三维水平井最优控制系统及应用

建立了三维水平井井眼轨迹的非线性多阶段动力系统，论述了该系统解的存在性及其最优控制的可控性和多解性。以均匀设计方法选初始点，并依此把允许区域分解为有限多个子域，在每个子域上构造了改进的Hooke-Jeeves优化算法。将它用于多口水平井的实际生产中，表明了本文给出的模型、算法及软件的正确性与有效性。     

侧钻水平井轨道设计模型、算法及应用

本针对三维水平井轨道优化设计问题，建立了以非线性常微分方程为主要约束条件的最优控制模型。以均匀设计方法选初始点，并依此把允许区域分解为有限多个子域，在每个子域上构造了改进的Hooke-Jeeves优化算法，将它用于多口水平井的实际生产中，表明了本给出的模型、算法及软件的正确性与有效性。     

非均质气藏水平井三维渗流的产能预测方法

建立了非均质气藏水平井三维渗流产能预测的数学模型,采用有限元方法对其进行求解,求解时将地质模型区块中不同空间位置处的渗透率值以及其它气藏物性参数分别布置到有限元模型的相应位置处的网格中,从而体现了三维空间中气藏的非均质特性.分析了非均质性对气藏水平井产能的影响.结果表明,气藏的非均质性对水平井的产气量影响很大,在相同的生产压差下,存在高渗透带的气藏水平井产量明显高于均质气藏水平井的产量;高渗透带条数越多、渗透率越大,导致气藏内压力消耗越小,水平井产量越高.最后,结合松辽盆地徐深层气田的地质特点和储层特性,给出了该气田的水平井产能预测实例.研究方法符合气藏的实际情况,为气藏水平井,特别是非均质气藏水平井的产能预测提供了一种行之有效的方法.     

一种模糊优化的全局算法及在三维水平井轨道设计中应用

本基于一种新的全局优化算法(EM)，提出一种求解模糊优化问题的全局优化算法。针对三维水平井轨道设计问题，提出两个模糊模型。最后把算法及模型应用到实际问题中，数值结果表明算法及模型是有效的、正确的。     

大挠度钻柱强度分析的平衡微分方程法

对小井眼、大曲率井中钻柱强度问题,以井轴为基准轴,在对井轴弯挠描述和钻柱微段三维受力变形分析的基础上,建立大位移钻柱平衡微分方程,采用Longe-Kutta法解之求内力,并依此求应力和建立强度条件.对H767侧钻水平井施工中钻柱应力计算分析,结果说明与有限元模型和弹性化软绳模型比较相吻合,该模型比有限元模型计算简捷方便;比弹性化软绳模型更完善可信;该井钻柱破坏事故愿因在于井眼曲率过大,兼有应力集中.     

二维井眼轨道设计模型及其精确解

讨论了典型的二维井眼轨道设计问题,建立了二维井眼轨道设计的一般数学模型,并求得了其全部精确解.这种方法避免了在设计中进行试算,设计计算简单、精确、快速.该模型具有普遍适用性,可广泛用于二维定向井、水平井和多目标井的井眼轨道设计.     

Newton-非Newton幂律流双区复合水平井压力动态特征分析

聚合物驱油在提高原油采收率方面具有重要作用，水平井在薄层油田的开发过程中占有重要优势.根据聚合物驱油过程，基于点源函数基本理论，建立了Newton-非Newton双区复合水平井试井解释数学模型.利用Laplace积分变换和Fourier有限余弦积分变换方法，获得了Laplace空间Newton-非Newton双区复合水平井试井解释数学模型的解析解.通过Stehfest数值反演得到了典型无因次井底压力、压力导数特征曲线.研究表明:幂律指数越小，外区压力、压力导数曲线上翘越明显，且压力导数曲线呈斜率为(1-m)/(3-m)的直线；当幂律指数为1时，该模型简化为水平井常规双区复合模型；水平井水平段长度越长，早期径向流阶段结束的时间越早； 内外区流度比越大、 内区半径越小， 外区压力和压力导数曲线位置越高， 且始终保持斜率为(1-m)/(3-m)的直线.     

基于组合权重法的页岩气水平井段评价模型及其应用

为实现页岩气水平井压裂层段精细开发,总结并筛选出了合适的段评价指标,利用测完井数据计算了水平井段各项指标参数,基于提出的段指标评价体系,采用层次分析法和熵值法相结合的组合权重方法开展了指标权重计算,通过乘法合成形式将标准化指标值和组合权重进行结合并建立水平井段评价模型,以重庆市涪陵区焦石坝A1井为例进行验证与应用,评价结果表明:A1井水平段整体处于中等甜点区,物性和可压裂性较好;与FSI产气剖面对比,段评价指数和与单段产气量具有较好的正相关关系,本评价模型能有效避免A1井第4段和第6段的无效性,以期为水平井精细开发时提供参考.     

页岩压裂水平井增产改造体积的动态演化模型

页岩气藏开发实践表明,水平井分段压裂是实现页岩气藏经济有效开发的关键技术,其中增产改造体积(SRV)是控制页岩压后效果的核心参数,对SRV的准确计算和表征已成为页岩压裂研究领域的重点难点问题.基于目前SRV评价方法的局限性,考虑动态扩展裂缝与储层应力场和压力场的耦合作用,以及它们实时变化触发储层天然裂缝的破裂机制,建立了一种SRV动态演化计算模型,采用该模型可以计算和表征裂缝动态扩展形态、储层渗透率演化分布以及SRV的空间展布.由于模型与页岩压裂过程中储层SRV实际动态扩展物理机制较一致,采用该模型对SRV的评价计算更符合矿场实际.基于文中提出的计算方法,以威远页岩气开发示范区某压裂井段为例,验证了模型的可靠性,并计算分析了SRV随时间的动态演化过程及变化规律.该文研究对于提升页岩水平井分段分簇压裂SRV计算准确性,提高压裂优化设计以及压后效果评估具有重要的理论指导意义和矿场应用价值.     

考虑基岩向井筒供气的气藏压裂水平井产能预测模型

基于等效井径模型,综合考虑各裂缝不同参数、裂缝变质量入流和井筒变质量流动等因素的影响,建立并求解了基岩和压裂缝同时向井筒供气的井筒与气藏耦合的压裂水平井产能预测模型.研究分析了压裂水平井产能敏感因素.结果表明:压裂水平井的产能随裂缝条数、裂缝半长、裂缝间距的增加而增加;为获得较高的产能,压裂时应尽可能保持裂缝与井筒垂直;压裂缝条数一定时,裂缝越长、间距越大,压裂缝产能贡献越大,而裂缝越短、间距越小时,基岩向井筒供气的贡献越大,压裂缝贡献相对较小.研究成果为气藏压裂水平井设计提供了科学依据.     

Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology

Fuzzy analytic hierarchy process (AHP) proves to be a very useful methodology for multiple criteria decision-making in fuzzy environments, which has found substantial applications in recent years. The vast majority of the applications use a crisp point estimate method such as the extent analysis or the fuzzy preference programming (FPP) based nonlinear method for fuzzy AHP priority derivation. The extent analysis has been revealed to be invalid and the weights derived by this method do not represent the relative importance of decision criteria or alternatives. The FPP-based nonlinear priority method also turns out to be subject to significant drawbacks, one of which is that it may produce multiple, even conflict priority vectors for a fuzzy pairwise comparison matrix, leading to entirely different conclusions. To address these drawbacks and provide a valid yet practical priority method for fuzzy AHP, this paper proposes a logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithmic nonlinear programming and derives crisp priorities from fuzzy pairwise comparison matrices. Numerical examples are tested to show the advantages of the proposed methodology and its potential applications in fuzzy AHP decision-making.     

A linear programming priority method for a fuzzy transportation problem with non-linear constraints

Demand and supply pattern for most products varies during their life cycle in the markets. In this paper, the author presents a transportation problem with non-linear constraints in which supply and demand are symmetric trapezoidal fuzzy value. In order to reflect a more realistic pattern, the unit of transportation cost is assumed to be stochastic. Then, the non-linear constraints are linearized by adding auxiliary constraints. Finally, the optimal solution of the problem is found by solving the linear programming problem with fuzzy and crisp constraints and by applying fuzzy programming technique. A new method proposed to solve this problem, and is illustrated through numerical examples. Multi-objective goal programming methodology is applied to solve this problem. The results of this research were developed and used as one of the Decision Support System models in the Logistics Department of Kayson Co.     

一类模糊非线性方程组及解法

模糊非线性方程组 ,在模糊控制和现实生活中很普遍 .本文考虑一类模糊非线性方程组的性质 ,然后给出一种解法 .首先把模糊非线性方程组转变成非线性规划 ,再用非线性规划中的方法或软件来解 .     

A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment

In this study, a fuzzy multi-objective joint replenishment inventory model of deteriorating items is developed. The model maximizes the profit and return on inventory investment (ROII) under fuzzy demand and shortage cost constraint. We propose a novel inverse weight fuzzy non-linear programming (IWFNLP) to formulate the fuzzy model. A soft computing, differential evolution (DE) with/without migration operation, is proposed to solve the problem. The performances of the proposed fuzzy method and the conventional fuzzy additive goal programming (FAGP) are compared. We show that the solution derived from the IWFNLP method satisfies the decision maker’s desirable achievement level of the profit objective, ROII objective and shortage cost constraint goal under the desirable possible level of fuzzy demand. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component.     

基于多目标规划法的模糊线性回归分析

讨论输入、输出均为模糊数,回归系数为实数时的模糊线性回归分析。由于模糊最小二乘线性回归容易受异常值的影响,而最小一乘法能有效地降低回归模型的误差。为此,基于最小一乘法,建立多目标规划模型并将其转化为非线性规划问题进行求解,从而实现模糊线性回归模型的参数估计。最后,结合一个数值实例,验证和比较该方法的合理性和优越性。     

Improved logarithmic linearizing method for optimization problems with free-sign pure discrete signomial terms

Free-sign pure discrete signomial (FPDS) terms are vital to and are frequently observed in many nonlinear programming problems, such as geometric programming, generalized geometric programming, and mixed-integer non-linear programming problems. In this study, all variables in the FPDS term are discrete variables. Any improvement to techniques for linearizing FPDS term contributes significantly to the solving of nonlinear programming problems; therefore, relative techniques have continually been developed. This study develops an improved exact method to linearize a FPDS term into a set of linear programs with minimal logarithmic numbers of zero-one variables and constraints. This method is tighter than current methods. Various numerical experiments demonstrate that the proposed method is significantly more efficient than current methods, especially when the problem scale is large.     

Fuzzy goal programming for multiobjective transportation problems

Several fuzzy approaches can be considered for solving multiobjective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.     

Fuzzy Bayesian estimation on lifetime data

Summary  The Bayesian estimation on lifetime data under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayesian estimation method will be used to create the fuzzy Bayes point estimator by invoking the well-known theorem called “Resolution Identity” in fuzzy set theory. On the other hand, we also provide computational procedures to evaluate the membership degree of any given Bayes point estimate. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g., GAMS or LINDO.     

An interactive fuzzy satisficing method for random fuzzy multiobjective integer programming problems through probability maximization with possibility

This paper considers multiobjective integer programming problems where each coefficient of the objective functions is expressed by a random fuzzy variable. A new decision making model is proposed by incorporating the concept of probability maximization into a possibilistic programming model. For solving transformed deterministic problems, genetic algorithms with double strings for nonlinear integer programming problems are introduced. An interactive fuzzy satisficing method is presented for deriving a satisficing solution to a decision maker by updating the reference probability levels. An illustrative numerical example is provided to clarify the proposed method.     

Fuzzy Bayesian system reliability assessment based on exponential distribution

The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayesian estimation method will be used to create the fuzzy Bayes point estimator of system reliability based on Exponential distribution by invoking the well-known theorem called “Resolution Identity” in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g., GAMS or LINGO (LINDO).