String
11%
Whirl
11%
Lithology
11%
Drill
11%
Convention
11%
Tools
11%
Impact
11%
Sample
11%
Correlation
11%
Speed
11%
Training
11%
Sensitivity Analysis
11%
Engineering
Porosity
100%
Models
100%
Prediction
100%
Artificial Neural Network
100%
Artificial Neural Network Model
44%
Data Point
22%
Error
22%
Accuracy
22%
Model Parameter
11%
Dataset
11%
Conventional Method
11%
Rate of Penetration
11%
Sandstone
11%
Drilling Parameter
11%
Logging Tool
11%
Penetration Rate
11%
Rotation Speed
11%
Prediction Performance
11%
Standpipe Pressure
11%
Pump Rate
11%
Estimation
11%
Empirical Correlation
11%
Surface
11%
Economics
11%
Drilling
11%
Buildings
11%
Testing
11%
Research
11%
Sensitivity Analysis
11%
Correlation
11%
Measurement
11%
Machine Learning Technique
11%
Chemical Engineering
Neural Network
100%
Hydrocarbon
11%
Learning System
11%
Carbonate
11%