A simple technique is proposed in this paper for estimating the coefficient of permeability an unsaturated soil based on physical properties soils that include grain size analysis, degree saturation or water content, and porosity soil. The method requires soil-water characteristic curve prediction as most conventional methods. procedure to define hydraulic conductivity function from which measu...

In this study, Caco-2 permeability results from different laboratories were compared. Six different sets of apparent permeability coefficient (Papp) values reported in the literature were compared to experimental Papp obtained in our laboratory. The differences were assessed by determining the root mean square error (RMSE) values between the datasets, which reached levels as high as 0.581 for t...

We evaluate the potential of permeability prediction original and modified versions Schlumberger-Doll Research (SDR) equation that is applied to data nuclear magnetic resonance (NMR) relaxometry. Different definitions characteristic relaxation time are considered. In a further modification, pore radius replaces in SDR equation. Only good estimate surface relaxivity enables reliable transformati...

In many civil engineering practices like design of landfills, earth dams, pavements and agricultural issues, it is necessary to know the permeability coefficient of soils. In situ tests are frequently used to predict permeability of the soil. On the other hand, high ability of Artificial Neural Networks (ANNs) in prediction of nonlinear behavior has attracted the attention of many researchers. ...

Permeability prediction problem has been examined using several methods such as empirical formulas, regression analysis and intelligent systems especially neural networks and fuzzy logic. This study proposes an improved and novel model for predicting permeability from conventional well log data. The methodology is integration of empirical formulas, multiple regression and neuro-fuzzy in a commi...

The classification of well-log responses into separate flow units for generating local permeability models is often used to predict the spatial distribution of permeability in heterogeneous reservoirs. The present research can be divided into two parts; first, the nuclear magnetic resonance (NMR) log parameters are employed for developing a relationship between relaxation time and reservoir poro...

Permeability, the ability of rocks to flow hydrocarbons, is directly determined from core. Due to high cost associated with coring, many techniques have been suggested to predict permeability from the easy-to-obtain and frequent properties of reservoirs such as log derived porosity. This study was carried out to put clustering methods (dynamic clustering (DC), ascending hierarchical clustering ...

Permeability is an important parameter connected with oil reservoir. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way o...