Evaluating Different Approaches to Permeability Prediction in a Carbonate Reservoir

Permeability can be directly measured using cores taken from the reservoir in the laboratory. Due to high cost associated with coring, cores are available in a limited number of wells in a field. Many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. The main objective of this study is to assess different approaches to the prediction of the estimation of permeability in a heterogeneous carbonate reservoir, i.e. Fahliyan formation in the southwest of Iran. The considered methods may be categorized in four groups, namely a) empirical models (Timur and Dual-Water), b) regression analysis (simple and multiple), c) clustering methods like MRGC (multi-resolution graph-based clustering), SOM (self organizing map), DC (dynamic clustering) and AHC (ascending hierarchical clustering), and d) artificial intelligence techniques such as ANN (artificial neural network), fuzzy logic, and neuro-fuzzy. This study shows that clustering techniques predict permeability in a heterogeneous carbonate better than other examined approaches. Among four assessed clustering methods, SOM performed better and correctly predicted local variations. Artificial intelligence techniques are average i