An Improved Method of Clay-Induced Rock Typing Derived from Log Data in Modelling Low Salinity Water Injection: A Case Study on an Oil Field in Indonesia

Hafizh Zakyan, Asep Kurnia Permadi, Egi Adrian Pratama and Muhammad Arif Naufaliansyah
Additional contact information
Hafizh Zakyan: Department of Petroleum Engineering, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia
Asep Kurnia Permadi: Department of Petroleum Engineering, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia
Egi Adrian Pratama: Department of Petroleum Engineering, Curtin University, Bentley, WA 6102, Australia
Muhammad Arif Naufaliansyah: Department of Petroleum Engineering, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia

Energies, 2022, vol. 15, issue 10, 1-15

Abstract: Low salinity water injection (LSWI) is an emerging way to improve waterflood performance through chemical processes. The presence of clay minerals is one of the required parameters to successfully implement LSWI in sandstone formations. The ability of clays to exchange the cations, represented by cation exchange capacity (CEC), leads to oil detachment from the rock surface and changes the formation wettability toward water-wet. There are still limited studies that discuss the implementation of specific CEC models in the field-scale LSWI reservoir simulation. This paper attempts to propose an improved method of clay-induced rock typing that can be representatively implemented for field-scale reservoir simulation. The scope of this study is limited to a sandstone reservoir from an oil field in Indonesia. The oil is considered light, and the reservoir contains main clay minerals, including kaolinite and illite, and a trace of chlorite was also found from the XRD evaluation. CEC can be derived from log data, while rock type can also be estimated from log data by using the artificial neural network method. The main finding is that the combination of those variables, i.e., log data, rock properties, and CEC, results in an improved method to characterize and classify the clay into three types associated with conventional rock types. The classification obtained by the clay typing method can be utilized as an input for advanced LSWI modeling, which is expected to provide more robust results. Furthermore, dispersed clay has a strong influence on the magnitude of cation exchange capacity rather than laminar and structural clays.

Keywords: clay typing; log-derived CEC; clay distribution; log-derived HFU; machine learning; ANN; EOR; LSWI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/10/3749/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/10/3749/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:10:p:3749-:d:819548

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().