Experimental study on production characteristics and enhanced oil recovery during imbibition and huff-n-puff injection in shale reservoir

With the tertiary oil recovery in the oilfield, the content of aging oil emulsion with high water content and complex components has become more prevalent, so it is crucial for aging oil to break the emulsification. In this paper, the experimental laws of water content are explored under the conditions of different transducer input powers through the ultrasonic reforming of aging oil, and the microscopic topography, particle size, components, etc. of oil samples before and after the irradiation of ultrasound are characterized through the microscopic analysis, particle size analysis and component analysis and other ways. The results show that the oil samples achieve the effect of demulsification and dehydration in the presence of ultrasonic cavitation effect, with a maximum dehydration rate of 98.24 %, and that the dehydration rate follows an "M-type" trend with the increase of power. The results of microscopic and particle size analyses demonstrate that ultrasonic irradiation destabilizes the oil-water interfacial membrane, and causes droplets of different sizes to collide, agglomerate, and settle. It was also observed that the droplets of the emulsion system are more evenly distributed and the intervals are increased. Furthermore, we hypothesize that ultrasound may be less irreversible in demulsification and dehydration of aging oil.

As an unconventional resource, shale reservoirs recently have attracted considerable attention in the petroleum industry. Shale plays are highly heterogenous vertically and laterally and are characterized by rapid changes in mineral composition. Thus, identification of dominant lithofacies is a key issue in the development of shale oil and gas reservoirs. In this study, various existing lithofacies in a shale section as a target unit in the Qingshankou Formation are divided based on organic matter content, sedimentary structure, and mineral composition. To delineate the electrofacies from the log, the multiresolution graph-based clustering (MRGC) is used to optimize the conventional logs that are sensitive to the electrofacies clustering analyses. Based on the principle of lithofacies identification, the electrofacies are artificially related to the lithofacies as well. This was done by analyzing the petrophysical characteristics of various shale lithofacies, to enable obtaining the main log parameters for the facies of the lacustrine shale section understudy. The results showed that by considering the underlying geologic criterion of each lithofacies, the MRGC method is able to correlate geophysical characteristics of each identified electrofacies for an optimal selection of six lithofacies.

The volcanic rocks of the Es3 Formation (the third member of the Eocene Shahejie) in the Liaohe Eastern Sag can be divided into four facies and twelve subfacies. The porosity spectrum, porosity bin, variation coefficient (VC), and porosity width derived from electrical imaging log data were applied to study and characterize the heterogeneity of four facies and nine subfacies, both qualitatively and quantitatively. However, the VC and porosity width cannot be used to quantitatively classify heterogeneity when the VC is small and the porosity width is large. In the present study, the authors propose a new parameter, P_vcd = variation coefficient × porosity width. Based on this parameter, using a combination of porosity spectra, porosity bin features, VC, and porosity width, lithofacies heterogeneity is divided into three categories. The first is weak heterogeneity, which has a P_vcd < 1.1, a VC < 0.15, and a porosity width < 6. The second is moderate heterogeneity, which has a P_vcd 1.1–1.6, a VC 0.15–0.25, and a porosity width 6–9. The third is strong heterogeneity, which has a P_vcd > 1.6, a porosity VC > 0.25, and a porosity width > 9. In these three cases, the porosity spectra mainly display unimodal features, the porosity bins are concentrated, the bimodal features with tails and porosity bins are less concentrated, or the multimodal features with tails and porosity bins are scattered, respectively. Favorable reservoirs of volcanic rocks are controlled by lithofacies. In the study area, the favorable reservoirs appear to be the diatreme subfacies with low or medium heterogeneity, the pyroclastic flow subfacies with low heterogeneity, the compound lava flow subfacies with low or medium heterogeneity, and the outer zone subfacies with strong heterogeneity.

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination () of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.

The influence of different reservoir spaces in shale reservoirs on imbibition recovery is a hot spot for improving shale oil recovery. However, the research on the influence of different chemical reagents on the recovery factor of different scale pores is limited, and the influence mechanism of shale imbibition recovery factor under the action of different media has not been systematically studied. Therefore, this study takes the Gulong shale oil reservoir as the research object, carries out imbibition experiments combined with nuclear magnetic resonance testing under different injection fluid conditions, quantifies the contribution of shale pores of different scales to imbibition recovery under different injection media conditions, and analyzes the influence of injection media types on imbibition recovery. The results show that the average contribution rate of different types of pores was in the order of interlayer clay (47.6%) > mesoscale pores (32.7%) > small pores (17.3%) > large pores (2.6%). The total imbibition recovery of shale with millimeter-scale sandy laminae was ranked as alkali solution (61.50%) > acid solution (60.92%) >GJ surfactant (39.79%)> distilled water (32.92%)>guanidine gum (30.38%) , and the total imbibition recovery of laminated shale was ranked as GJ surfactant (39.1%) > slick water (38.0%) > guanidine gum (34.7%) > distilled water (29.2%). Document Type: Original article Cited as: Wei, J., Wang, A., Wang, R., Yang, Y., Zhao, X., Zhou, X. Contribution of different shale storage spaces to recovery rate and mechanism of oil mobilization during imbibition. Capillarity, 2025, 15(1): 12-24. https://doi.org/10.46690/capi.2025.04.03