Prediction of centrifuge capillary pressure using machine learning techniques

Abstract

In current literature in the petroleum industry, machine learning has been used to predict capillary pressure only on the centrifugal data points and not the complete capillary pressure curves generated from existing correlations after analysis. This paper will present novel information that will benefit the petroleum industry as it shows machine learning techniques can be used to obtain the complete capillary pressure curve which is the end goal in undertaking an SCAL centrifuge experiment. This research involves testing core samples using a centrifuge set up to produce capillary pressure data points. Then, using a commercial SCAL interpretation software, the collected data is utilized to generate complete capillary pressure curves based on developed literature correlations. RCAL data for the core samples is also obtained to be used with the machine learning techniques. The machine learning models are then applied to the collected data to predict the capillary pressure curves. Optimization of the different machine learning techniques is done to improve the predictions. The results show the machine learning techniques perform very well on the validation set after being trained on the training set. The machine learning models also provide reasonable prediction of the complete capillary pressure curves on the testing data set. Changing of the machine learning technique parameters also shows the effect on the overall precision and the improvements that can be made. Further research can be done to see the effectiveness of using machine learning techniques to predict other SCAL properties such as relative permeability. This can then greatly reduce the time needed to obtain these extremely important properties for reservoir characterization.