Viscosity prediction using image processing and supervised learning

Abstract

The paper implements image processing followed by supervised learning to predict the viscosity of hydrocarbon present at a specific reservoir depth in a heavy oil reservoir located in California, USA. Viscosity is predicted from white-light and UV-light images of side-wall core samples extracted from specific depths along the wellbore. The fluid viscosity primarily ranges from 5 to 600 cP at a temperature of 180 degF. The white-light and UV-light images are available for 680 side-wall core samples. Sobel, Sato, Hessian, LBP, and Multi-Otsu filters extract meaningful features from red, green, and blue pixel intensities of the white-light and UV-light images. The distribution of pixel-wise values for each filtered/raw image is further processed to derive histogram-based features. As a result, 600 features are extracted for each side-wall core sample. Thresholds based on the variance, MI score, F score, and Person’s r are imposed to select the most informative features for the viscosity prediction. Compared to filtered white-light images, raw RGB white-light images have stronger viscosity association. On the contrary, filtered UV-light images have stronger viscosity association as compared to raw RGB UV-light images. Supervised learning is deployed using the resulting features in both regression and classification of target viscosity. For the prediction of continuous-valued viscosity (regression), random forest is the best performer with a mean absolute error of 27 ∓ 3 cP. For the viscosity classification, high-viscosity and low-viscosity samples can be very well detected at a F1 Weighted Score higher than 0.95 and Matthew’s Correlation Score higher than 0.9. Overall, for viscosity prediction, classification is much superior to the regression. The novel image-based viscosity prediction workflow will help lower the cost and improve the precision of laboratory-based viscosity measurements and simultaneously contribute to a high-resolution reservoir model development.