油田高含水期稳产措施配置多层目标规划

措施规划对于延长油田稳产年限 ,提高采油速度及提高最终采收率是十分必要的 .有些学者建立了油田稳产措施规划的整体或区块规划模型 ,但没有考虑实际油田生产各生产层系的地质特性和所采取措施的差别 .本文针对油田开发实际中存在多层现象 ,以区块的各个生产层为基础 ,建立了油田措施的多层目标规划模型 ,并采用合理的算法进行求解 .应用结果表明 ,多层目标规划使措施配置更精细 ,更能反映生产实际 ,是解决油田措施配置问题的一项有力工具     

油田增产措施合理规划的优化模型研究

在考虑均衡使用增产措施的前提下,以油田规划期内增产措施5年的产油量最大为目标,以增产措施的产油量下限、产水量和费用的上限、增产措施的工作量上限为约束条件建立了规划期内增产措施产油量最大的整数线性优化模型,并运用LINDO6.1对模型编程求解和分析,给油田规划提供一个很好的参照.     

油田开发措施配置的最优控制建模及求解

为了解决油田开发后期各种措施的规划问题,本文提出了一个最优控制数学模型,并讨论了其求解方法,将其应用于油田后期的开发措施配置,并通过油田实例应用证明了这一方法的实用性.     

基于SEC准则的油田开发规划不确定优化模型及算法

在油价低迷的国际背景下,上市石油公司进行油田开发规划时越来越重视经营效益。美国证券交易委员会(SEC)要求上市石油公司采用产量法计提折耗,极大地影响了石油公司开发规划方案的制定。油田开发受到自然、技术等多种不确定因素的影响,在制定开发规划时需充分考虑这些不确定性。本文基于不确定理论,考虑措施增油效果和新增投资两类不确定参数,以经营效益最大化和新增投资最小化为目标,构建了基于SEC准则的油田开发规划不确定优化模型,并利用差分进化算法求解,给出措施工作量的帕累托解集。本文以D油田年度规划为例,通过构建模型并求解,给出开发规划方案集,并进一步分析SEC储量的下降对上市石油公司经营效益、新增投资回报率、油气总产量、油气完全成本和措施工作量的影响,为企业制定开发规划方案提供参考。     

基于控制论的油田开发五年规划模型及其应用

由于油气系统的动态性和复杂性，长期以来油田的五年规划基本上都是采用逐年规划累加来进行代替，整体优化性有待提高。本文从整体优化的角度出发，将整个油气开发系统按其产量构成划分为若干个子系统，在研究总产量与其分项产量之间递变规律的基础上，结合功能模拟建立了油田产量开发系统的状态转移方程，并在此基础上利用目标规划建立了油田五年规划模型。实际应用表明，该模型能很好的解决油田开发的五年规划问题。     

油田产量构成的多阶段多目标优化

为了确保中后期原油稳产,如何将油田总产量合理细分到各采油厂成为油田开发规划面临的重要问题.建立了油田产量构成的多阶段多目标优化模型,将油田总产量按照产量构成的类型分配给各分项产量,进一步将各分项产量分配到各采油厂.结果表明,通过多阶段优化,在确保油田总产量稳定提高的同时,油田的总成本在两个阶段都有所下降,油田效益提高;此外,油田总产量目标被细分到了各采油厂的各分项产量,为油田决策者下达具体的产量指标提供了依据,以便油厂根据自身实际优化开发指标,完成油田下达的产量任务.     

基于不同优先级的油田增产多目标规划

为了衡量目标优先级不同对油田增产决策的影响,建立了基于不同优先级的多目标规划模型,并对增油量和措施费用两个目标不同优先级下的模型进行了求解,结果表明,目标优先级不同,油田增产措施的配置结果不同,但主要集中在对有效增产措施的配置上,对于效果较差的增产措施并无差异;同时,目标优先级不同导致目标的最优解相差较大,反映了决策者偏好对油田中后期增产决策的重要性.     

系统数值优化方法的研究与应用

大系统具有结构复杂、功能综合、因素众多等特点,相应的规划和决策问题的求解是非常困难的。我们针对大系统工程规划和决策的特点,结合运筹学的原理,提出了大系统数值优化概念,建立了优化模型,给出了优化问题的求解方法;并选择了中石油的DS-1油田作为应用实例,进行了油田采油工程数值优化,取得了很好的效果。为一般的复杂工程系统的规划和决策提供了一个新方法。     

油田开发规划多目标产量分配优化模型及其应用

利用功能模拟原理(微分模拟方法及神经网络方法)建立的油田开发指标关联关系，研究并建立了油田开发规划中的产量最大，成本最低。效益最好的多目标产量分配优化模型。这些优化模型成功地解决了油田的总产量及对应的工作量成本等其它开发指标最优地分配到各二级开采单位的产量最优分配问题，将模型应用于国内某中后期开发油田的开发规划中产生了较好的经济效益。     

二(双)层规划综述

二(双)层规划是研究二层决策的递阶优化问题.其理论、方法和应用在过去的30多年取得了很大的发展.本文对二层规划问题的基本概念、性质和算法作了综述,并且对下层规划问题的解不唯一的情况也作了介绍,最后还给出了几种常见的二层规划模型.     

Descent methods for convex essentially smooth minimization

Consider the problem of minimizing a convex essentially smooth function over a polyhedral set. For the special case where the cost function is strictly convex, we propose a feasible descent method for this problem that chooses the descent directions from a finite set of vectors. When the polyhedral set is the nonnegative orthant or the entire space, this method reduces to a coordinate descent method which, when applied to certain dual of linearly constrained convex programs with strictly convex essentially smooth costs, contains as special cases a number of well-known dual methods for quadratic and entropy (either –logx orx logx) optimization. Moreover, convergence of these dual methods can be inferred from a general convergence result for the feasible descent method. When the cost function is not strictly convex, we propose an extension of the feasible descent method which makes descent along the elementary vectors of a certain subspace associated with the polyhedral set. The elementary vectors are not stored, but generated using the dual rectification algorithm of Rockafellar. By introducing an -complementary slackness mechanism, we show that this extended method terminates finitely with a solution whose cost is within an order of of the optimal cost. Because it uses the dual rectification algorithm, this method can exploit the combinatorial structure of the polyhedral set and is well suited for problems with a special (e.g., network) structure.This work was partially supported by the US Army Research Office Contract No. DAAL03-86-K-0171 and by the National Science Foundation Grant No. ECS-85-19058.     

On dynamic programming: Compactness of the space of policies

The compactness of the set of policies in a dynamic programming decision model, which guarantees the existence of an optimal policy, is proven by reducing the problem to the compactness of the set of probability measures which are induced by the policies. When studying the set of probability measures, use is made of the weak topology and the so-called ws^∞-topology. A definition and a discussion of the latter topology is given in this paper, where we pay attention to criteria for relative compactness.     

Robust risk management

Estimating the probabilities by which different events might occur is usually a delicate task, subject to many sources of inaccuracies. Moreover, these probabilities can change over time, leading to a very difficult evaluation of the risk induced by any particular decision. Given a set of probability measures and a set of nominal risk measures, we define in this paper the concept of robust risk measure as the worst possible of our risks when each of our probability measures is likely to occur. We study how some properties of this new object can be related with those of our nominal risk measures, such as convexity or coherence. We introduce a robust version of the Conditional Value-at-Risk (CVaR) and of entropy-based risk measures. We show how to compute and optimize the Robust CVaR using convex duality methods and illustrate its behavior using data from the New York Stock Exchange and from the NASDAQ between 2005 and 2010.     

解非凸规划问题动边界组合同伦方法

本文给出了一个新的求解非凸规划问题的同伦方法,称为动边界同伦方程,并在较弱的条件下,证明了同伦路径的存在性和大范围收敛性．与已有的拟法锥条件、伪锥条件下的修正组合同伦方法相比,同伦构造更容易,并且不要求初始点是可行集的内点,因此动边界组合同伦方法比修正组合同伦方法及弱法锥条件下的组合同伦内点法和凝聚约束同伦方法更便于应用．     

Multiple goal non-cooperative conflicts under uncertainty: A fuzzy set approach

This paper investigates strategy selection for a participant in a two-party non-cooperative conflict which involves both uncertainty and multiple goals. Uncertainty arises from the players not knowing the utility functions. Multiple objectives appear as the result of the payoff being a vector of prizes and the players attempt to attain various goals for each prize separately. The main objective is to present a fuzzy set/fuzzy programming solution concept to the conflict situation. In doing so, we compare a Bayesian player to one that employs fuzzy set techniques. We point out some of the advantages of the fuzzy set method. The necessary computations in the fuzzy set method are explained in detail through an example.     

Control the Fibre Orientation Distribution at the Outlet of Contraction

A two dimensional model of the orientation distribution of fibres in a paper machine headbox is studied. The goal is to control the fibre orientation distribution at the outlet of contraction by changing its shape. The mathematical formulation leads to an optimization problem with control in coefficients of a linear convection-diffusion equation as the state problem. Then, the problem is expressed as an optimal control problem governed by variational forms. By using an embedding method, the class of admissible shapes is replaced by a class of positive Radon measures. The optimization problem in measure space is then approximated by a linear programming problem. The optimal measure representing optimal shape is approximated by the solution of this linear programming problem. In this paper, we have shown that the embedding method (embedding the admissible set into a subset of measures), successfully can be applied to shape variation design to a one dimensional headbox. The usefulness of this idea is that the method is not iterative and it does not need any initial guess of the solution.      

An interior semi-infinite programming method

In this paper, a new method for semi-infinite programming problems with convex constraints is presented. The method generates a sequence of feasible points whose cluster points are solutions of the original problem. No maximization over the index set is required. Some computational results are also presented.This work was partly supported by Republicka Zajednica za Nauku Socijalisticke Republike Srbije. The authors are indebted to Professor R. A. Tapia for encouraging the approach taken in this research.     

An active set strategy for solving optimization problems with up to 200,000,000 nonlinear constraints

Numerical test results are presented for solving smooth nonlinear programming problems with a large number of constraints, but a moderate number of variables. The active set method proceeds from a given bound for the maximum number of expected active constraints at an optimal solution, which must be less than the total number of constraints. A quadratic programming subproblem is generated with a reduced number of linear constraints from the so-called working set, which is internally changed from one iterate to the next. Only for active constraints, i.e., a certain subset of the working set, new gradient values must be computed. The line search is adapted to avoid too many active constraints which do not fit into the working set. The active set strategy is an extension of an algorithm described earlier by the author together with a rigorous convergence proof. Numerical results for some simple academic test problems show that nonlinear programs with up to 200,000,000 nonlinear constraints are efficiently solved on a standard PC.     

Spherical minimax location problem using the Euclidean norm: Formulation and optimization

The minimax spherical location problem is formulated in the Cartesian coordinate system using the Euclidean norm, instead of the spherical coordinate system using spherical arc distance measures. It is shown that minimizing the maximum of the spherical arc distances between the facility point and the demand points on a sphere is equivalent to minimizing the maximum of the corresponding Euclidean distances. The problem formulation in this manner helps to reduce Karush-Kuhn-Tucker necessary optimality conditions into the form of a set of coupled nonlinear equations, which is solved numerically by using a method of factored secant update with a finite difference approximation to the Jacobian. For a special case when the set of demand points is on a hemisphere and one or more point-antipodal point pair(s) are included in the demand points, a simplified approach gives a minimax point in a closed form.     

DETERMINATION OF EFFICIENT RANGE OF TARGET LEVELS IN MULTIPLE OBJECTIVE PLANNING

This paper describes a nonlinear programming model combined with a binary search technique that systematically searches for the minimum value of a given objective within the nondominated solution set. The procedure provides a way of determining the range of efficient target levels for any multiobjective planning problem using information contained in the pay-off table. The method is illustrated using a numerical example.