Numerical Simulation of Oil-Water Two-Phase Flow in Low Permeability Tight Reservoirs Based on Weighted Least Squares Meshless Method

In response to the complex characteristics of actual low-permeability tight reservoirs,this study develops a meshless-based numerical simulation method for oil-water two-phase flow in these reservoirs,considering complex boundary shapes.Utilizing radial basis function point interpolation,the method approximates shape functions for unknown functions within the nodal influence domain.The shape functions constructed by the aforementioned meshless interpolation method haveδ-function properties,which facilitate the handling of essential aspects like the controlled bottom-hole flow pressure in horizontal wells.Moreover,the meshless method offers greater flexibility and freedom compared to grid cell discretization,making it simpler to discretize complex geometries.A variational principle for the flow control equation group is introduced using a weighted least squares meshless method,and the pressure distribution is solved implicitly.Example results demonstrate that the computational outcomes of the meshless point cloud model,which has a relatively small degree of freedom,are in close agreement with those of the Discrete Fracture Model(DFM)employing refined grid partitioning,with pressure calculation accuracy exceeding 98.2%.Compared to high-resolution grid-based computational methods,the meshless method can achieve a better balance between computational efficiency and accuracy.Additionally,the impact of fracture half-length on the productivity of horizontal wells is discussed.The results indicate that increasing the fracture half-length is an effective strategy for enhancing production from the perspective of cumulative oil production.

WEIGHTED LEAST SQUARE METHOD FOR S-N CURVE FITTING

An S-N curve fitting approach is proposed based on the weighted least square method, and the weights are inversely proportional to the length of mean confidence intervals of experimental data sets. The assumption coincides with the physical characteristics of the fatigue life scatter. Two examples demonstrate the method. It is shown that the method has better accuracy and reasonableness compared with the usual least square method.

ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

Preconditioned iterative methods for solving weighted linear least squares problems

A class of preconditioned iterative methods, i.e., preconditioned generalized accelerated overrelaxation （GAOR） methods, is proposed to solve linear systems based on a class of weighted linear least squares problems. The convergence and comparison results are obtained. The comparison results show that the convergence rate of the preconditioned iterative methods is better than that of the original methods. Furthermore, the effectiveness of the proposed methods is shown in the numerical experiment.

STUDY ON FREQUENCY ESTIMATION BASED ON WEIGHTED LEAST SQUARE METHOD WITH THREE FOURIER COEFFICIENTS

In this paper,a sinusoidal signal frequency estimation algorithm is proposed by weighted least square method.Based on the idea of Provencher,three biggest Fourier coefficients in the maximum periodogram are considered,the Fourier coefficients can be written as three equations about the amplitude,phase,and frequency,and the frequency is estimated by solving equations.Because of the error of measurement,weighted least square method is used to solve the frequency equation and get the signal frequency.It is shown that the proposed estimator can approach the Cramer-Rao Bound(CRB)with a low Signal-to-Noise Ratio(SNR)threshold and has a higher accuracy.

A STUDY ON THE WEIGHT FUNCTION OF THE MOVING LEAST SQUARE APPROXIMATION IN THE LOCAL BOUNDARY INTEGRAL EQUATION METHOD

The meshless method is a new numerical technique presented in recent years.It uses the moving least square(MLS)approximation as a shape function.The smoothness of the MLS approximation is determined by that of the basic function and of the weight function,and is mainly determined by that of the weight function.Therefore,the weight function greatly affects the accuracy of results obtained.Different kinds of weight functions,such as the spline function, the Gauss function and so on,are proposed recently by many researchers.In the present work,the features of various weight functions are illustrated through solving elasto-static problems using the local boundary integral equation method.The effect of various weight functions on the accuracy, convergence and stability of results obtained is also discussed.Examples show that the weight function proposed by Zhou Weiyuan and Gauss and the quartic spline weight function are better than the others if parameters c and α in Gauss and exponential weight functions are in the range of reasonable values,respectively,and the higher the smoothness of the weight function,the better the features of the solutions.

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.

Data point selection for weighted least square fitting of cavity decay time constant

For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting （NLSF） method and the linear regression of the sum （LRS） method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.

Criteria for Weighted Moving-Mean Method

The moving-mean method is one of the conventional approaches for trend-extraction from a data set. It is usually applied in an empirical way. The smoothing degree of the trend depends on the selections of window length and weighted coefficients, which are associated with the change pattern of the data. Are there any uniform criteria for determining them? The present article is a reaction to this fundamental problem. By investigating many kinds of data, the results show that: 1) Within a certain range, the more points which participate in moving-mean, the better the trend function. However, in case the window length is too long, the trend function may tend to the ordinary global mean. 2) For a given window length, what matters is the choice of weighted coefficients. As the five-point case concerned, the local-midpoint, local-mean and global-mean criteria hold. Among these three criteria, the local-mean one has the strongest adaptability, which is suggested for your usage.

WEIGHTED NONLINEAR REGRESSION

Minimization of the weighted nonlinear sum of squares of differences may be converted to the minimization of sum of squares. The Gauss-Newton method is recalled and the length of the step of the steepest descent method is determined by substituting the steepest descent direction in the Gauss-Newton formula. The existence of minimum is shown.

事件触发机制下配电网三相动态状态估计

随着高级量测体系的发展和智能电表的广泛应用,为配电网三相状态估计提供了丰富的终端量测信息。与此同时,大量的智能电表数据给配电网通信系统提出了更高的通信带宽和实时存储要求。为了缓解量测拥堵和时延现象,文中引入事件触发机制代替传统量测数据的周期性采样,在保证有效量测信息及时上传的同时减少通信成本和投资。在此基础上,针对配电网实时状态感知问题,提出了基于鲁棒集合卡尔曼滤波的配电网三相动态状态估计方法,在正常运行场景下,能够保持与无偏估计加权最小二乘法相近的估计精度;当含有坏数据时,该方法也拥有较强的鲁棒性。

基于卡尔曼滤波算法的电池状态估计

为更好地获得锂离子电池荷电状态SOC(state-of-charge)估计值,选用二阶等效电路模型作为研究对象,针对带有遗忘因子的递推最小二乘法在参数辨识中易受到噪声等环境因素干扰的缺点,提出偏差补偿最小二乘法来实现模型参数的准确辨识,并结合无迹卡尔曼滤波算法对SOC进行估计。针对无迹卡尔曼滤波算法稳定性差等缺点,提出利用权重向量更新滤波算法中的卡尔曼滤波增益。实验结果表明,所提算法估计SOC的总误差可控制在2.7%以内,验证了算法的鲁棒性和有效性。

含未知输入非线性系统的扩展平方根容积卡尔曼滤波算法

针对工程实际应用中存在的未知输入会导致经典的非线性滤波器状态估计精度下降甚至滤波发散的问题,提出了一种基于最小方差无偏估计(minimum variance unbiased estimation,MVUE)准则的扩展平方根容积卡尔曼滤波(extended square-root cubature Kalman filter,ESRCKF)算法。首先,结合上一时刻未知输入估计值对状态一步预测值进行修正,得到含未知输入条件下的状态预测值。其次,设计新息并采用加权最小二乘(weighted least squares,WLS)法获取当前时刻未知输入的无偏估计。最后,通过最小化协方差矩阵的迹,同时采用拉格朗日乘子法和舒尔补引理得到系统状态的最小方差无偏估计。仿真结果表明,相比于现有的非线性滤波算法,ESRCKF算法提高了在处理含未知输入非线性系统时的状态估计精度,并能同时实现系统状态和未知输入的最优估计,验证了该算法的有效性。

未知信号传播速度的TDOA定位方法研究

研究了未知信号传播速度时的到达时间差(Time-Difference-Of-Arrival,TDOA)定位问题,提出两种联合估计信号传播速度和目标位置的定位方法。第一种方法为两步加权最小二乘方法。在该方法中,首先不考虑变量之间关系,得到一个关于未知变量的初始加权最小二乘估计。为改进第一步估计的性能,第二步考虑第一步估计中变量之间的关系,将其转换为一个标准的广义信赖域子问题,最终获得更高精度的估计性能。第二种方法为半正定松弛方法,通过构建非线性非凸加权最小二乘问题,然后利用半正定松弛技术将其松弛为凸的半正定规划问题,容易求解。仿真结果表明,所提两种方法均能够在高斯噪声下,且噪声不太大时达到克拉美-罗界。

考虑地震事件内空间相关性的区域概率地震危险性分析

一次地震事件不同地点的地震动强度参数与结构响应参数之间是空间相关的,相关的地震效应将导致地震损失急剧集中与积累,从而造成灾难性事件,但是现有的区域尺度地震风险评估并没有考虑地震动的空间相关性。该文采用地统计学方法表征地震事件内地震动强度参数的空间相关性,提出了理论半变异函数的几何稳健估计方法,并采用加权最小二乘法实现对短距离空间相关性的准确高效评估。以新潟越冲地震为例,基于指数半变异函数模型,采用加权最小二乘法对几何稳健估计半变异函数值进行拟合,得到了PGA、PGV和0 s~10 s谱加速度的空间相关性函数,提出了变程b值的预测模型,结果表明:相比于短周期谱加速度,长周期谱加速度的空间相关性衰减速率更小,且在T=0.45 s处存在拐点。将所开发的空间相关性模型应用于区域概率地震危险性分析,采用Monte Carlo方法实现了空间相关随机场的区域地震动强度参数的模拟,改进了传统区域地震危险性分析的方法,发现绝大多数情况下考虑空间相关性的年超越概率大于未考虑空间相关性的年超越概率,表明考虑空间相关性对于建筑群落及基础设施风险评估、韧性城市建设等具有重要的指导意义。

基于时序聚类及改进Pettitt的系统谐波阻抗估计

针对谐波电压与电流数据出现严重无序性,导致常规非干预式方法难以对系统谐波阻抗进行准确的实时估计,提出一种基于时序聚类及改进Pettitt的系统谐波阻抗估计方法。首先,运用时序聚类中互相关系数原理使谐波电压和电流序列平移对齐并筛选出强相关性的数据。然后,为了规避阻抗突变的影响,提出基于二元分割的Pettitt方法对阻抗突变点进行检验。最后,使用再生权最小二乘法,通过赋权重系数进一步减小异常值对结果的影响。仿真与实例结果表明,本文方法可以有效降低背景谐波波动和阻抗突变对系统谐波阻抗估计带来的误差。

岩石抗剪强度参数的加权线性回归估计与验证

研究了岩石抗剪强度参数的优化估计方法,针对岩石抗剪强度数据中存在的异方差问题,引入了加权线性回归方法。在岩石抗剪强度的线性回归分析中,一般假定因变量观测值的观测误差都服从同一正态分布,但忽略了三轴强度数据中所存在的异方差问题。利用位于四川省西南地区的露天砂岩为研究对象,获取砂岩在不同围压下的三轴强度数据并应用加权线性回归方法进行分析并解决了数据的异方差问题。首先介绍了岩石抗剪强度的理论基础和相关的统计方法,然后详细阐述了加权线性回归的概念及应用范围。提出了加权线性回归的理论公式和检验方法,并解释了加权回归中的权重定义和计算方法。最后通过位于四川省西南地区的露天砂岩的试验数据进行验证,研究结果证明了加权最小二乘法在岩石抗剪强度参数估计中的有效性,研究成果可为抗剪强度参数的选取提供相关的理论依据。

基于改进权函数距离的机器人运动偏差补偿算法设计

针对机器人在组装和生产过程中,由于几何参数存在一定误差,使连杆以及关节处等不可避免地出现细微差别,对机器人运动精度产生一定影响问题,提出了基于改进权函数距离的机器人运动偏差补偿算法设计方案。在定位机器人位置前添加扭角,获取机器人两坐标系间转换矩阵,根据线性标定计算机器人运动定位的绝对误差,利用改进权函数建立机器人距离误差数学模型,初步补偿运动误差。计算机器人末端执行器中心点位置和姿态的偏差,将补偿问题转化成机器人运动优化问题,得出运动偏差优化问题目标函数,经多次迭代得出最终补偿结果。实验结果表明,所提方法的误差补偿效果佳,重心补偿后的机器人运行稳定性好。

基于分布式网络的水面舰艇编队一体化导航方法

[目的]针对体系化作战中,水面舰艇编队导航面临设备损毁、导航干扰等复杂作战环境的挑战,提出一种基于分布式网络的水面舰艇编队一体化导航系统及方法。[方法]通过分布式协同导航提升编队应对复杂环境的可靠性和灵活性。编队一体化导航系统分为设备层、系统层和体系层,设备层输出原始的GNSS/INS导航数据,系统层输出节点独立导航数据,体系层融合生成编队协同导航数据。为实现体系层编队协同导航数据融合,采用Floyd算法,以基线传递路径最短的原则构建分布式编队协同导航网络,在节点独立导航数据的基础上通过动态基线传递生成节点协同导航数据;通过加权最小二乘法融合多个节点协同导航数据输出编队协同导航数据。[结果]仿真结果表明,应用该方法可成功构建分布式协同导航网络。通过导航支援,保障了受毁伤节点导航的不中断;通过精度支援,提升了受干扰节点的导航精度。[结论]分布式协同导航具有抗毁伤、抗干扰、可提高精度的优势,并可有效应对复杂的作战环境。

高分辨率罗兰圆光学系统的色差校正方法研究

罗兰圆光学系统优点众多,但系统中因平凸透镜的使用,导致各特征波长色散后的成像点在罗兰圆附近产生了一定量的偏移量,使得各波长成像点依次排列在一条曲面上。由于目前新型采集器件线阵CMOS或CCD的使用,而这类传感器的感光面是一个平面,因此导致结果产生较严重的色差。提出了一种使用加权最小二乘法拟合像面的色差校正方法,并计算出系统像面的中心偏移距和偏移倾斜角这两个校正参数,将其引入原本罗兰圆光学系统进行色差校正,从而确定线阵光电传感器像面的最佳位置,实现高分辨率。此外在真空紫外光谱中C、P和S等元素非常重要,可使用本文提出的基于加权最小二乘法的色差校正方法,通过增加这类元素权值占比,实现最优分辨率。最后通过Zemax软件进行光学仿真,完成了罗兰圆光学系统的仿真以及优化。并给出170~410 nm波段点列图的成像宽度仿真以及各元素特征波长在CMOS传感器上所采集到的光谱图,推算出其实际应用中的分辨率,其照度光谱图显示其分辨率均可保持在0.02 nm,接近系统理论最佳分辨率。研究结果表明基于加权最小二乘法拟合像面的方法,可实现罗兰圆光学系统的色差校正,实现高分辨率。