Open AccessApplication of artificial neural network to predict permeability value of the reservoir rock
Author(s) -
Ghanima Yasmaniar,
Suryo Prakoso,
Ratnayu Sitaresmi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/2/022056
Subject(s) - permeability (electromagnetism) , geology , artificial neural network , porosity , petroleum reservoir , petroleum engineering , relative permeability , well logging , geotechnical engineering , artificial intelligence , computer science , chemistry , biochemistry , membrane
Permeability is an important reservoir property but it is difficult to predict. An 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 method can be an alternative to predict the incomplete permeability values. 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. Artificial neural network was applied on G-19 and G-11 Well that had 51 of core data. 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.