The heterogeneity of pores, an inevitable and intractable challenge during steam flooding, directly induces channeling phenomenon and seriously suppresses oil recovery. Additionally, prolonged exposure of steam exacerbates the diversion capacity of pores with different sizes, thereby exerting further influence on the shape and amount of remaining oil. Considering the microscopic aspect, the variation coefficient of pore size is remarked to characterize the heterogeneity of porous media. In this work, cores with microcomputerized tomography (micro-CT) and microfluidic models with image recognition are employed to research the oil−water distribution and occurrence of remaining oil affected by temperature and velocity in porous media. The results from core displacement experiments show that high temperature and strong heterogeneity conjointly increase the proportion of membranous oil and decrease the proportion of aggregated remaining oil, indicating that the sweep efficiency is increasing. Specially, when the temperature increases from 100 to 150 °C, the sweep efficiency exhibits a notable increase of more than 15%. In contrast, when the temperature rises from 200 to 250 °C, the corresponding increment is less than 2%. From the results of the microfluidic model, the great injection velocity and weak heterogeneity synergistically expand the horizontal sweep region. Through the examination of the macroscopic distribution and microscopic occurrence of remaining oil during steam flooding with varying injection parameters, the impact of steam flooding on heavy oil in heterogeneous reservoirs can be effectively and comprehensively demonstrated. The findings of this work offer promising application prospects in terms of optimizing the sweep efficiency through the adjustment of steam injection temperature and velocity, which can have positive impacts.
Due to the long-term scouring of steam/water flooding, the water channels restricts the expansion of streamlines in the swept region. The formation of the main streamline, an inevitable and troublesome challenge during steam/water flooding, restrict the spread of the sweep region and the oil extraction in oil reservoirs. To realize the swept main streamlines adjustment (SA), well pattern adjustment (WPA) and polymer flooding (PF) are the mature technologies applied in the development of reservoir. The WAF and PF, as two kinds of oil extracting methods with different principles and operations, is difficult to directly verify the disturbance law to main streamlines in the same model or experimental physical field. Two-dimensional sand-packed model can elucidate the mechanism of WPA and PF for SA based on the direct processing of images and data analysis of production data. Through the oil–water distribution images from displacement experiment, the influence of viscous fingering generated by streamlines development can be obtained and described by the mathematical model to illustrate the relationship between penetration intensity and mobility ratio. In addition, the dynamic production data can reflect the change of flow resistance and water cut during the expansion of swept region. Based on observations of macro and micro perspectives, the experimental results show that the WPA greatly expands the coverage region of the streamlines, while PF makes the streamlines denser in the swept region. By comparing the distribution of streamlines between the two methods, the different shapes of streamlines are deeply influenced by the mobility ratio that determines the viscous fingering and the well pattern type. Finally, the adaptability of different methods for extracting the remaining oil is proposed. The WPA pays attention to improving the macro sweep efficiency outside the swept region. Meanwhile, the PF strategy pays more attention to improving the micro sweep efficiency in the swept region. The analysis of single-factor shows that viscous fingering has an obvious interference effect on the streamline morphology development, which highlights the meaning and importance of using the synergistic effect of WPA and PF to enhance oil recovery.
Cyclic steam stimulation (CSS) is one efficient technology for enhancing heavy-oil recovery. However, after multiple cycles, steam channeling severely limits the thermal recovery because high-temperature steam preferentially breaks through to the producers. To solve the issues of steam breakthrough, it is essentially important and necessary to recognize steam channeling. In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. The set of feature attributes is constructed based on the permeability ratio, steam quality, and steam-injection speed, which provides the reference for the construction of the training-sample set, steam-channeling reconstruction set, and prediction set. Based on the realistic data, the Pearson correlation coefficient is implemented to confirm the linear correlation among different characteristics; thus, the dimension reduction of the characteristic parameters is achieved. The random oversampling method is adopted to treat the unbalanced training-sample set. Our results show that this model can accurately describe the current state of steam channeling and predict steam propagation in the following cycles.
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