The Prediction of Water-oil Relative Permeability Key Points Using An Adaptive Neuro-fuzzy Inference System
| Autor: | M. R. Rasaei, R. Roghanian, S. R. Asadolahpour |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Adaptive neuro fuzzy inference system
General Chemical Engineering Multivariable calculus Crossover Energy Engineering and Power Technology General Chemistry Geotechnical Engineering and Engineering Geology Regression Nonlinear system Fuel Technology Key (cryptography) Range (statistics) Biological system Relative permeability Mathematics |
| Zdroj: | Petroleum Science and Technology. 32:2004-2019 |
| ISSN: | 1532-2459 1091-6466 |
| DOI: | 10.1080/10916466.2010.493913 |
| Popis: | Adaptive neuro-fuzzy inference system (ANFIS) is a powerful nonlinear, multivariable regression technique. Here ANFIS was used to identify complex relation between water-oil relative permeability key points and rock and fluid properties. Some 260 relative permeability curves from Iranian carbonate and sandstone reservoirs were used in this study. For each curve six key points (i.e., end points and the crossover points) were considered. ANFIS was then used to predict these key points from different rock and fluid properties. The results showed that very high correlation coefficients in the range of 0.8–0.92 are achievable for Kr key points. ANFIS is a very suitable tool therefore to obtain un-normalized water-oil relative permeability curves with high accuracy when the required core and fluid properties are available. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|