The amount and type of clays in reservoirs have a significant impact on formation evaluation and reservoir performance studies. Currently, clay typing requires either reservoir cores for laboratory analysis or advanced logs such as elemental spectral mineralogy logs, both are available in only a small fraction of wells drilled. In this study, we will explore a possibility of using commonly measured logs to estimate clay volumes. Specifically, the logs used for the study are formation resistivity (RT), total porosity (PHIT) and gamma ray (GR). Since there are no known relationships relating these logs with clay volume, machine learning has been used for data analysis and parameter prediction. This is followed by exploring the possibilities of using array induction resistivity measurements to classify clay types downhole.

An important property of clay minerals is their ability to adsorb ions on their exposed surface, which is measured by its cation exchange capacity (CEC). We have developed a method of using induction resistivity data to extract CEC downhole and displayed as depth profiles. There are four major types of clays commonly encountered in the oil fields: Kaolinite, Chlorite, Illite, and Smectite, with their CEC values ranging from low to high. Since each type of clay has its own CEC value, thus a synthetic CEC depth profile for any clay can be constructed if its volume fraction is known. On the other hand, CEC derived from the downhole resistivity data represents the combined effects of all the clay types presented in the formation being surveyed. By comparing the resistivity-based CEC profile with the synthetic ones, it is possible to define a volume fraction for each clay type, for the purpose of clay typing.

On the other hand, based on a previous developed method, total CEC representing the combined effects of all clays can be extracted from induction resistivity logs. By comparing the resistivity-based measured total CEC with the synthetic type curves, clay typing from downhole induction resistivity measurement is achieved. A workflow is developed for the application.

The proposed methodology was tested on the logs from six wells. The results indicated that RT, PHIT and GR logs have strong correlations with clay volume and the model trained with these logs could be used to predict clay volumes for blind datasets. The workflow for clay typing was tested on the logs from two wells with positive results.

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