AN INTEGRATED APPROACH OF PERMEABILITY DETERMINATION USING HYDRAULIC FLOW UNIT AND ARTIFICIAL NEURAL NETWORK METHODS IN THE KMJ FIELD

Hariyadia, Hariyadia and Kristanto, Dedy and Saputroa, Emanuel Jiwandono and Aliefan, Tubagus Adam and Mamesah, Jerry Devios (2023) AN INTEGRATED APPROACH OF PERMEABILITY DETERMINATION USING HYDRAULIC FLOW UNIT AND ARTIFICIAL NEURAL NETWORK METHODS IN THE KMJ FIELD. JOURNAL OF EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 66 (2). pp. 9-21. ISSN 1006-3080)

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Abstract

This study discussed on determining of reservoir rock permeability in the challenging and heterogeneous
of KMJ Oil Field. Through the integration of well log and core data, the difficulty of permeability
determination in this field compared to more homogeneous reservoirs could be handled. The
permeability determination is based on well log analysis, which is validated against permeability obtained
from core analysis or well testing. Selecting the appropriate permeability method is crucial for accurately
calculating reservoir rock transmissibility in dynamic models. The analysis begins with the calculation of
physical properties, such as Vshale rocks and porosity, derived from well log analysis, which is further
validated against mud log data and core porosity measurements. In core analysis, core porosity and
permeability data were utilized to calculate essential parameters including reservoir quality index (RQI),
∅z, and flow zone indicator (FZI) cores using the Hydraulic Flow Unit (HFU) method. To determine
permeability in well intervals without core data, the Artificial Neural Network (ANN) method to predict the
FZI log was used. By combining the HFU and ANN methods, then could be generated to permeability
prediction values specific to the field conditions. In the case of the KMJ-105 well of KMJ field, the
resulting permeability predictions exhibit a deviation coefficient of 0.85 and a gradient (m) of 0.94, with
an error percentage of 9.81%. These findings demonstrate the effectiveness of the integrated of HFU and
ANN methods in permeability determination and provide valuable insights for reservoir characterization
and management.
Keywords: Permeability, Flow zone indicator, Hydraulic flow unit, Artificial neural network, Rock type

Item Type:	Article
Uncontrolled Keywords:	Permeability, Flow zone indicator, Hydraulic flow unit, Artificial neural network, Rock type
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Dr.Ir, MT Dedy Kristanto
Date Deposited:	22 Aug 2023 04:27
Last Modified:	22 Aug 2023 04:29
URI:	http://eprints.upnyk.ac.id/id/eprint/37049

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