Multiple permeability predictions using an observational learning algorithm

Computers and Geosciences

Volumn 26, Issue 8, 2000, Pages 907-913

  Wong, Patrick M ^a   Jang, Min ^b   Cho, Sungzoon ^c   Gedeon, Tom D ^d  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b Pohang University of Science and Technology (POSTECH)   (South Korea)

^c Seoul National University   (South Korea)

^d MURDOCH UNIVERSITY   (Australia)

Author keywords

Bootstrapping; Neural networks; Observational learning; Permeability; Virtual samples; Well logs

Indexed keywords

ALGORITHM; HYDROCARBON RESERVOIR; PERMEABILITY;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; HYDROCARBON RESERVOIR; PERMEABILITY; RESERVOIR CHARACTERIZATION;

EID: 0033839114     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-3004(00)00026-1     Document Type: Article

References (20)

1. State-of-the-art in permeability determination from well log data: Part 1 - A comparative study, model development. SPE 30978
2. Virtual sample generation using a population of networks
3. Estimation of rock permeability in petroleum reservoir
4. A new approach to improved log-derived permeability
5. On the approximate realization of continuous mappings by neural networks
6. Permeability prediction with artificial neural network modelling in the Venture Gas Field, offshore Eastern Canada
7. Prediction of reservoir permeability using genetic algorithms
8. Rock porosity prediction using multilayer perceptrons
9. Permeability determination from well logs and core data. SPE 27646
10. A practical Bayesian framework for back-propagation networks
11. Estimating permeability by the use of neural networks in thinly bedded shaly gas sands
12. State-of-the-art in permeability determination from well log data: Part 2 - verification, accurate permeability predictions, the touch-stone of all models. SPE 30979
13. Reducing variance of committee prediction with resampling techniques
14. Predicting permeability from porosity using artificial neural networks
15. On combining artificial neural networks
16. Permeability determination using neural networks in the Ravva Field, offshore India
17. A critical comparison of neural networks and discriminant analysis in lithofacies, porosity and permeability predictions