Indonesia Conference Directory

Permeability is an important reservoir property but is difficult to predict. Accurate measurement of permeability values can be obtained from core data analysis. However, this analysis is not possible to do at all interval wells in the field, so that permeability information becomes incomplete. Then, the use of artificial neural network methods can be an alternative to predict the incomplete permeability value. This study used 191 of sandstone core samples from Upper Cibulakan Formation in the North West Java Basin. These core data were used to determine hydraulic flow unit (HFU) from the reservoir, and to obtain a relationship between porosity and permeability for each HFU. The application of artificial neural network method is done by building a database of flow zone indicator (FZI) based on its relationship with log data. From this FZI value, the HFU class can be known. Afterward, the permeability value can be obtained according to the equation of the relationship between porosity and permeability at each HFU that had been generated. Based on this study, the result of permeability value is not much different from core data at the same depth, so that this method can be applied to obtain the permeability in uncored intervals.