Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.

On the direct searches for non-smooth stochastic optimization problems

Many difficult engineering problems cannot be solved by the conventional optimization techniques in practice. Direct searches that need no recourse to explicit derivatives are revived and become popular since the new century. In order to get a deep insight into this field, some notes on the direct searches for non-smooth optimization problems are made. The global convergence vs. local convergence and their influences on expected solutions for simulation-based stochastic optimization are pointed out. The sufficient and simple decrease criteria for step acceptance are analyzed, and why simple decrease is enough for globalization in direct searches is identified. The reason to introduce the positive spanning set and its usage in direct searches is explained. Other topics such as the generalization of direct searches to bound, linear and non-linear constraints are also briefly discussed.

Electrical Impedance Tomography Based on Direct Search Method

Solution to impedance distribution in electrical impedance tomography （EIT） is an ill-posed nonlinear inverse problem. It is especially difficult to reconstruct an EIT image in the center area of a measured object. Tikhonov regularization with some prior information is a sound regnlarization method for static electrical impedance tomography under the condition that some true impedance distribution information is known a priori. This paper presents a direct search method （DSM） as pretreatment of image reconstruction through which one not only can construct a regularization matrix which may locate in areas of impedance change, but also can obtain an initial impedance distribution more similar to the true impedance distribution, as well as better current modes which can better distinguish the initial distribution and the true distribution. Simulation results indicate that, by using DSM, resolution in the center area of the measured object can be improved significantly.

PRP-Type Direct Search Methods for Unconstrained Optimization

Three PRP-type direct search methods for unconstrained optimization are presented. The methods adopt three kinds of recently developed descent conjugate gradient methods and the idea of frame-based direct search method. Global convergence is shown for continuously differentiable functions. Data profile and performance profile are adopted to analyze the numerical experiments and the results show that the proposed methods are effective.

A DIRECT SEARCH FRAME-BASED ADAPTIVE BARZILAI-BORWEIN METHOD

This paper proposes a direct search frame-based adaptive Barzilai-Borwein method for unconstrained minimization. The method is based on the framework of frame-based algorithms proposed by Coope and Price, but we use the strategy of ABB method and the rotational minimal positive basis to reduce the computation work at each iteration. Under some mild assumptions, the convergence of this approach will be established. Through five hundreds and twenty numerical tests using the CUTEr test problem library, we show that the proposed method is promising.

A New Direct Search Method Based on Separable Fractional Interpolation Model

In this paper, we propose a new separable fractional interpolation model which can be established by 2n interpolation points where n is the number of variables. Based on this model, a new direct search method is presented. In this method, a new iterate is determined by solving the fractional interpolation model in trust region. Under mild assumptions, the convergence results of this method are given and proved, Numerical experiments show that the new method is promising.

Differential evolution with controlled search direction

A novel and simple technique to control the search direction of the differential mutation was proposed.In order to verify the performance of this method,ten widely used benchmark functions were chosen and the results were compared with the original differential evolution(DE)algorithm.Experimental results indicate that the search direction controlled DE algorithm obtains better results than the original DE algorithm in term of the solution quality and convergence rate.

Watermark Embedding for Direct Binary Searched Halftone Images by Adopting Visual Cryptography

In this paper,two methods are proposed to embed visual watermark into direct binary search(DBS)halftone images,which are called Adjusted Direct Binary Search(ADBS)and Dual Adjusted Direct Binary Search(DADBS).DADBS is an improved version of ADBS.By using the proposed methods,the visual watermark will be embedded into two halftone images separately,thus,the watermark can be revealed when these two halftone images are overlaid.Experimental results show that both methods can achieve excellent image visual quality and decoded visual patterns.

The Forming Conditions of and Ore-searching Direction for B, Mg,Pb, Zn and Au Deposits in the Liaoji Proterozoic Rift

The Liaoji Proterozoic rift is an inter-intracontinenatl rift developed from Archean granite-greenstone tectonic regime and contains many important mineral deposits of U, B, magnesite, Pb-Zn, Au, Ag, Co and P. These deposits were formed as the result of late mobilization, transportation and concentfation of the previously enriched ore-forming mate- rials in several ore-bearing formations formed during the rift stage. So the metallogeny of these deposits in the rift shows both inheritance and new generation of the ore-forming materials. In future ore-searching practice, attentions should be paid on the studies of the ore-bearing formations in the rift, on the multiple stages of metallogeny and and on multiple derivations of the ore-forming materials.

Minimum Distortion Direction Prediction-based Fast Half-pixel Motion Vector Search Algorithm

A minimum distortion direction prediction-based novel fast half-pixel motion vector search algorithm is proposed, which can reduce considerably the computation load of half-pixel search. Based on the single valley characteristic of half-pixel error matching function inside search grid, the minimum distortion direction is predicted with the help of comparative results of sum of absolute difference(SAD) values of four integer-pixel points around integer-pixel motion vector. The experimental results reveal that, to all kinds of video sequences, the proposed algorithm can obtain almost the same video quality as that of the half-pixel full search algorithm with a decrease of computation cost by more than 66%.

三维环境中机器人路径规划算法改进

为解决快速扩展随机树算法(rapid-exploration random tree,RRT*)在三维环境中盲目搜索路径以及缺乏节点扩展记忆性等问题,提出一种融合蚁群算法的双向搜索算法ACO-RRT*。为适应精细化三维建模环境和解决地面起伏不平坦等问题,对RRT*算法进行改进优化。采用双向搜索策略,在起点和终点同时运行改进后的RRT算法和蚁群算法,相向而行,对路径长度和运行时间进行优化。针对生成路径不够平滑等问题,引入B样条曲线平滑策略优化路径。仿真结果表明,所提算法能够有效用于机器人三维路径规划。

基于PMADS与共轭优化法的单元上下料口布局与AGV配置联合优化

在具有有限能力物料储运系统的单元流水式车间中,针对单元上料与下料(P/D)口位置布局与物料搬运AGV数量配置的联合优化问题,建立了以最小化平均运输总成本和AGV配置成本为目标的单元P/D口布局与AGV配置联合优化模型。由于P/D口布局与AGV配置具有不同的优化特点,为了提高算法效率和结果的质量,提出一种嵌入共轭优化法和粒子群优化的网格自适应直接搜索算法(PMADS),在优化过程中分别对新解的P/D口布局及对应的AGV配置进行再优化。将所提算法与其他对比算法应用于某精密制造企业新工厂规划项目,结果显示PMADS算法在性能和求解质量上均优于其他算法,有效解决了单元流水式车间单元P/D口布局与AGV配置的联合优化问题。结果验证了所提算法在求解车间单元P/D口布局与AGV配置问题上的有效性、高效性及实用价值,所提共轭优化法加强了算法搜索的方向性,提高了算法效率和解的质量。

一种基于地图预处理识别凹点优化路径规划效率的方法

为了减少存在多个无法通行或无法到达终点的路径段情况下移动机器人路径的计算节点数量,提高算法的搜索效率,文中提出一种地图数据预处理功能,对路径规划算法中的预估计算法进行辅助,通过障碍物权重计算需要地图中存在的凹点地形,在执行路径算法过程中,通过记录的凹点约束搜索方向提高搜索效率,同时对路径规划算法的安全问题进行改进。实验结果表明,改进后的路径规划算法具有较好的路径规划能力来应对多个无法通行或无法到达终点的路径段,更加符合实际的应用场景。

有向图上基于层次树索引的最大cycle truss社区搜索

社区搜索旨在从信息网络中找出包含用户查询顶点的高内聚连通子图,cycle truss是一种基于cycle三角形的社区搜索模型,而现有的基于索引的cycle truss社区搜索方法存在索引空间大、搜索效率低、社区内聚性低的缺点。为了解决这一问题,提出一种基于层次树索引的最大cycle truss社区搜索方法。首先,提出了k-cycle truss分解算法,并引入了两个重要的概念:cycle三角连通与k-层次等价。基于k-层次等价设计了层次树索引TreeCIndex与表结构索引SuperTable,在此基础上,并基于这两个新的索引,提出了两个高效的cycle truss社区搜索算法。在4个真实数据集上与已有的基于TrussIndex与EquiTruss的社区搜索算法进行了比较,实验结果表明,TreeCIndex与SuperTable比TrussIndex与EquiTruss节省至少41.5%的空间,索引构建的时间节省8.2%至98.3%,且搜索最大cycle truss社区的效率分别高出了一个和两个数量级。

基于专利跟踪分析国外激光武器发展情况

针对国外激光武器发展应用情况,在全球专利数据库范围内开展美国、德国、英国、以色列等国外专利检索,获取了2017年以后公开的435项专利;基于专利技术构成分析了解到国外从提升激光器功率、合束效率、光束指向精度与速度等方面,突破高能激光武器作战距离、机动目标跟踪和持续作战能力;从专利区域分布分析了解到,美国的技术创新占国外申请总量的61%,且有54.7%的专利受到了政府资助;针对国外重点项目或政府资助专利进行方案剖析,深度挖掘了核潜艇激光武器和炫目激光武器的技术改进方向。通过国外激光武器专利的跟踪与分析,梳理国外技术发展动向,为我国技术发展与风险防御提供信息支撑。

基于改进粒子群算法的柔性制造系统无死锁优化调度

柔性制造系统的优化调度问题是一个复杂的组合优化和NP-hard问题。以赋时Petri网为模型、最小化最大完工时间为优化目标,利用改进粒子群算法对一类柔性制造类系统建立了一种新的无死锁优化调度方法。该方法首先采用2层编码方式对路径和工序进行编码,建立工序与粒子位置之间的一一映射关系;其次,基于实时在线的死锁避免策略对粒子进行死锁检测与修复,保证所搜索的粒子均能解码为无死锁的可行调度序列;然后,设计了2种改进策略:粒子工序定向调整策略和局部搜索策略,以提高算法的寻优效率和局部搜索能力,保证快速得到最优或次优的可行序列;最后,利用2个仿真实验验证所提算法的有效性。实验结果表明:与其他已有算法相比,改进粒子群算法在求解柔性制造系统无死锁优化调度问题上具有较好的寻优能力。

基于均匀圆阵矢量传感器的DOA和极化参数联合估计

针对极化敏感阵列中非完备电磁矢量传感器多参数联合估计问题,提出一种基于三维电磁矢量传感器平面圆阵模型的波达方向(DOA)和极化参数联合估计方法。利用阵列流型矩阵的特性将四维谱函数进行解耦,严格证明DOA搜索与极化参数估计的不相关性,将四维谱搜索优化为仅与DOA相关的二维谱搜索,提出DOA谱峰搜索二级比较策略,可使雷达自动识别谱峰坐标,有效提高信号处理的实时性;提出一种基于精英反向学习和Lévy飞行的改进粒子群算法对极化参数进行估计,提高极化参数的收敛性能。通过与同类算法进行仿真对比,结果表明:所提算法在牺牲少量时间的条件下可提高估计精度。

基于边缘梯度搜索的激光定向能量沉积熔池尺寸提取方法

针对激光定向能量沉积过程中存在熔池弱边缘导致熔池宽度难以准确提取的问题,本工作基于熔池边缘灰度梯度变化差异,提出了一种基于边缘梯度搜索模型的熔池宽度提取方法。为了得到精确的熔池尺寸,首先基于数学形态学的方法对熔池图像进行预处理得到粗定位边缘,然后通过试验确定弱边缘梯度阈值,最后采用边缘梯度搜索模型对粗定位边缘进一步分割得到精分割边缘,使用椭圆拟合法进行边缘拟合得到熔池宽度,试验结果表明该方法具有良好的熔池宽度检测精度。

A Filter-Based Pattern Search Method for Unconstrained Optimization

We discuss a filter-based pattern search method for unconstrained optimization in this paper. For the purpose to broaden the search range we use both filter technique and frames, which are fragments of grids, to provide a new criterion of iterate acceptance. The convergence can be ensured under some conditions. The numerical result shows that this method is practical and efficient.

Status and Perspectives of Dark Matter Searches in China

In this paper,we give a review of direct detection searches reported by the DAMA(China as a Co-Ⅰ),and indirect detection searches reported by the ATIC(China as a Co-Ⅰ).We also outline the future plans of dark matter search in China.