The effective displacement of the shale oil from organic nanopores plays a significant role in development of the shale oil reservoirs. In order to deeply understand the microscopic displacement mechanism of alkane of shale oil by CO2 in organic nanopores, microscopic pore model of organic matter and molecular model of CO2 and n-dodecane were established to investigate the influences of key parameters on the displacement process by using the Monte Carlo and molecular dynamics simulation method. The instantaneous adsorption of molecules demonstrates that the displacement of n-dodecane and the adsorption of CO2 are proportional to the increase of the injection pressure of CO2 as well as the pore size. In addition, the results also show that the adsorption capacity of CO2 first increases and then decreases with the increase of the temperature, which indicates that the optimum temperature exists for the adsorption of CO2. This work can provide critical insights into understanding the microscopic displacement mechanism of shale oil by CO2 in organic nanopores in shale oil reservoirs and lay a solid foundation for the CO2 flooding in the shale oil reservoir and the CO2 storage.

Coring experiments show that nanopores are extensively distributed in shale oil reservoirs and tend to be deformed when a significant pressure variation exists, and thus the dynamic capillary force phenomenon and flow mechanisms in nanopores can be significantly changed. To characterize the two-phase flow mechanisms in nanopores influenced by the synergistic effect of microcosmic pore deformation and dynamic capillary force, models based on Gassmann’s theory are established to describe the variations of pore radius and roughness in a dynamic pressure field. And then, innovative methods to quantify the dynamic capillary force phenomenon under comprehensive influence of pore size, roughness, and pressure are developed. Meanwhile, mathematical models, considering the effect of the pore deformation and dynamic capillary force, are furtherly derived to characterize the water-oil two-phase flow behavior for relatively large nanopores in shale oil reservoir, which can be used to investigate the influence of the vital parameters. The results indicate that the dynamic capillary force phenomenon turns out to be more significant when variations of pore structure and pressure are considered simultaneously. Moreover, the pore deformation and dynamic capillary force caused by pressure change can exert remarkable synergistic influence on the transport capacity for typical flow modes. Bulk modulus is one of the key factors to determine the degree of influence. An optimal pressure can be obtained to coordinate the competitive effect of seepage channel and capillary force for water-drive-oil mode with limited driving force. Based on that, emphasis should be placed on pressure control during the shale oil development process. This work theoretically underpins the quantitative characterization and the analysis of two-phase flow in shale reservoirs at the nanopore scale.

Quantitative detection of the defect size by infrared thermography is difficult. In this paper, a novel temperature integral method (TIM) is introduced for the quantitative detection of the defect size. The TIM integrates the temperature values of each pixel across the defect area to obtain the defect sizes quantitatively and conveniently. The performance of the TIM on the defect size detection is evaluated thoroughly with both experiments and simulations. Furthermore, the TIM method was compared with existing methods for quantitative detection of defect size. The results indicate robustness and accuracy of TIM.

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A lock-in thermography technique based on a periodical square wave is used to detect stainless steel plates with defects. Combining a neural network with lock-in thermography, an image processing technique is proposed, and the results are compared with traditional image processing methods. A full-field defect reconstruction technology is proposed that combines pulsed phase thermography, threshold segmentation technology, and lock-in thermography technology to reconstruct the full-field depth image. This method has fast processing speed and high detection accuracy. Finally, the effects of excitation frequency and duty cycle on thermal image quality, defect detection range, and defect detection accuracy are investigated through extensive experiments to arrive at the optimal excitation frequency and duty cycle.