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Streamline-adjustment-assisted heterogeneous combination flooding is a new technology for enhanced oil recovery for post-polymer-flooded reservoirs. In this work, we first carried out a series of 2D visualization experiments for different chemical flooding scenarios after polymer flooding. Then, we explored the synergistic mechanisms of streamline-adjustment-assisted heterogeneous combination flooding for enhanced oil recovery and the contribution of each component. Test results show that for single heterogeneous combination flooding, the residual oil in the main streamline area after polymer flooding is ready to be driven, but it is difficult to be recovered in the non-main streamline area. Due to the effect of drainage and synergism, the streamline-adjustment-assisted heterogeneous combination flooding diverts the injected chemical agent from the main streamline area to the non-main streamline area, which consequently expands the active area of chemical flooding. Based on the results from the single-factor contribution of the quantitative analysis, the contribution of temporary plugging and profile control of branched preformed particle gels ranks in the first place and followed by the polymer profile control and the effect of streamline adjustment. On the contrary, the surfactant contributes the least to enhance the efficiency of oil displacement. [ABSTRACT FROM AUTHOR]

Simultaneous-source acquisition has been recognized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method (RLSRTM) using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a flat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneous-source data, incomplete data and noisy data. [ABSTRACT FROM AUTHOR]