Gotowa bibliografia na temat „Linhai zhong xue”

ABSTRACT Slickwater or linear gel is often used for fracturing development in tight sandstone reservoirs. As it carries sand into the formation through the wellbore, its sand-carrying performance changes with the continuous change of liquid shear rates. In this paper, a new type of viscoelastic slickwater is proposed, which avoids the problems of low sand-carrying capacity of conventional slickwater and large damage of guar. Through theoretical calculation, taking the injection rate of 10 m3/min as an example, the maximum shear rate of fracturing fluid in wellbore and fracture is 884.82 s−1 and 8.33 s−1 respectively. Viscoelastic slickwater polymer is physically cross-linked and reversible associated to form a network structure. It has excellent shear resistance at different shear rates (0.01∼1000 s−1). The intersection point of viscoelastic modulus is lower than 0.021 Hz, and the viscoelastic performance is excellent. Under the low shear rates of fracture (0.01∼10 s−1), the performance of viscoelastic slickwater suspended sand is excellent, and the equilibrium height of sand bank is lower than 26.7% of fracture height. Under the high shear rates (10∼1000 s−1) in the wellbore, its viscosity is high, and the friction reduction rate can reach 81.59%. In addition, it breaks quickly, the amount of residue is low, and the conductivity can be retained by more than 90%. Under field application, the sand concentration of the system is as high as 300 kg/m3, and the production of vertical well after fracturing can reach 0.96 tons/day in more than one year. Viscoelastic slickwater can realize friction reduction and sand carrying at the same time, which has the value of popularization and application. INTRODUCTION The tight sandstone reservoir has features of tight lithology, low porosity, and low permeability, and others, mainly secondary porosity, and high feldspar and cuttings content. Due to low reservoir pressure coefficient, its natural productivity is low (Wang et al., 2022; Zhong et al., 2022). Compared with conventional reservoirs, tight sandstone reservoirs have staggered pore distribution and poor connectivity, high water saturation, strong stress sensitivity, and limited recovery by conventional oil displacement methods (Xie et al., 2022; Dong, et al.,2019). Therefore, new unconventional reservoir stimulation technologies and measures are urgently needed.

ABSTRACT In underground mining, stopes are excavated step by step, and each step has a certain length according to the mine planning. Displacements of the free surface of the stope sidewall are contributed by the excavation of stopes. Rockburst is caused by the exaction in underground stopes in rockburst proneness rock mass, especially in deep underground mines. In this paper, the stope excavation length effects on the displacement and rockburst in underground stopes induced by excavation were investigated by numerical modelling analysis. Five different stope excavation length scenarios were proposed and performed to study the effect of excavation length on the stope stability at a Canadian hard rock mine. With different increasing phases during displacement initiation and rockburst development, all five excavation scenarios achieved almost the same final results in rockburst tendency and displacement at the analyzed location on the stope sidewalls. Compared with the other four excavation scenarios, scenario SCN#1 is more effective and efficient in numerical simulation analysis, especially for the numerical simulation analysis of the full-size underground mine. INTRODUCTION As underground mining works progress into deeper and more complex geological environments, they are experiencing more stress-induced rock damage initiation problems, which have seriously impeded mining efficiency and effectiveness (Kaiser et al., 2000). To better understand underground mining stope convergence and deal with the rock mass damages during the excavation, many researchers are actively addressing these issues. Barla (Barla et al., 2010; Barla et al., 2012; Barla & Pelizza, 2000) proposed several approaches for stope design by assessing the interaction between rock mass and structures in rock mass with time-dependent squeezing behavior. Janoszek (Janoszek, 2020) proposed two indexes to predict natural hazards in longwall working with analysis of coal and roof properties, interaction in shield loading and roof-floor by numerical modelling based on the Mohr-Coulomb criterion. Rock mass squeezing phenomenon around the stopes are widely investigated by considering the different rates of advancing (Ghaboussi & Gioda, 1977), analyzing the extension of microcrack length and development of excavation damage zone (Golshani et al., 2007; Tang et al., 2018), time-dependent deformation, and elastoplastic behavior (Malan, 2002), in the means of semi-empirical back analysis approach (Manh et al., 2015), analytical solution (Sulem et al., 1987), and numerical modelling (Ghaboussi & Gioda, 1977; Golshani et al., 2007; Manh et al., 2015; Wang & Huang, 2011; Weng et al., 2010; Xu & Apel, 2020). Gioda (Gioda & Cividini, 1996) discussed the linear and non-linear viscous constitutive laws and developed numerical methods to analyze the time-dependent effect on performance in squeezing rocks. The interaction mechanism between the stope excavation and stope stability was studied by analyzing the supports, physical model tests, underground research laboratory measurement, and numerical simulation (e Sousa et al., 2012; Funatsu et al., 2008; Martino & Chandler, 2004; Vazquez-Silva et al., 2020; Xu, 2021; Yuan & Yang, 2021; Zhang et al., 2019).

ABSTRACT Because of the difficulties in producing energy supply, fracturing-flooding has been applied in water driven low-permeability oil reservoirs, in which water is injected near the breakdown pressure to produce a large number of microfractures and increase the sweeping area. In this work, the fracturing-flooding experiments and Brazilian split tests were carried out using sandstone outcrops to study the influence of injection rate on the damage characteristics. The results show that: (1) There are two key pressures during the generation of fracturing-flooding fractures: fracturing initiation pressure (FIP) and formation breakdown pressure (FBP). The fracturing-flooding injection pressure should be designed between FIP and FBP. (2) The loading rate is controlled by the injection rate. When the injection rate increases from 4 mL/min to 12 mL/min, the FIP increases by only 3%, meaning that the injection rate or loading rate has little effect on FIP. (3) With the increase of injection rate and loading rate, the tensile strength and FBP first decrease and then increase, and the critical loading rate is 0.09 MPa/s. In order to obtain a wider injection pressure range, the injection rate should be far away from the critical rate. This study is of significance for optimizing fracturing-flooding. INTRODUCTION Fracturing-flooding is a technology that injects water near breakdown pressure to produce a large number of microfractures and increase the swept area. Because of the difficulties of producing energy supplements in water drive low-permeability reservoirs, China's Daqing Oilfield, Shengli Oilfield, Jiangsu Oilfield, and other oilfields have successively carried out fracturing-flooding field experiments and achieved ideal results (He and Wang, 2018; Yang, 2020; Gao, 2022; Wang, 2022; Huang et al., 2022). However, as a new technology, fracturing-flooding still has problems, such as an unclear understanding of the mechanism of increasing injection and the law of pressure transmission (Huang et al., 2022; Guo et al., 2022), which need to be solved urgently. Macro-fractures are generated by the connection of micro-fractures, meaning there should be a fracturing initiation point (FIP) before the formation breakdown point (FBP). For the triaxial compression test, FIP is the point where the axial strain curve of rock changes from a linear to nonlinear, which characterizes that the rock changes from linear elastic deformation stage to a stable crack extension stage (Liang et al., 2012; Peng et al., 2015; Guo et al., 2019; Zhang et al.,2020). For the FIP of tensile failure, Xu and Zhao (2008) and Chen Lei et al. (2021) monitored the crack initiation process with a three-point bending test and found a stable extension stage before the breakdown. By analyzing the field injectivity tests curve, Feng and Gray (2017) found that the FIP occurs when the pressure-time plot deviates from the linear relationship. With hydraulic fracturing laboratory experiments, Wu et al. (2020) pointed out FIP can be determined with the deviation point in the pressurization rate curve. With the increase in injection rate, the difference between FIP and FBP becomes larger. Aliabadian et al. (2019) and Shapeng et al. (2021) monitored the crack evolution process of the Brazilian splitting experiment by Digital Image Correlation (DIC) and acoustic emission (AE) and also confirmed that there is FIP in tensile failure.

ABSTRACT Accurate identification and extraction of fractures in core scanning electron microscope (SEM) images are particularly important for understanding and evaluating fractures in rocks. At present, most of the fracture extraction methods do not take into account the characteristics of fractures extension trend and edge change, resulting in many noise points and breakpoints in the extracted fractures. In view of the above problems, this paper proposes a new method of core SEM image fracture extraction based on double-side inspection and morphological completion. Double-side inspection fractures extraction grids the image and judges the micro-element containing fracture elements. There are many discontinuities in the preliminary extracted fracture image. The study presents a fractures completion algorithm based on the combination of long-short-term memory neural network (LSTM) and Similarity-matching to achieve rapid completion of the fracture discontinuities. Compared with threshold segmentation, the recall rate and precision rate of the proposed method are greatly improved. This method is the application of digital image processing and machine learning algorithm in rock fracture image processing and has important application value in rock fracture quantitative characterization and digital core establishment. INTRODUCTION Among the proven oil and gas reserves in the world, the reserves of fractured oil and gas reservoirs account for more than 60%. At present, there are many research methods for fractures reservoirs. It is an important direction of current research to extract fractures through core image recognition for analysis. The SEM scan can achieve continuous non-destructive imaging of the core, so the current core fracture extraction is mainly based on the SEM core scan image to extract fractures through image segmentation technology (Zou et al, 2016; Ma et al, 2014). At present, fracture segmentation is mainly based on threshold segmentation to binarize the core image, where the fracture area and non-fracture area show two different colors (Wang et al, 2022; Purswani et al, 2020; Zhang et al, 2017). The level set segmentation algorithm can obtain closed contour curves, so this method is also a common algorithm in image segmentation. Through this algorithm, the approximate contour curves of fractures can be obtained from core SEM images. In view of the shortcomings of the level-set algorithm, many scholars have improved it. The improvement is mainly to improve the accuracy of the segmented contour by combining other algorithms with the algorithm (Yang et al, 2012; Xu and Peng, 2012). The improvement of the image binarization algorithm can also effectively extract fractures. Pixels are divided based on the maximum inter-class variance method so that the maximum variance of different categories can effectively reduce the noise points in the image after fractures extraction (Wang, 2006). Through the Beamlet transform, the core image after threshold segmentation can effectively extract the linear features of fractures from the two-dimensional image, thus improving the accuracy of fractures extraction (Gao and Wang, 2010). With the rapid development of artificial intelligence technology, the accuracy of core image segmentation can also be improved with the help of a machine learning algorithm. Effective image segmentation can also be achieved based on BP neural network, but the convergence speed of the BP neural network is slow, so some scholars have made a series of improvements to the BP neural network through genetic algorithm, fuzzy set theory, and other methods (Zhou, 2014; Xiong et al,1999; Yang et al, 2007). The binarization of an image can also be understood as the clustering of image pixels. The pixels in the image are grouped into two categories according to their characteristics. The clustering algorithms such as support vector machine (SVM) and centerless fuzzy clustering have also been used for the binary segmentation of images (Wei et al, 2007; Ma et al, 2013). Combining clustering algorithms with threshold segmentation can effectively improve the extraction accuracy of natural fractures (Fan et al, 2022).