Polymer flood (PF) applications have increasingly been extended to medium-to-heavy oil reservoirs for enhanced displacement and sweep efficiency and pushing the recovery beyond the limits of conventional recovery techniques. The relatively low carbon footprint and gas-light nature has made PF attractive in many cases compared to the traditional thermal methods. Consequently, many fields in the Sultanate of Oman with viscosities ranging from about 90cP to 500cP have been studied and field trialled for polymer development, and one such field has been successfully undergoing field-scale PF for over 8 years, which is the subject of this study.
As PF is matured in the field, the ongoing challenge is to support production operations and optimise flood performance.
This study lays the foundation for holistic simulation study targeting theoretical based considerations for PF optimization. It starts with understanding the nature of polymer/polymer and polymer/water type displacements and stabilities, and encompasses modelling the phenomena of viscous fingering/instabilities in a range of model set-ups, starting from high resolution core-scale 2D models to 3D sector models incorporating varying degrees of geological heterogeneities.
Understanding of displacement stability gained with high-resolution models is extended to investigate polymer/water mixing, Water-Alternating-Polymer (WAP) recovery and polymer grading (tapering). These subjects have been integrated to emphasise the optimal polymer slug size requirement with creaming curve analyses that build on the principles of containing fingers/instabilities due to lower viscosity follow-up slugs or chase water.
The polymer flood optimization is taken to the next step by investigating the concepts of polymer grading. Three prevalent grading concepts proposed by Claridge (, , , ), Ligthelm-Schulte and Stegemeier in combination with different mixing rules form the basis of polymer grading assessment.
The study highlights significant scope for optimizing polymer flood in the field both in terms of long-term improved recovery performance at reduced cost as well as tackling the short-term operational challenges, potentially impacting the business bottom-line.