Abstract
HPAM-Cr(3+) (partially hydrolyzed polyacrylamide-chromium ion) gels are widely used in enhancing oil recovery (EOR) due to their advantages of low cost, controllability, and high strength. The propagation distance of gels within the reservoir significantly negatively impacts their gelation performance. However, the extent of this influence remains unclear, hindering precise optimization for field applications. This study first established a gelation performance characterization method based on visual inspection, rheological parameters, and long-term stability, accurately classifying gels into five types: stable strong gel (SSG), stable weak gel (SWG), colloidal dispersion gel (CDG), unstable gel (USG), and over-crosslinked gel (OCG). Subsequently, cross-experiments were conducted using varying concentrations of HPAM and Cr(3+). Based on the contour map of visual appearance, storage modulus (G'), and water loss rate (R(w)) of the gels, distribution maps of gel morphology versus concentration were constructed. The gel performance was found to depend on the HPAM concentration and the crosslinking ratio (molar ratio of HPAM carboxyl groups to Cr(3+) ions). No gel formation occurred when the HPAM concentration was below 800 mg/L, while concentrations above 2500 mg/L effectively inhibited over-crosslinking. The crosslinking ratio range for forming SSG was 5.56 to 18.68, with an optimal value of 9.27. Furthermore, the effect of propagation distance on gelation performance was investigated through 60 m sand-packed flow experiments. Results indicated that the minimum value of the crosslinking ratio was 2.632, the stable SSG formed when the propagation distance was less than 21 m, SWG formed within the 21-34 m range, and no intact gel formed beyond 34 m. It means that only the first 35% of the designed distance formed effective SSG for plugging. Finally, an optimization method for gel dosage design was established based on the findings. This method determines the optimal gel dosage for achieving effective plugging by calculating the volume of crosslinking system passing through the target fluid diversion interface and referencing the gel morphology distribution maps. These findings provide a straightforward and effective approach for the precise design of in-depth profile control agents.