一类全局最优化问题的动态隧道方法

研究了连续变量函数的全局最优化问题 ,给出了动态隧道方法。该动态隧道方法由局部搜索和动态隧道 2个阶段构成。在局部搜索阶段用了动态系统方法。对全局最优化问题的实例进行了数值实验 ,数值结果表明了该方法的稳健性和有效性。     

基于蚁群智能的遥感影像分类新方法

智能式遥感分类是遥感研究的新热点.提出了一种基于蚁群智能规则挖掘(ant-miner)的遥感影像分类新方法.遥感数据各波段之间存在较强的相关性,这种相关性往往会导致分类产生误差.而ant-miner算法中的信息素是基于规则整体性能的,信息素的动态更新能有效地处理相关性较强的数据,所提供的正反馈信息能纠正启发式函数缺陷所造成的错误.因此,蚁群智能算法应用于遥感分类具有一定的优势.将该方法用于广州市地区的遥感影像,取得了较好的分类结果.并与See5.0决策树方法及最大似然方法(MLH)进行了对比研究,实验结果表明,蚁群智能算法分类精度比后两者的分类精度更高.     

测量控制网优化设计的可靠性准则法

测量控制网优化设计与网的精度、可靠性、灵敏度以及费用等准则有关,但这些准则之间的关系又十分密切,本文提出了一种基于观测值内部可靠性指标的测量控制网模拟法优化设计新方法,介绍了其原理和特点,并用实例说明了用该方法的计算步骤和优化效益。     

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.     

基于免疫粒子群优化算法的影像纹理分类

粒子群优化算法是基于群智能的随机全局优化方法,它源于对鸟群简化社会系统的模拟。为了提高标准粒子群优化算法的收敛性能,将生物免疫系统的记忆能力和多样性引入标准粒子群优化算法,提出一种免疫粒子群优化算法。在提取纹理样本Laws纹理能量模板特征、小波特征等纹理特征的基础上,提出针对分类问题的粒子表达方法和群体寻优策略,实现了基于免疫粒子群算法的纹理分类。实验结果表明,与标准粒子群优化算法相比,免疫粒子群优化算法在获取训练样本类别中心时具有较好的收敛性能,并且基于该算法的影像纹理分类具有较高的分类精度。     

Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.     

Optimization of SAGD Well Elevation

The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations. A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB) and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort McMurray, Alberta.     

数据备份服务器备份性能分析

针对数据备份服务器备份性能优化问题，分别对实时数据存储与历史数据备份之间的关系、数据备份服务器所备份的数据类型非唯一性、客户数量对备份服务器备份效率的影响三方面进行初步的测试与分析，提出了通过优化数据块的选取、优化数据采集方式、优化备份策略提高备份服务器备份效率的基本方法。     

Combination of Dependent Realizations Within the Gradual Deformation Method

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.     

Melting of β-quartz up to 2.0 GPa and thermodynamic optimization of the silica liquidus up to 6.0 GPa

Melting relations of β-quartz were experimentally determined at 1.0 GPa (1900±20 °C), 1.5 GPa (2033±20 °C), and 2.0 GPa (2145±20 °C) using a new high-pressure assembly in a piston–cylinder apparatus and substantial differences were found with data previously reported. The new melting data of β-quartz were combined and optimized with all available thermodynamic, volumetric, and phase equilibria data for β-cristobalite, β-quartz and coesite to produce a P–T liquidus diagram for silica valid up to 6.0 GPa. Using the new optimized thermodynamic parameters, the invariant point β-cristobalite+β-quartz+liquid and β-quartz+coesite+liquid were determined to lie at 1687±17 °C and 0.457 GPa, and 2425±25 °C and 5.00 GPa, respectively.