• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Evolution of gas kick and overflow in wellbore and formation pressure inversion method under the condition of failure in well shut-in during a blowout

    2022-06-02 05:00:24GuoShuaiJuTieYanXiaoFengSunJingYuQuQiaoBoHu
    Petroleum Science 2022年2期

    Guo-Shuai Ju,Tie Yan,Xiao-Feng Sun,Jing-Yu Qu,Qiao-Bo Hu

    College of Petroleum Engineering,Northeast Petroleum University,Daqing,163318,Heilongjiang,People's Republic of China

    Keywords:Gas kick Formation pressure Multiphase flow Computational model Long short-term memory

    ABSTRACT With ongoing development of oil exploration and techniques,there is a significant need for improved well control strategies and formation pressure prediction methods.In this paper,a gas-liquid transient drift flow model was established according to the gas-liquid two-phase flow characteristics during the gas kick.A Roe scheme was used for numerical calculation based on the finite volume method.The changes of bottom-hole pressure,casing pressure,the development law of cross-sectional gas holdup,and gas velocity,along with the vertical well depth,were analyzed through simulation examples.The time-series characteristics of mud pit gain were obtained by adjusting the formation parameter.The complex nonlinear mapping relationship between the formation parameters and the mud pit gain was established.The long short-term memory network (LSTM) of deep learning was used to obtain a formation pressure inversion when the blowout is out of control and the well cannot be shut-in.Experimental data from a well were used to verify the gas-liquid two-phase transient drift flow model based on the finite volume method,demonstrating that this method is reliable,with greatly improved prediction accuracy.This approach provides theoretical support for the early monitoring of gas kick during drilling,and for well-killing design and construction after uncontrolled blowout.

    1.Introduction

    With the expansion of oil and gas exploration and increased construction of complex wells in deep,deepwater/ultra-deepwater,high-temperature,and high-pressure areas,there are increased requirements for safety and risk management for well drilling and well control(Bhandari et al.,2015;Sun et al.,2018;Xu et al.,2020).Therefore,it is important to study the development law of gasliquid two-phase transient flow characteristics in the wellbore during the gas kick and to predict the formation pressure when blowout is out of control and the well cannot be shut-in.This information can help ensure the safety of drilling,improve drilling efficiency,reduce drilling cost,and allow the restoration of control with improved well-killing design and construction in response to blowout.

    The coupling process of gas-liquid two-phase flow plays an essential role in the process of gas kick.The gas-liquid two-phase flow in the wellbore has typically been studied using the drift flow model,which is composed of continuity equations of each phase and a mixing momentum equation.This model directly generates a set of equations to study the mathematical structure of the twophase flow,and has advantages of simplicity and transparency(Kulia et al.,2015,2016;Zeidan and Sekhar,2018;Shen,2020).Ishii and Hibiki(2011)studied the motion and constitutive equations of drift velocity under different two-phase flow patterns.Considering macroscopic factors such as the geometric parameters of the interface,the volume force field,the shear stress,and the momentum transfer at the interface,they established a constitutive equation describing the relative motion between phases in the drift flow model.Evaluating the drift flow model's practical significance for analysis of two-phase flow,Hibiki and Ishii (2003) determined the two-phase flow pattern distribution parameters and drift constitutive velocity equation in microgravity.Fjelde et al.(2016)established a transient calculation model for the numerical simulation of two-phase flow,effectively reducing numerical dissipation and discretization errors based on the drift flow model and the AUSMV scheme.Wang et al.(2016) proposed a new algorithm to solve the drift flow model by referring to the SIMPLE algorithm and the calculation results are in good agreement with those calculated by the Roe method.Ma et al.(2016)established a multiphase flow model combined with a well control hydraulic model of pressurecontrolled drilling based on the drift flow model.This model retained transient multiphase flow characteristics of fluid and gas in the wellbore and adopted an appropriate closure relationship.Wei et al.(2018) used the MUSCL solver framework to establish a gas-liquid two-phase transient flow model based on the drift flow model,described the gas-liquid phase relationship with Shi's relationship,and solved it through the second-order AUSMV numerical scheme.

    For uncontrolled blowout caused by a severe gas kick,the conventional method of determining formation pressure by reading the casing pressure and standpipe pressure after shut-in the well will not work.Formation pressure is an essential key parameter in a well-killing design after a blowout is out of control(Anderson et al.,2011;Li et al.,2016;Meister et al.,2003).The interactive interpretation method can predict formation pressure in combination with the two-phase flow calculation model,continuously adjusting bottom hole parameters during forward modeling to obtain prediction results with high consistency with measured wellhead parameters.However,this method is a trial and error method based on manual optimization,requiring a large amount of calculation and with limited processing accuracy(Wang et al.,2021).In recent years,deep learning has attracted extensive attention for modeling.As a kind of recurrent neural network with memory ability,long short-term memory (LSTM) can intelligently process the timeseries evolution characteristics of data (Kratzert et al.,2018;Yildirim et al.,2019;Zhao et al.,2017).In this study,time-series evolution characteristics of wellhead mud pit gain were determined by adjusting the bottom hole parameters and using a transient calculation model.The corresponding training set was then established and input into the LSTM network.Measured time series evolution data of mud pit gain were then used with the trained LSTM network to realize the inversion and prediction of formation pressure.

    The finite difference method has been widely used to solve the drift flow model.This method has low conservation,relies on high computational cost to satisfy the conservation relationship,and is accessible to divergence in the iterative calculation.There is no attempt to retrieve formation pressure in case of blowout out of control and unable to shut in the well.The earlier a gas kick is detected,the higher the possibility of successful well control.In this study,a gas-liquid two-phase transient drift flow model based on the finite volume method was established.The Roe scheme was used for numerical calculation to analyze gas kick and overflow development law in the wellbore.LSTM method based on deep learning was used for the inversion of formation pressure when blowout is out of control.This work can provide theoretical support for the early monitoring of gas kick during drilling and for wellkilling design and implementation after a blowout.

    2.Mathematical model of gas-liquid two-phase transient flow

    The gas-liquid two-phase transient flow in the wellbore after gas kick can be calculated as follows:(1) According to the continuity equation of gas phase and liquid phase and the mixture momentum equation of gas-liquid phase,a gas-liquid two-phase transient drift flow model can be established in matrix form.(2)Jacobian matrix transformation is then carried out for the equations.According to the chain rule,the sub-elements of the Jacobian matrix A can be derived by constructing intermediate functions.(3)The approximate linearization matrixof matrix A is obtained,and the complex nonlinear equations can be transformed into simpler linear equations.(4) The Roe scheme is then used to find the relationship between matrix A and.(5) The Roe flux difference scheme is used to solve the conserved variables,and these variables can then be updated on the time layer.

    2.1.Governing equation of gas-liquid two-phase transient flow

    The governing equations of gas-liquid two-phase transient flow in the wellbore include the continuity equations and the momentum equation.Continuity equations of gas phase and liquid phase are:

    where αgand αlare the volume fraction of gas phase and liquid phase,respectively;ρgand ρlare the density of gas phase and liquid phase,respectively;vgand vlare the velocity of gas phase and liquid phase,respectively;p is the pressure;ρmis the mixing density of gas phase and liquid phase;Ffricis the friction term;g is the acceleration of gravity;θ is the inclination angle.

    Because the interaction between the gas and liquid phase is ignored,it is necessary to introduce the gas-liquid drift relation Ffricto close the drift flow model.Shi's gas-liquid drift relationship has been widely used in the drilling industry and verified by many actual data (Shi et al.,2005).

    The drift flow model equations of gas-liquid two-phase flow in vector form can then be obtained by simultaneous Eq.(1)-(3):

    These governing equations are a class of typical hyperbolic nonlinear partial differential equations.The use of these equations may generate problems such as weak discontinuity,discontinuous derivatives of the solution,a discontinuous solution,and the strong discontinuity of the function itself.Additionally,a numerical method is required to solve the problem.In this paper,we use the finite volume method to solve the nonlinear governing equations based on the Roe scheme (Roe,1981),including Jacobian matrix transformation and solving the approximate linearization matrix.The step-by-step calculations are detailed below.

    2.2.Transformation of the Jacobian matrix

    Without consideration of the source term S(W),Eq.(4) can be written as a homogeneous equation by applying the chain rule:

    The transformed Jacobian matrix A(W) is:

    Due to the strong nonlinearity of the drift flow control equation's coefficient matrix A(W),its eigenvalues and flow propagation direction cannot be determined accurately.Therefore,it is difficult to construct the upwind difference of the nonlinear vector equation.To address this,an intermediate function can be constructed and through chain rule,every submatrix of the Jacobian matrix can be obtained.Because the derivation process is lengthy,the derivation results of each sub-matrix are given directly here.

    Among them,ε1,ε2,and ε3are parameters without physical meaning,and are used only to simplify the above formula's expression.

    where C0is the gas distribution coefficient.

    Using these equations,the expression of the Jacobian matrix A(W) is obtained.

    2.3.Calculating the approximate linearization matrix of Jacobian matrix

    According to the gas-liquid two-phase transient drift flow model,the splitters of the linearized matrix can be calculated from the continuity and momentum equations.Similarly,the split form of flux term F(W) can be obtained.

    where L and R are the left and right sides of the flux.

    Next,the Roe average values of the continuity equation,momentum equation,pressure term,void fraction,liquid fraction,and interphase slip relation can be calculated.Since there are many subelements in matrix splitting design,resulting in a cumbersome calculation process,the complex expression of splitting sub-terms is not presented here.Using the above steps,the approximate linearization matrix of the original Jacobian matrix can be obtained.

    The relationship between gas density,pressure,and temperature is used as the auxiliary equation of the model.The auxiliary equation has three parts:the density relationship of compressible gas under different temperature and pressure conditions(ideal gas equation of state),the relationship between pressure and viscosity of gas-phase(Dean-Stiel viscosity model(Tan et al.,2017)),and the general flow pattern discrimination relationship of gas-liquid twophase (including dispersed bubble flow,bubble flow,slug flow,stirred flow,annular fog flow,etc.).

    2.4.Boundary conditions and the Roe algorithm to solve equations

    The grid division of the wellbore is shown in Fig.1,with the bottom and wellhead set as the boundary conditions of the flow region.To simulate gas kick,the parameters of the bottom hole and wellhead will change with the change of gas-liquid two-phase coupling relationship in the wellbore,correlating to a change of boundary conditions.During drilling,when gas invasion occurs in the wellbore or if the wellhead back pressure is adjusted,the boundary conditions change dynamically,and the changes of these boundary conditions alter the flow parameters in the whole wellbore grid space.The boundary conditions can be obtained from the characteristic lines and compatibility equations.

    The annular flow field inlet is set as the pressure-inlet boundary condition.The drilling fluid flows along the bottom hole inlet direction,and the inlet velocity is evenly distributed.The annulus outlet is set as the boundary condition of the pressure-outlet,with unidirectional flow back along the annulus.

    According to the Roe flux difference scheme,the transient drift flow equations can be solved as:

    Through the Jacobian matrix and its approximate linearization matrix obtained by the previous calculation,the conserved variables can then be calculated by the Roe iterative scheme according to Eq.(25).

    The calculation scheme is as follows (see Fig.2):

    (1) Input basic parameters,including well trajectory,wellbore structure,formation parameters,and physical parameters of drilling fluid and natural gas.

    (2) Discretize the wellbore flow region and divide into onedimensional grids to determine the total simulation time,time step,and convergence conditions.

    (3) Starting with each node parameter of the well before gas kick at initial values and using the bottom of the well at any time as the starting point (the first node as the boundary value),the Roe scheme based on the finite volume method can be applied to get the 2nd,3rd,and nth recursively.

    Fig.2.Flow chart to solve the gas-liquid two-phase transient drift flow model.

    (4) The parameter values of each space node in the time layer are calculated step by step to obtain the velocity field and pressure field distribution of the whole wellbore space.All parameters of the time layer node are then used as the known data of the next layer,and the cycle continues until the preset total simulation time cycle is reached.

    3.Formation pressure inversion method based on LSTM

    Long short-term memory network (LSTM) is an improved algorithm based on recurrent neural networks (RNN).LSTM has the advantages of RNN for the processing of time-series data and overcomes the limitations of long-term dependence and easy gradient disappearance.LSTM retains the chain form of RNN and is composed of a series of recursively connected subnetworks of memory blocks.The LSTM fundamental structure consists of three gates in the interaction layer,i.e.,input gate,output gate,and forget gate (Greff et al.,2016).The cooperation of the three gates can effectively control the information coming into the model.The network and structure of LSTM are shown in Fig.3.

    Fig.1.The grid division of the wellbore.

    The time series of wellhead mud pit gain of the input layer is x=(x1,x2,…,xt),the state of the hidden layer memory unit h=(h1,h2,…,ht)and output layer sequence y=(y1,y2,…,yt)can be calculated as follows:

    The memory unit memorizes the historical information of the sequence data together with the hidden state.The information in the memory unit is controlled by three gating units.The forget gate is:

    The input gate adds new information to the memory unit according to ht-1and xt:

    The output gate determines htaccording to ht-1,xt,and ct:

    where xtis the input at t time;ytis the output at t time;htis the hidden state at t time;Wxh,Whh,and Whyare the weights of input,hidden,and output,respectively;bhand byare the offset of hidden state and output,respectively;f(·) and g(·) are the activation functions of hidden layer and output layer,respectively;ht-1is the output of the last single state;Wxf,Whf,and Wcfare the corresponding weights of forgetting gate,respectively;bfis the offset of forget gate;σ is the sigmoid function;Wxi,Whi,and Wciare the corresponding weights of input gate;biis the offset of input gate;tanh is the hyperbolic tangent activation function;Wxcand Whcare the corresponding weights of memory units;bcis the bias term of memory unit weights;Wxo,Who,and Wcoare the corresponding weights of output gate;bois the bias of output gate;ctis the state value of cell structure at t time.

    Fig.3.Structure of LSTM memory unit.

    Fig.4.Photos of full-scale experimental well.

    By changing the bottom hole parameters,including formation pressure and gas-liquid physical parameters,the model can be trained in real-time to meet the parameters of specific wells.The established gas-liquid two-phase transient calculation model can be used to obtain the time series data set of mud pit gain change to explore gas invasion and overflow development law under different formation parameters.These data are input into the LSTM neural network to learn the sequential operation law of mud pit gain fluctuation caused by a gas kick.LSTM training uses the mean relative error (MRE) and mean square error (MSE) as evaluation indexes.With a large amount of data in the time-series data set,the unique gating mechanism of LSTM and the use of a memory unit enable learning all historical information up to the current time and then allow modeling of this long-distance dependency when dealing with such a long input sequence.When training LSTM,the forward propagation is calculated using Eqs.(28)-(32),and the coupling result of the current memory unit is calculated using Eq.(33),combining the forget and input units.According to the error equation for gradient backpropagation training,the Adam algorithm can then be used as an adaptive momentum estimation algorithm for optimization training.

    4.Case calculation

    We tested this approach using a full-scale experimental well in the well control training center of the Daqing drilling engineering company (Fig.4).The gas-liquid two-phase transient drift flow model described above was used to predict the gas-liquid twophase development law in the wellbore after gas kick and thencalculate the gas holdup in the annulus at different times and the distribution of gas-phase velocity with well depth.

    The test well has complete wellhead equipment(including bell mouth and BOP group),experimental annulus and gas storage annulus,a 35 MPa full set of well control equipment,an energy storage air compressor,a simulated gas injection device,a measurement and control system,and a monitoring system.The specific parameters are as follows:vertical depth of 2000 m,continuous gas injection simulation realized at 2000 m borehole depth,hole diameter of 215.9 mm,and drill pipe outer diameter of 127 mm.The BHA is the experimental wellbore that consists of a bit,drill collar,drill pipe,variable thread joint,back pressure valve,annular pressure measuring nipple,and other components.Air is injected into the gas storage well through the air compressor until the pressure rises to the specified value.Drilling fluid enters the annulus through the surface manifold,drilling tools,and other equipment.The gas is injected into the annulus through a parasitic pipeline from the gas storage well.The parameters of the experimental well were used to generate the basic parameters of the simulation model,with consistent well depth,borehole diameter,and drill pipe diameter.The simulation model included 1000 grids,and the gas kick duration was set to 100 s(100-200 s after the start of the experiment).The total simulation time is 1000 s and the time step is 0.005 s.Other experimental parameters are listed in Table 1.

    Table 1Well experimental parameters.

    Table 2Evaluation indexes of LSTM model prediction results.

    The transient simulation analysis of gas-liquid two-phase flow was carried out using the gas-liquid two-phase transient drift flow model described in this work.And the specific results are detailed below.Fig.5 shows the variation of bottom hole pressure with time within 110 s before the experiment (10 s after gas kick) and the distribution of cross-sectional gas holdup and gas velocity with well depth at t=110 s.When the bottom hole gas first intrudes into the wellbore,the velocity of the liquid column in the wellbore increases because the bottom hole gas increases the annular circulation friction resistance.At the same time,because the invaded volume occupies part of the liquid column volume,the hydrostatic fluid column pressure decreases.However,the gas does not expand significantly due to the small air intake.The hydrostatic fluid column pressure is less,and the increase of annular circulating friction is more significant than the loss of hydrostatic fluid column pressure,so the bottom hole pressure increases.There is some free gas at the bottom of the well,and the cross-section gas holdup is no longer zero.

    Fig.6 shows the change of bottom hole pressure within the first 210 s of the experiment (10 s after the end of gas kick) and the distribution of cross-sectional gas holdup and gas velocity with well depth at t=210 s.At t=210 s,all the gas has been completely injected into the wellbore.With increased air intake,the bottom hole pressure decreases.The larger the bottom hole pressure,the smaller the volume occupied by gas and the smaller the pressure loss of the hydrostatic column,resulting in a minor reduction of the bottom hole pressure.With the constant gas intrusion rate,the pressure fluctuates widely at critical points during the initial and completion stages of the intake,but quickly returns to normal.

    Fig.7 shows the variation of bottom hole pressure within the first 680 s of the experiment and the distribution of gas holdup and gas velocity with well depth at t=680 s.When the gas column migrates to the middle of the wellbore,as the gas column moves upward along the wellbore,the wellbore pressure becomes smaller,and the gas expands continuously.The maximum cross-sectional gas holdup increases from 0.2 at the bottom of the well to 0.3.The gas velocity increases with the decrease of the well depth,but the velocity change is not apparent.The gas expansion increases the velocity of the liquid column which increases the circulating friction pressure drop,but the gas expansion also reduces the hydrostatic pressure.The hydrostatic fluid column pressure loss is dominant,so the bottom hole pressure continues to decrease,but the decrease rate is relatively slow.

    Fig.5.Development law of gas kick during the first 110 s (10 s after gas kick).

    Fig.6.Development law of gas kick during the first 210 s (10 s after the end of gas kick).

    Fig.8 shows the variation of bottom hole pressure within the first 980 s before the experiment and the distribution of gas holdup and gas velocity with well depth at t=980 s.When the gas column moves close to the wellhead,the gas in the annulus expands rapidly.The closer the wellhead is,the higher the gas holdup.The wellhead is almost full of gas,and the upper liquid column is nearly emptied,resulting in a sharp decrease in hydrostatic fluid column pressure.Although the gas velocity increases sharply,the bottom hole pressure decreases sharply because the gas density is far less than the drilling fluid density.The increase in annular friction loss is negligible compared with the hydrostatic fluid column pressure.The variation of bottom hole pressure and casing pressure as measured are shown in Fig.8,where the dotted lines show the measured pressure fluctuation curves.We found that the gas-liquid two-phase transient calculation model can better predict the development law of bottom hole pressure and casing pressure compared with the model,with average relative errors of prediction of 11.58% and 8.41%,respectively.The analysis error may be caused by the difference between the discrimination formula of the gas-liquid two-phase flow pattern and the actual situation as well as by inconsistency between the drill collar and drill pipe.

    The gas-liquid two-phase transient calculation model can effectively simulate the fluid changes in the wellbore,allowing prediction of the gas-liquid two-phase transient flow development and the gas holdup and gas velocity distribution law at different well depths.However,it is difficult to predict the formation pressure when blowout is out of control due to a severe gas kick.The use of the LSTM method with deep learning can determine the nonlinear mapping relationship between the mud pit gain and formation pressure.Studying the time series characteristics and evolution mechanism of mud pit gain fluctuation caused by the gas kick and using measured early wellhead mud pit gain fluctuation data allows determination of inversion and prediction of formation pressure.

    Fig.7.Development law of gas kick overflow during the first 680 s.

    Fig.8.Development law of gas kick overflow in 980 s (comparison between experimental (dotted lines) and predicted (solid lines) values).

    Fig.9.Comparison of measured and LSTM predicted values of time series evolution of mud pit gain under four different formation pressures:(a) 20.2 MPa;(b) 20.5 MPa;(c)21.0 MPa;(d) 21.4 MPa.

    The fluctuation time-series data set of mud pit gain is calculated by adjusting formation parameters according to the established gas-liquid two-phase transient drift flow model to meet the specific LSTM model training needs.Here,the sampling interval was 0.5 s,and 1045 training sets of time series evolution samples were obtained.These data were divided into the training set and test set at a ratio of 8:2,and then the data training set were further divided into the training set and verification set at a ratio of 8:2 for crosstraining.The data set was then normalized and preprocessed to enhance robustness and generalization.The LSTM algorithm establishes the inversion prediction model by combining the entire connection layer and nonlinear activation function for intelligent training.This was done as follows.Using double-layer LSTM and the entire connection layer in series,the neuron parameters of the hidden layer were 256,512,and 1000,the learning rate range was 0.005-0.00001,the batch size was 128,and the number of epochs was 4000.As the number of iterations increased,the error of training samples decreased.After 2750 iterations,the model converged,and the final model was obtained.The prediction accuracy of the model was 87.3%.The time series evolution data sets of four groups of mud pit gain data under different formation pressures were simulated using this model and also obtained through full-scale experiments,as shown in Fig.9.The inversion values of different formation pressures and the model's prediction indexes are listed in Table 2.

    It can be seen from Fig.9 that the prediction results of the LSTM model at the local position deviate significantly from the measured values.For example,there is significant prediction error of gas invasion overflow in the initial stage.Still,the overall prediction results are similar,which can better reduce the fluctuation of mud pit gain caused by the gas kick.As shown in Table 2,the inversion accuracy of formation pressure is high and the mean relative error in the testing process is about 11.5%,indicating that the LSTM model can be used for effective inversion and prediction of formation pressure.

    The inversion methods of formation pressure when blowout is out of control can be summarized as follows:(1)the gas-liquid twophase transient calculation model based on finite volume method is first used to calculate the time-series variation characteristics of mud pit gain under field conditions;(2) the training set is established and input into the algorithm to train the LSTM neural network;(3) the time series characteristics of mud pit gain measured in the early stage of gas invasion are substituted into the training set to invert the formation pressure.

    5.Conclusions

    In this paper,we established a drift flow model of gas-liquid two-phase flow in the wellbore.In this approach,the governing equations can be solved using the finite volume method and the Roe scheme.The corresponding calculation conditions are then used to obtain the transient development law of gas-liquid twophase flow in the wellbore after gas kick.

    When gas intrudes into the wellbore,the annular circulation friction resistance increases,and the bottom hole pressure increases.When the gas column moves to the middle of the wellbore,the gas expands continuously.The circulation friction resistance increases,and the hydrostatic fluid column pressure decreases,so the bottom hole pressure decreases continuously.When the gas column moves close to the wellhead,the gas expands rapidly and the hydrostatic fluid column pressure decreases sharply,so the bottom hole pressure decreases sharply.

    Based on the deep learning algorithm framework,the timeseries variation characteristics of mud pit gain were obtained by the adjusting bottom hole parameters according to the transient calculation model.The corresponding training set was then established and input into the algorithm to train the LSTM neural network,extract the time relationship between formation pressure and mud pit gain,describe the evolution characteristics of this relationship,and establish an accurate inversion prediction model.The measured time series evolution data of mud pit gain were entered into the trained LSTM network to realize formation pressure inversion when the blowout is out of control,and the well cannot be shut-in.

    Experimental data were collected from the well control training center of a Daqing Drilling Engineering Company and used to confirm the reliability of prediction using the gas-liquid two-phase transient drift flow model.The formation pressure inversion and prediction results based on the LSTM algorithm exhibited high accuracy,providing theoretical support for the early monitoring of gas kick and overflow and prediction of formation pressure after uncontrolled blowout.

    Acknowledgements

    This work was financially supported by the National Natural Science Foundation of China (Grant No.51974090,51474073).

    激情在线观看视频在线高清| 国产精品一区二区免费欧美| 久久性视频一级片| 成年女人永久免费观看视频| 午夜精品一区二区三区免费看| 欧美极品一区二区三区四区| 午夜精品久久久久久毛片777| 麻豆av在线久日| 极品教师在线免费播放| 日韩 欧美 亚洲 中文字幕| 亚洲中文字幕日韩| 女人被狂操c到高潮| 18美女黄网站色大片免费观看| 午夜免费观看网址| 亚洲精品一区av在线观看| 午夜日韩欧美国产| 欧美大码av| 欧美3d第一页| 精品一区二区三区四区五区乱码| 久久性视频一级片| 中亚洲国语对白在线视频| 视频区欧美日本亚洲| 国产精品久久久人人做人人爽| 日本五十路高清| 亚洲欧美精品综合一区二区三区| 一级毛片精品| 久久人妻av系列| 日本在线视频免费播放| 老司机午夜十八禁免费视频| 午夜福利视频1000在线观看| 成人午夜高清在线视频| 18禁国产床啪视频网站| 成人国产一区最新在线观看| 国产视频一区二区在线看| 搡老熟女国产l中国老女人| 亚洲天堂国产精品一区在线| 99久久综合精品五月天人人| 黄色片一级片一级黄色片| 十八禁人妻一区二区| 国产精品久久久久久人妻精品电影| 91av网一区二区| 国内少妇人妻偷人精品xxx网站 | 人妻丰满熟妇av一区二区三区| 夜夜夜夜夜久久久久| 精品欧美国产一区二区三| 啪啪无遮挡十八禁网站| 叶爱在线成人免费视频播放| 最新在线观看一区二区三区| 亚洲熟妇中文字幕五十中出| 亚洲国产精品999在线| 两个人的视频大全免费| 小说图片视频综合网站| 国产精品亚洲美女久久久| 国产探花在线观看一区二区| 国产av一区在线观看免费| 久久久久国产一级毛片高清牌| 一进一出抽搐动态| 精品一区二区三区视频在线 | 久久精品国产亚洲av香蕉五月| 最好的美女福利视频网| av中文乱码字幕在线| 免费av不卡在线播放| 欧美高清成人免费视频www| 国产成人啪精品午夜网站| 国产亚洲欧美98| 久久久久性生活片| 在线观看免费午夜福利视频| 后天国语完整版免费观看| 午夜免费激情av| 丝袜人妻中文字幕| 久久亚洲真实| 亚洲熟妇熟女久久| 在线观看一区二区三区| 午夜免费成人在线视频| 神马国产精品三级电影在线观看| 99视频精品全部免费 在线 | 深夜精品福利| 精品国产三级普通话版| 日韩欧美 国产精品| 每晚都被弄得嗷嗷叫到高潮| 麻豆久久精品国产亚洲av| 男人和女人高潮做爰伦理| 国产精品av视频在线免费观看| 亚洲 国产 在线| 国产高潮美女av| 国内精品美女久久久久久| 国产视频一区二区在线看| 国产亚洲精品久久久久久毛片| 岛国在线观看网站| 国产伦一二天堂av在线观看| 可以在线观看毛片的网站| 日本一本二区三区精品| 两个人看的免费小视频| 男女那种视频在线观看| 精品久久久久久久久久久久久| 蜜桃久久精品国产亚洲av| 亚洲va日本ⅴa欧美va伊人久久| 欧美另类亚洲清纯唯美| 午夜福利成人在线免费观看| 老司机午夜福利在线观看视频| 最新在线观看一区二区三区| 免费看日本二区| 99久国产av精品| 国产成人影院久久av| 久久久久国内视频| 国产精品久久视频播放| 亚洲欧美一区二区三区黑人| 嫩草影院精品99| 琪琪午夜伦伦电影理论片6080| 欧美黑人欧美精品刺激| 国产高清视频在线观看网站| 老熟妇仑乱视频hdxx| 久久久久久九九精品二区国产| 两个人的视频大全免费| 999久久久国产精品视频| 欧美性猛交╳xxx乱大交人| 18禁国产床啪视频网站| 午夜a级毛片| 久久久久久九九精品二区国产| 又黄又爽又免费观看的视频| 超碰成人久久| 国产精品乱码一区二三区的特点| 757午夜福利合集在线观看| 国产精品一区二区三区四区免费观看 | 久久久久久久久免费视频了| 欧美成人免费av一区二区三区| 亚洲av电影不卡..在线观看| 精品久久久久久成人av| 亚洲精品乱码久久久v下载方式 | 国产高清有码在线观看视频| 亚洲美女视频黄频| 久久精品夜夜夜夜夜久久蜜豆| 草草在线视频免费看| 热99在线观看视频| 国产主播在线观看一区二区| 久久久久亚洲av毛片大全| 巨乳人妻的诱惑在线观看| 黄色成人免费大全| netflix在线观看网站| 在线十欧美十亚洲十日本专区| 日本熟妇午夜| 色老头精品视频在线观看| 欧美精品啪啪一区二区三区| 亚洲一区二区三区不卡视频| 黑人欧美特级aaaaaa片| 制服人妻中文乱码| 一本精品99久久精品77| 亚洲人成网站高清观看| 中文字幕高清在线视频| 亚洲无线观看免费| 国产亚洲精品av在线| 中文字幕高清在线视频| 淫妇啪啪啪对白视频| 亚洲欧美日韩高清在线视频| 亚洲 欧美一区二区三区| 国产精品电影一区二区三区| 成人精品一区二区免费| 日韩欧美在线乱码| 韩国av一区二区三区四区| 亚洲专区字幕在线| 亚洲欧美日韩东京热| 99国产精品一区二区三区| 亚洲最大成人中文| 成年版毛片免费区| 曰老女人黄片| av天堂中文字幕网| 全区人妻精品视频| 国产亚洲精品久久久久久毛片| 中文资源天堂在线| 成人精品一区二区免费| 国产单亲对白刺激| 舔av片在线| 亚洲无线在线观看| 国产精品九九99| 免费看十八禁软件| 午夜精品久久久久久毛片777| 母亲3免费完整高清在线观看| 叶爱在线成人免费视频播放| 日韩三级视频一区二区三区| 一夜夜www| 欧美xxxx黑人xx丫x性爽| 又爽又黄无遮挡网站| 午夜精品一区二区三区免费看| 国内精品一区二区在线观看| 在线观看66精品国产| 亚洲电影在线观看av| 亚洲中文字幕一区二区三区有码在线看 | 真人做人爱边吃奶动态| 夜夜躁狠狠躁天天躁| 看片在线看免费视频| 国产激情偷乱视频一区二区| 免费一级毛片在线播放高清视频| 好看av亚洲va欧美ⅴa在| 国产视频一区二区在线看| 免费看a级黄色片| 99在线人妻在线中文字幕| 露出奶头的视频| 一夜夜www| 母亲3免费完整高清在线观看| 这个男人来自地球电影免费观看| 这个男人来自地球电影免费观看| 午夜激情欧美在线| 日本撒尿小便嘘嘘汇集6| 亚洲色图 男人天堂 中文字幕| 三级毛片av免费| 禁无遮挡网站| 狂野欧美激情性xxxx| 欧美国产日韩亚洲一区| 一级毛片精品| 91在线观看av| 亚洲成人免费电影在线观看| 午夜视频精品福利| 99国产极品粉嫩在线观看| 午夜影院日韩av| 久久这里只有精品中国| 亚洲精品色激情综合| 一个人免费在线观看的高清视频| 母亲3免费完整高清在线观看| 性色avwww在线观看| 十八禁人妻一区二区| 最近在线观看免费完整版| 首页视频小说图片口味搜索| 国产麻豆成人av免费视频| 精品午夜福利视频在线观看一区| 亚洲国产精品久久男人天堂| 午夜精品一区二区三区免费看| 久久精品国产综合久久久| 国产免费男女视频| 精品国产超薄肉色丝袜足j| 神马国产精品三级电影在线观看| 久久精品aⅴ一区二区三区四区| 国产成年人精品一区二区| 精品国产乱码久久久久久男人| 色精品久久人妻99蜜桃| 亚洲五月天丁香| 欧美日韩一级在线毛片| 亚洲人成伊人成综合网2020| 欧洲精品卡2卡3卡4卡5卡区| 黄色片一级片一级黄色片| 精品99又大又爽又粗少妇毛片 | 精品国产美女av久久久久小说| 无遮挡黄片免费观看| 他把我摸到了高潮在线观看| 国产精品爽爽va在线观看网站| а√天堂www在线а√下载| 欧美成人一区二区免费高清观看 | 日韩欧美国产在线观看| 成人欧美大片| 日韩欧美在线乱码| 免费看光身美女| 免费无遮挡裸体视频| 国产野战对白在线观看| 亚洲精品粉嫩美女一区| 成人性生交大片免费视频hd| 亚洲天堂国产精品一区在线| 变态另类成人亚洲欧美熟女| 日韩欧美一区二区三区在线观看| 亚洲av第一区精品v没综合| 成人鲁丝片一二三区免费| 99国产精品99久久久久| 精品国产亚洲在线| 国产精品,欧美在线| 在线观看美女被高潮喷水网站 | 国产亚洲欧美98| 亚洲va日本ⅴa欧美va伊人久久| 午夜影院日韩av| 国产精品久久久久久精品电影| 久久精品综合一区二区三区| 国产成人影院久久av| 美女高潮的动态| 国产欧美日韩精品一区二区| 欧美激情久久久久久爽电影| 91字幕亚洲| 国产精品一区二区三区四区免费观看 | 日韩欧美一区二区三区在线观看| 久久精品综合一区二区三区| 亚洲七黄色美女视频| 麻豆国产av国片精品| 国产高清videossex| 国产午夜精品论理片| 一进一出抽搐gif免费好疼| avwww免费| 偷拍熟女少妇极品色| 禁无遮挡网站| 日韩欧美在线二视频| 亚洲无线观看免费| 久久久久久九九精品二区国产| 宅男免费午夜| 亚洲国产欧美一区二区综合| 两人在一起打扑克的视频| 日日夜夜操网爽| 国产亚洲av高清不卡| 在线观看一区二区三区| 国产精品九九99| 最近最新免费中文字幕在线| 国产97色在线日韩免费| 亚洲av中文字字幕乱码综合| 久久香蕉精品热| 亚洲精品一卡2卡三卡4卡5卡| 男女午夜视频在线观看| 88av欧美| 男人的好看免费观看在线视频| 亚洲avbb在线观看| 美女被艹到高潮喷水动态| 亚洲国产高清在线一区二区三| 精华霜和精华液先用哪个| 久久精品国产清高在天天线| 国产午夜福利久久久久久| 两人在一起打扑克的视频| 特级一级黄色大片| 亚洲激情在线av| 日本在线视频免费播放| 天堂影院成人在线观看| 欧美乱码精品一区二区三区| 12—13女人毛片做爰片一| 久久久久久久久久黄片| 99在线人妻在线中文字幕| 男插女下体视频免费在线播放| www.精华液| 欧美绝顶高潮抽搐喷水| 国产精品综合久久久久久久免费| 久久精品aⅴ一区二区三区四区| 18禁美女被吸乳视频| 国产成人av教育| 啦啦啦韩国在线观看视频| 欧美激情久久久久久爽电影| 久久久国产欧美日韩av| 琪琪午夜伦伦电影理论片6080| 亚洲欧美日韩高清专用| 老司机午夜福利在线观看视频| 99久久99久久久精品蜜桃| 午夜福利在线观看免费完整高清在 | 国内精品美女久久久久久| 国产蜜桃级精品一区二区三区| av视频在线观看入口| 狂野欧美白嫩少妇大欣赏| 99久久精品一区二区三区| 不卡一级毛片| 99热只有精品国产| 国产成人影院久久av| 日本在线视频免费播放| 成年版毛片免费区| 在线视频色国产色| 嫩草影院精品99| 黄色片一级片一级黄色片| 99久久精品一区二区三区| 一区二区三区激情视频| 一区福利在线观看| 99久久无色码亚洲精品果冻| 免费在线观看成人毛片| 中文字幕av在线有码专区| 欧美三级亚洲精品| 国产精品一区二区三区四区久久| 日本一二三区视频观看| 亚洲人与动物交配视频| 亚洲在线自拍视频| 久久久久国产精品人妻aⅴ院| 在线视频色国产色| 黄频高清免费视频| 亚洲一区高清亚洲精品| 在线观看舔阴道视频| 国产三级中文精品| 中文字幕人妻丝袜一区二区| 午夜福利在线观看吧| 又爽又黄无遮挡网站| 看黄色毛片网站| 国产午夜福利久久久久久| 久久久久久久久久黄片| 黄片大片在线免费观看| 午夜精品久久久久久毛片777| 精品久久久久久久久久免费视频| 亚洲欧美精品综合一区二区三区| 亚洲自拍偷在线| 听说在线观看完整版免费高清| 女人被狂操c到高潮| 日本一本二区三区精品| 免费在线观看影片大全网站| 欧美黑人欧美精品刺激| 麻豆av在线久日| 午夜成年电影在线免费观看| 免费av不卡在线播放| 日韩欧美一区二区三区在线观看| 脱女人内裤的视频| 91九色精品人成在线观看| 老熟妇仑乱视频hdxx| 国产真人三级小视频在线观看| 黄频高清免费视频| 久久精品国产99精品国产亚洲性色| 天天一区二区日本电影三级| av国产免费在线观看| 免费看美女性在线毛片视频| 又爽又黄无遮挡网站| 一级毛片女人18水好多| 午夜福利在线观看免费完整高清在 | 欧美日韩黄片免| 18禁黄网站禁片免费观看直播| 久久精品91无色码中文字幕| 午夜免费激情av| 天天躁狠狠躁夜夜躁狠狠躁| 欧美成人性av电影在线观看| 丰满人妻熟妇乱又伦精品不卡| 久久精品人妻少妇| cao死你这个sao货| 国产av麻豆久久久久久久| 18禁黄网站禁片免费观看直播| a在线观看视频网站| 在线观看舔阴道视频| 国产精品一及| 97超级碰碰碰精品色视频在线观看| 老司机在亚洲福利影院| 久久久水蜜桃国产精品网| 桃色一区二区三区在线观看| 精品福利观看| 国产精品爽爽va在线观看网站| 亚洲国产精品久久男人天堂| 日本免费一区二区三区高清不卡| 免费搜索国产男女视频| 中文字幕高清在线视频| 99久久精品热视频| 中文字幕精品亚洲无线码一区| 国产精品一区二区精品视频观看| 国产精品日韩av在线免费观看| 国产激情久久老熟女| 国产精品日韩av在线免费观看| 一二三四社区在线视频社区8| 亚洲一区高清亚洲精品| 亚洲 欧美 日韩 在线 免费| 叶爱在线成人免费视频播放| 伦理电影免费视频| 亚洲乱码一区二区免费版| 国产亚洲av嫩草精品影院| 日本熟妇午夜| 国产精品九九99| www.熟女人妻精品国产| 亚洲精品在线观看二区| 男插女下体视频免费在线播放| 超碰成人久久| 亚洲精品美女久久av网站| 日本三级黄在线观看| www.自偷自拍.com| 性色avwww在线观看| 成人永久免费在线观看视频| 老汉色av国产亚洲站长工具| 色综合欧美亚洲国产小说| 免费在线观看影片大全网站| 99国产精品一区二区蜜桃av| 国产精品野战在线观看| 久久婷婷人人爽人人干人人爱| 亚洲人成伊人成综合网2020| 亚洲精品在线美女| 亚洲av第一区精品v没综合| 亚洲 欧美一区二区三区| 日韩 欧美 亚洲 中文字幕| 可以在线观看的亚洲视频| 欧美日韩综合久久久久久 | 大型黄色视频在线免费观看| 国产精品久久视频播放| 黑人操中国人逼视频| АⅤ资源中文在线天堂| 精品久久久久久成人av| 久久久久久九九精品二区国产| a级毛片在线看网站| 91在线观看av| 亚洲熟妇熟女久久| svipshipincom国产片| 丁香欧美五月| 日韩有码中文字幕| 日韩中文字幕欧美一区二区| 午夜亚洲福利在线播放| 免费一级毛片在线播放高清视频| 婷婷六月久久综合丁香| 成年女人毛片免费观看观看9| 国产午夜福利久久久久久| 久久精品综合一区二区三区| 99热这里只有是精品50| 日韩 欧美 亚洲 中文字幕| 99热6这里只有精品| 人妻夜夜爽99麻豆av| 日韩高清综合在线| 天天一区二区日本电影三级| a级毛片a级免费在线| 真实男女啪啪啪动态图| 男女做爰动态图高潮gif福利片| 国产亚洲欧美98| 日韩人妻高清精品专区| 给我免费播放毛片高清在线观看| 免费无遮挡裸体视频| 听说在线观看完整版免费高清| 日韩大尺度精品在线看网址| 日韩欧美一区二区三区在线观看| 亚洲精品456在线播放app | 国内揄拍国产精品人妻在线| 中文字幕精品亚洲无线码一区| 午夜福利免费观看在线| 成年免费大片在线观看| 一区二区三区高清视频在线| a在线观看视频网站| av天堂中文字幕网| 男女之事视频高清在线观看| 欧美最黄视频在线播放免费| 天堂动漫精品| 级片在线观看| av欧美777| 国产精品亚洲av一区麻豆| 久久精品人妻少妇| 麻豆国产av国片精品| 国产极品精品免费视频能看的| 久久亚洲真实| 一个人看视频在线观看www免费 | 国产精品一及| 国产激情久久老熟女| 亚洲真实伦在线观看| 久99久视频精品免费| 夜夜看夜夜爽夜夜摸| 国产成人影院久久av| av天堂中文字幕网| 久久精品91无色码中文字幕| 曰老女人黄片| 我要搜黄色片| 91九色精品人成在线观看| 中文字幕熟女人妻在线| 久久久久久久久中文| 国产精品爽爽va在线观看网站| 看片在线看免费视频| 日本 av在线| 国产视频内射| 伊人久久大香线蕉亚洲五| 久久午夜亚洲精品久久| 久久国产精品影院| 亚洲aⅴ乱码一区二区在线播放| 不卡av一区二区三区| 国产精品av久久久久免费| 欧美乱码精品一区二区三区| 最好的美女福利视频网| 无遮挡黄片免费观看| 操出白浆在线播放| av天堂中文字幕网| 90打野战视频偷拍视频| 精品99又大又爽又粗少妇毛片 | 熟女人妻精品中文字幕| 久久久久国产精品人妻aⅴ院| 久久这里只有精品中国| 亚洲欧美激情综合另类| 欧美三级亚洲精品| av视频在线观看入口| xxx96com| 搡老岳熟女国产| 两个人看的免费小视频| 亚洲精品一区av在线观看| 又大又爽又粗| 黑人操中国人逼视频| 免费高清视频大片| 久久久国产成人精品二区| 大型黄色视频在线免费观看| 中国美女看黄片| 国产精品99久久久久久久久| 天堂动漫精品| 高清在线国产一区| 久久久国产欧美日韩av| 男人的好看免费观看在线视频| 熟妇人妻久久中文字幕3abv| 亚洲中文av在线| 中文字幕最新亚洲高清| 日韩 欧美 亚洲 中文字幕| aaaaa片日本免费| 亚洲 欧美 日韩 在线 免费| 国产精品爽爽va在线观看网站| 黑人欧美特级aaaaaa片| 日韩 欧美 亚洲 中文字幕| 欧美3d第一页| 亚洲av成人不卡在线观看播放网| 高清毛片免费观看视频网站| 婷婷亚洲欧美| 国产精品av久久久久免费| 国产成人av激情在线播放| 男女做爰动态图高潮gif福利片| 日本黄色片子视频| 日韩精品青青久久久久久| 熟女人妻精品中文字幕| 欧美日韩黄片免| 久久天躁狠狠躁夜夜2o2o| www日本在线高清视频| 国产一区二区三区在线臀色熟女| 精品国内亚洲2022精品成人| 国产欧美日韩一区二区三| 欧美中文日本在线观看视频| 波多野结衣高清无吗| av女优亚洲男人天堂 | 一夜夜www| 日日摸夜夜添夜夜添小说| 亚洲av美国av| 国产aⅴ精品一区二区三区波| 在线观看一区二区三区| 成年免费大片在线观看| 男人舔女人的私密视频| 男插女下体视频免费在线播放| 亚洲成人免费电影在线观看| av视频在线观看入口| 一个人看视频在线观看www免费 | 欧美黄色片欧美黄色片| 级片在线观看| 国产精品久久久久久精品电影| 美女扒开内裤让男人捅视频| 亚洲成人中文字幕在线播放| 成年女人毛片免费观看观看9| 天天躁狠狠躁夜夜躁狠狠躁| 久久这里只有精品19| 黄色 视频免费看| 麻豆成人午夜福利视频| 久久久久久人人人人人| 一本一本综合久久| 国产亚洲av高清不卡| 免费人成视频x8x8入口观看| 国产精品亚洲美女久久久| 国内毛片毛片毛片毛片毛片|