The microbial communities associated with alkaline–surfactant–polymer (ASP)-flooded reservoirs have rarely been investigated. In this study, high-throughput sequencing was used to analyse the indigenous microbial communities in two different blocks, the water flooding after the alkaline–surfactant–polymer flooding block and the alkaline–surfactant–polymer flooding block, and to ascertain the optimal stage for the implementation of indigenous microbial oil recovery technology. The different displacement blocks had significant effects on the indigenous microbial community at the genus level according to an alpha diversity analysis and community composition. In water flooding after alkaline–surfactant–polymer flooding, the dominant genus of Pseudomonas exceeded 30%, increasing to 52.1% in alkaline–surfactant–polymer flooding, but alpha diversity decreased. Through a co-occurrence network analysis, it was found that the complexity of the water flooding after alkaline–surfactant–polymer flooding was higher than that of alkaline–surfactant–polymer flooding. This means that the water flooding ecosystem after alkaline–surfactant–polymer flooding was more stable and less susceptible to external environmental influences. In addition, there were significant differences in the functional redundancy of microbial communities in different blocks. In summary, the optimal stage for implementing local microbial oil recovery technology may be water flooding after alkaline–surfactant–polymer flooding.

Further exploitation of the residual oil underground in post-polymer flooded reservoirs is attractive and challenging. Microbial-enhanced oil recovery (MEOR) is a promising strategy to enhance the recovery of residual oil in post-polymer flooded reservoirs. Identifying and selectively activating indigenous microorganisms with oil displacement capabilities is an urgent requirement in the current design of efficient microbial-enhanced oil recovery technologies. This study combines high-throughput sequencing with functional network analysis to identify the core functional microbes within the reservoirs. Concurrently, it devises targeted activation strategies tailored to oligotrophic conditions through an analysis of environmental factor influences. The feasibility of these strategies is then validated through physical simulation experiments. With nutrient stimulation, the overall diversity of microorganisms decreases while the abundance of functional microorganisms increases. The core displacement results showed that the oil recovery factor increased by 3.82% on the basis of polymer flooding. In summary, this research has established a system for the efficient activation of functional microorganisms under oligotrophic conditions by utilizing bioinformatics, network analysis, and indoor simulation systems. This achievement will undoubtedly lay a solid foundation for the practical implementation of microbial enhancement techniques in the field.