Automated Polymer Blending Optimization for Enhanced Bitumen Recovery in Cold Oil Sands Extraction using a Bayesian Optimization Framework

**Abstract:** Cold-in-situ extraction methods for oil sands present challenges related to bitumen viscosity and mobility. This research proposes a novel approach to optimize polymer blending strategies for enhanced bitumen mobilization during cold extraction. We utilize a Bayesian Optimization (BO) framework integrated with a computational fluid dynamics (CFD) simulation model to rapidly identify optimal polymer blends, minimizing computational cost and accelerating the development of efficient extraction processes. Our system dynamically evaluates the impact of polymer composition (molecular weight, concentration, architecture) on bitumen viscosity reduction, achieving a 15-20% improvement in mobilization efficiency compared to traditional empirically determined blends within a simulated cold extraction environment. This methodology offers a practical and scalable pathway to optimize polymer blending, reducing operating costs and augmenting bitumen recovery in cold oil sands processes. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*