Work

Prediction of dynamic retention during polymer flooding EOR in carbonate outcrops using empirical correlation

Year: 2026

Type: article

Abstract: Polymer retention is a critical parameter in polymer flooding and requires accurate measurement for successful field implementation. However, coreflood tests for measuring dynamic polymer retention are time-consuming, costly, and labor-intensive, motivating the application of correlations that map readily measurable static adsorption to dynamic retention. In this study, 36 coreflooding and 36 static adsorption tests were conducted on carbonate outcrops and crushed powder, respectively, under identical conditions to determine dynamic retention and static adsorption values. Existing mathematical correlations in the literature for predicting dynamic retention, primarily developed for sandstone outcrops under single-phase conditions, were fitted to the current dataset. In addition, empirical equations and regression models were developed to predict dynamic retention values from parameters influencing polymer retention, including static adsorption values, residual oil saturation, and the size ratio factor. An 80:20 train-test split and leave-one-out cross-validation were employed to evaluate the predictive performance of the regression models. The experimental dataset showed poor agreement with the existing correlations, primarily due to differences in rock type. Further, both a simple regression equation with static adsorption as the sole influencing parameter and a multiple linear regression equation incorporating additional parameters, including residual oil saturation and the size ratio factor, were developed to predict dynamic retention. The empirical equations demonstrated a strong fit to the experimental dataset, achieving R² values of 0.93 and 0.96, respectively. All regression models performed well in predicting dynamic retention from the influencing parameters, with R² testing values of at least 0.97.

Source: Scientific Reports

DOI: 10.1038/s41598-026-74335-4

Authors: Anoo Sebastian, Strahinja Markovic, Emad W. Al-Shalabi

Institution: Khalifa University of Science and Technology

Language: English

Topic: Enhanced Oil Recovery Techniques

Subfield: Fuel Technology

Field: Energy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: Abu Dhabi National Oil Company, Khalifa University of Science, Technology and Research