Determination of Rock Type Using Hydraulic Flow Unit Concept to Predict Permeability with Artificial Neural Network - Ghanima Yasmaniar; Ratnayu Sitaresmi; Suryo Prakoso (Penerbitan Universitas Trisakti, 2018)
Permeability is one of the important of reservoir characteristics, but is difficult to predict it. The accurate permeability values can be obtained from core data analysis, but it is not possible to do at all of the well intervals in the field. This study used 191 sandstone core samples from the Upper Cibulakan Formation in the North West Java Basin. The concept of HFU (Hydraulic Flow Unit) developed by Kozeny-Carman is used to generate the relationship between porosity and permeability for each rock type. Afterward, to estimate the permeability value at uncored intervals, the statistical methods of artificial neural network based on log data are used on G-19 Well, G Field which is located in the North West Java Basin. Based on core data analysis from this research, the reservoir consists of eight HFU with different equations to estimate permeability for each HFU. From this reserarch, the results of permeability calculations at uncored intervals are not much different from the core data at the same depth. Therefore the approach of permeability prediction can be used to determine the value of permeability without performing core data analysis so that it can save the company expenses.