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

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