应用地震反演技术预测储层

为了解决开发过程中储层精细刻画难题,提出将曲线重构地震反演技术引入带储层预测工作中,根据曲线不同频带范围对砂岩敏感性特征,开展基于高频恢复、低频补偿原来的曲线重构技术开展地震反演预测.通过对南八区西部的地震反演实例表明:对于大于2m砂岩分辨能力较高的,隔层太小时,两套砂岩只能当一套砂岩组合反演出来;对于小于2米的砂岩,只有当隔层大于4m条件下才能清晰识别.地震反演能很好的推测地下岩层结构和物性参数的空间分布,有效的提高了薄储层预测精度.     

On the numerical approximation of the rotation number

The starting point of this paper is a polygonal approximation of an invariant curve of a map. Using this polygonal approximation an approximation for the circle map (the restriction of the map to the invariant curve) is obtained. The rotation number of the circle map is then approximated by the rotation number of the approximated circle map. The error in the obtained approximate rotation number is discussed, and related to the error in the polygonal approximation of the invariant curve. Simple algorithms for the approximation of the rotation number are described. A numerical example illustrates the theory.     

喇萨杏油田储层非均质性表征新参数——地层系数变异程度

目前储层非均质性的表征主要考虑渗透率这一个因素,衍生出的非均质参数有时会受到平均值、最大值的影响使非均质表征的准确程度降低,为此结合喇萨杏油田20口取芯井资料,基于洛伦兹曲线,引入储层有效厚度,提出储层非均质性表征的新参数一地层系数变异程度,以细分小层为单位,将各小层的地层系数由高到低排序,绘制地层系数贡献率和层数累计百分数的关系曲线,计算的基尼系数即为地层系数变异程度,该参数与级差、突进系数和变异系数相比更加全面的反映了储层的非均质程度,并与储层动用程度成反比.经过实例分析可得,地层系数变异程度对储层动用程度的影响更加显著,该参数与动用程度关系曲线的相关系数为0.855,高于其他非均质参数,当只考虑薄差储层时,相关系数可达0.9866,所以新参数可应用于评价储层的宏观非均质程度,尤其是薄差储层.     

Focal approximation on the complex plane

The problem of analytic approximation of a smooth closed curve specified by a set of its points on the complex plane is proposed. An algorithmic method for constructing an approximating lemniscate is proposed and investigated. This method is based on a mapping of the curve to be approximated onto the phase circle; the convergence of the method is proved. The location of the lemniscate foci inside the curve provides the degrees of freedom for the focal approximation.     

Discrete Variational Calculus for B-Spline Curves

In this work, we study discrete variational problems, for B-spline curves, which are invariant under translation and rotation. We show this approach has advantages over studying smooth variational problems whose solutions are approximated by B-spline curves. The latter method has been well studied in the literature but leads to high order approximation problems. We are particularly interested in Lagrangians that are invariant under the special Euclidean group for which B-spline approximated curves are well suited. The main application we present here is the curve completion problem in 2D and 3D. Here, the aim is to find various aesthetically pleasing solutions as opposed to a solution of a physical problem. Smooth Lagrangians with special Euclidean symmetries involve curvature, torsion, and arc length. Expressions of these, in the original coordinates, are highly complex. We show that, by contrast, relatively simple discrete Lagrangians offer excellent results for the curve completion problem. The novel methods we develop for the discrete curve completion problem are general, and can be used to solve other discrete variational problems for B-spline curves. Our method completely avoids the difficulties of high order smooth differential invariants.     

Asymptotic expansions for potential functions of i.i.d. random fields

Summary For sums of finite range potential functions of an iid random field we derive the validity of formal expansions of length two. Under standard conditions, formal expansions are valid if and only if the characteristic functions of the sum converge to zero for all nonzero frequency parameters. If this convergence fails, the distribution of the sum can be approximated by a mixture of lattice distributions. The result applies to m-dependent random fields generated by independent random variables.     

Numerical algorithms for Panjer recursion by applying Bernstein approximation

In actuarial science, Panjer recursion (1981) is used in insurance to compute the loss distribution of the compound risk models. When the severity distribution is continuous with density function, numerical calculation for the compound distribution by applying Panjer recursion will involve an approximation of the integration. In order to simplify the numerical algorithms, we apply Bernstein approximation for the continuous severity distribution function and obtain approximated recursive equations, which are used for computing the approximated values of the compound distribution. The theoretical error bound for the approximation is also obtained. Numerical results show that our algorithm provides reliable results.     

Regularization of the double period method for experimental data processing

In physical and technical applications, an important task is to process experimental curves measured with large errors. Such problems are solved by applying regularization methods, in which success depends on the mathematician’s intuition. We propose an approximation based on the double period method developed for smooth nonperiodic functions. Tikhonov’s stabilizer with a squared second derivative is used for regularization. As a result, the spurious oscillations are suppressed and the shape of an experimental curve is accurately represented. This approach offers a universal strategy for solving a broad class of problems. The method is illustrated by approximating cross sections of nuclear reactions important for controlled thermonuclear fusion. Tables recommended as reference data are obtained. These results are used to calculate the reaction rates, which are approximated in a way convenient for gasdynamic codes. These approximations are superior to previously known formulas in the covered temperature range and accuracy.     

Stochastic dual dynamic programming applied to nonconvex hydrothermal models

In this paper we apply stochastic dual dynamic programming decomposition to a nonconvex multistage stochastic hydrothermal model where the nonlinear water head effects on production and the nonlinear dependence between the reservoir head and the reservoir volume are modeled. The nonconvex constraints that represent the production function of a hydro plant are approximated by McCormick envelopes. These constraints are split into smaller regions and the McCormick envelopes are used for each region. We use binary variables for this disjunctive programming approach and solve the problem with a decomposition method. We resort to a variant of the L-shaped method for solving the MIP subproblem with binary variables at any stage inside the stochastic dual dynamic programming algorithm. A realistic large-scale case study is presented.     

垂直密度表示及其应用

该文论述了Vertical Density Representation (VDR)的历史发展, 现状及其在随机数生成, 多元密度构造等领域的应用及在非正态多元统计分析的潜在应用.     

Prediction of the life of glued joints under load by application of the time - temperature superposition principle

The applicability of the time-temperature superposition principle for predicting the life of glued joints from the results of accelerated tests at elevated temperatures and loads is established at phenomenological levels. It is shown that the master curve can be approximated by a dependence analogous to the Zhurkov equation.     

A labeling algorithm for the sensitivity ranges of the assignment problem

In view of the simplex-type algorithm, the assignment problem is inherently highly degenerate. It may be the optimal basis has changed, but the optimal assignment is unchanged when parameter variation occurs. Degeneracy then makes sensitivity analysis difficult, as well as makes the classical Type I range, which identifies the range the optimal basis unchanged, impractical. In this paper, a labeling algorithm is proposed to identify two other sensitivity ranges – Type II range and Type III range. The algorithm uses the reduced cost matrix, provided in the final results of most solution algorithms for AP, to determine the Type II range which reflects the stability of the current optimal assignment. Thus, the algorithm generates a streamlined situation from searching the optimal solution until performing the sensitivity analysis of the assignment problem. The Type III range, reflecting the flexibility of optimal decision making, can be obtained immediately after the Type II range is determined. Numerical examples are presented to demonstrate the algorithm.     

Type I operators and the approximation of singular two-point boundary value problems

A class of self adjoint operators associated with second order singular ordinary differential expressions, arises naturally when the problem is weakly formulated and integration by parts is performed. We call this class Type I operators. It turns out that this class can be successfully used to tackle numerical approximations of singular two-point boundary value problems. They can also be approximated by regular differential operators in a straightforward manner without having to bring the delicate structure of singular differential operators to the forefront of the investigation.     

Approximation of multivariate distribution functions

In the paper the unknown distribution function is approximated with a known distribution function by means of Taylor expansion. For this approximation a new matrix operation — matrix integral — is introduced and studied in [PIHLAK, M.: Matrix integral, Linear Algebra Appl. 388 (2004), 315–325]. The approximation is applied in the bivariate case when the unknown distribution function is approximated with normal distribution function. An example on simulated data is also given.      

Monte carlo estimation of the sampling distribution of nonlinear model parameter estimators

The sampling distribution of parameter estimators can be summarized by moments, fractiles or quantiles. For nonlinear models, these quantities are often approximated by power series, approximated by transformed systems, or estimated by Monte Carlo sampling. A control variate approach based on a linear approximation of the nonlinear model is introduced here to reduce the Monte Carlo sampling necessary to achieve a given accuracy. The particular linear approximation chosen has several advantages: its moments and other properties are known, it is easy to implement, and there is a correspondence to asymptotic results that permits assessment of control variate effectiveness prior to sampling via measures of nonlinearity. Empirical results for several nonlinear problems are presented.This research was supported in part by the Office of Naval Research under Contract N00014-79-C-0832.     

Numerical Computation of Parametric Induced Roll Motions in Random Seas

For a vessel in open seas, the sudden appearance of roll motions due to waves from the front or rear leads to dangerous situations up to capsizing. The equations of motion used to analyze the roll motion include the righting lever curve. This curve is set up by means of hydrostatic calculations and approximated by polynomials for further analysis. The irregular waves are modeled in terms of a continuous-time ARMA process. The resulting model of stochastic differential equations is investigated numerically by Local Statistical Linearization. The necessary stochastic moments and their derivatives are computed using Itô's differential rule and Gaussian closure. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Fast and Accurate Approximation of the Full Conditional for Gamma Shape Parameters

The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt with by using either Metropolis–Hastings moves, rejection sampling methods, or numerical integration. However, in models with a large number of shape parameters, these existing methods are slower or more complicated than one would like, making them burdensome in practice. It turns out that the full conditional distribution of the gamma shape parameter is well approximated by a gamma distribution, even for small sample sizes, when the prior on the shape parameter is also a gamma distribution. This article introduces a quick and easy algorithm for finding a gamma distribution that approximates the full conditional distribution of the shape parameter. We empirically demonstrate the speed and accuracy of the approximation across a wide range of conditions. If exactness is required, the approximation can be used as a proposal distribution for Metropolis–Hastings. Supplementary material for this article is available online.     

On G continuous interpolatory composite quadratic Bézier curves

In the paper the interpolation by G² continuous composite quadratic Bézier curves is studied. It is shown that the interpolation problem can be naturally posed correctly in such a way that a smooth curve f is approximated up to the order 4, i.e., one order more than in the corresponding function case. In addition, the tangent direction of f is approximated up to order 3, and the curvature up to order 2.     

Concertina-like movements of the error curve in the alternation theorem

If a continuous function f is approximated by elements of a Haar space in the maximum norm on an interval, the error curve of the best approximation has well known alternation properties. It is shown that if f is adjoined to the Haar space all zeros of the error function are monotonously increasing functions of the endpoints, and that under an additional hypothesis, the entire graph of the error curve is shifted to the left or right when the endpoints are moved accordingly.