Analysis of Microscopic Remaining Oil Based on the Fluorescence Image and Deep Learning

Fossil fuels like oil and natural gas continue to be the primary sources of global energy. Enhancing hydrocarbon recovery from exploited reservoirs has been a major scientific concern in the petroleum industry. Following extended exploitation, the reservoir's oil-water dynamics become intricate, thereby complicating petroleum and natural gas extraction. Pore-scale analysis of microscopic remaining oil (micro-remaining oil) offers theoretical underpinning for enhancing production from high-water-cut oil reservoirs. Fluorescence thin-section analysis allows for the direct evaluation of reservoir oil-bearing properties using oil-containing samples, providing insights into the occurrence and distribution patterns of micro-remaining oil without requiring time-consuming core displacement experiments. The high resolution of fluorescence images further establishes this technique as a representative method for studying micro-remaining oil. However, conventional fluorescence image analysis methods are often subjective and labor-intensive. To address this limitation, we trained four deep learning networks-U-Net, ResU-Net, ScSEU-Net, and Unet++-and applied them innovatively to automate fluorescence image segmentation. Evaluation of network performance via statistical metrics and visual observation indicated that all four networks achieved high segmentation accuracy, particularly ResU-Net, which showed robustness against over-segmentation, under-segmentation, and image noise. Finally, leveraging optimal segmentation results, we conducted quantitative analyses of oil saturation, micro-remaining oil patterns, and pore occupancy. The study demonstrated that ternary composite agents substantially decreased the presence of cluster, film, and adsorbed oils by reducing the oil-water mobility ratio and lowering oil-water interfacial tension. Primarily, these agents displaced crude oil from pores larger than 60 micrometers in an equivalent radius, leading to a significant reduction in their content. Nevertheless, substantial quantities of micro-remaining oil are still confined in pores smaller than 50 micrometers in an equivalent radius, emphasizing the need for attention during subsequent development adjustments. Our research has notably improved the efficiency and accuracy of fluorescence image analysis, effectively supporting the enhancement of recovery in high-water-cut oil reservoirs.