Fractal model of spontaneous imbibition in low-permeability reservoirs coupled with heterogeneity of pore seepage channels and threshold pressure

Spontaneous imbibition(SI)is an important mechanism for enhancing oil recovery in low-permeability reservoirs.Due to the strong heterogeneity,and the non-Darcy flow,the construction of SI model for lowpermeability reservoirs is extremely challenging.Commonly,traditional SI models based on single or averaged capillary tortuosity ignore the influence of heterogeneity of pore seepage channels and the threshold pressure(TP)on imbibition.Therefore,in this work,based on capillary model and fractal theory,a mathematical model of characterizing SI considering heterogeneity of pore seepage channels is established.On this basis,the threshold pressure was introduced to determine the pore radius at which the wetted phase can displace oil.The proposed new SI model was verified by imbibition experimental data.The study shows that for weakly heterogeneous cores with permeability of 0-1 m D,the traditional SI model can characterize the imbibition process relatively accurately,and the new imbibition model can increase the coefficient of determination by 1.05 times.However,traditional model has serious deviations in predicting the imbibition recovery for cores with permeability of 10-50 m D.The new SI model coupling with heterogeneity of pore seepage channels and threshold pressure effectively solves this problem,and the determination coefficient is increased from 0.344 to 0.922,which is increased by2.68 times.For low-permeability reservoirs,the production of the oil in transitional pores(0.01-0.1μm)and mesopores(0.1-1μm)significantly affects the imbibition recovery,as the research shows that when the heterogeneity of pore seepage channels is ignored,the oil recovery in transitional pores and mesopores decreases by 7.54%and 4.26%,respectively.Sensitivity analysis shows that increasing interfacial tension,decreasing contact angle,oil-water viscosity ratio and threshold pressure will increase imbibition recovery.In addition,there are critical values for the influence of these factors on the imbibition recovery,which provides theoretical support for surfactant optimization.

Numerical analysis on the influence of vortex motion in a reverse Stairmand cyclone separator by using LES model

This study aims to analyze the influence of vortex motion in a reverse Stairmand cyclone separator by using LES model.The mathematical analysis indicated that the energy dissipation and the flow characteristics of incompressible fluid are directly related to on the vortex motion.The results of the Q criterion-based iso-vortex surface could well reflect the tendency of the vortex structure,in which the iso-vortex surface exhibited a distorted distribution rather than around the center axis.At the turning point of velocity vector,vortices were formed and developed,and the point was the center of the local vortex core.In addition,the vortex formed an irregular annular region around the wall at the bottom of vortex finder.The vortex structure near the dust hopper presented a strong distortion.Moreover,there were two rotating flow in the opposite direction within the dust hopper.These phenomena would affect the separation performance,which was significance to cyclone separator.

Extralevator abdominoperineal excision versus abdominoperineal excision for low rectal cancer: a meta-analysis

Background:Extralevator abdominoperineal excision (ELAPE) has become a popular procedure for low rectal cancer as compared with abdominoperineal excision (APE). No definitive answer has been achieved whether one is superior to the other. This study aimed to evaluate the safety and efficacy of ELAPE for low rectal cancer with meta-analysis.Methods:The Web of Science, Cochrane Library, Embase, and PubMed databases before September 2019 were comprehensively searched to retrieve comparative trials of ELAPE and APE for low rectal cancer. Pooled analyses of the perioperative variables, surgical complications, and oncological variables were performed. Odds ratio (OR) and mean differences (MD) from each trial were pooled using random or fixed effects model depending on the heterogeneity of the included studies. A subgroup analysis or a sensitivity analysis was conducted to explore the potential source of heterogeneity when necessary.Results:This meta-analysis included 17 studies with 4049 patients, of whom 2248 (55.5%) underwent ELAPE and 1801 (44.5%) underwent APE. There were no statistical differences regarding the circumferential resection margin positivity (13.0% vs. 16.2%, OR = 0.69, 95% CI = 0.42-1.14, P = 0.15) and post-operative perineal wound complication rate (28.9% vs. 24.1%, OR = 1.21, 95% CI = 0.75-1.94, P= 0.43). The ELAPE was associated with lower rate of intraoperative perforation (6.6% vs. 11.3%, OR = 0.50, 95% CI = 0.39-0.64, P < 0.001) and local recurrence (8.8% vs. 20.5%, OR = 0.29, 95% CI = 0.21-0.41, P < 0.001) when compared with APE.Conclusions:The ELAPE was associated with a reduction in the rate of intra-operative perforation and local recurrence, without any increase in the circumferential resection margin positivity and post-operative perineal wound complication rate when compared with APE in the surgical treatment of low rectal cancer.

Unbalanced classification method using least squares support vector machine with sparse strategy for steel surface defects with label noise

Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.