Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization   总被引：2，自引：0，他引：2  

Satomi Suzuki  Guillaume Caumon  Jef Caers 《Computational Geosciences》2008,12(1):105-119

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.  相似文献   

基于免疫粒子群优化算法的影像纹理分类     

李林宜  李德仁 《测绘学报》2008,37(2):0-249

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。  相似文献   

基于改进PSO-SVM的噪声稳健建筑物变形监测方法     

张建奇 《北京测绘》2021,35(2):166-171

对高层、超高层建筑物进行实时,高精度的变形监测对提前预防安全隐患,保证人民生命财产安全具有重要意义。建筑物变形作为一种典型的随机性和微弱性过程,噪声等误差的存在会影响从中提取有用的变形信息。针对该问题,提出一种改进粒子群(Particle Swarm Optimization,PSO)算法优化支持向量机(Support Vector Machine,SVM)的噪声稳健建筑物变形监测方法,利用改进PSO算法的全局搜索能力对SVM的核参数进行优化,提升预测精度的同时增强算法的噪声稳健性。基于实测数据的试验结果表明,相对于传统交叉验证SVM和小波方法,所提方法可以获得更高的变形预测精度,并且在低信噪比条件下优势更加明显。  相似文献   

长沙某高边坡支护设计优化          下载免费PDF全文

李仲秋  隆威 《探矿工程》2006,33(10):35-37

结合长沙某高边坡支护工程实例，详细研究边坡岩土条件及环境条件，充分挖掘岩土参数潜力：≯耋翌睾；方綦建藉蓑术、经济对比分析，优选锚杆网格梁法进行边坡加固支护。实践表明，锚杆网格梁法是一种加固高边坡性价比很好的方法。  相似文献   

Optimization of nonconventional wells under uncertainty using statistical proxies     

Vincent Artus  Louis J. Durlofsky  Jérôme Onwunalu  Khalid Aziz 《Computational Geosciences》2006,10(4):389-404

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.  相似文献   

Optimization of SAGD Well Elevation     

Jason A. McLennan  Weishan Ren  Oy Leuangthong  C. V. Deutsch 《Natural Resources Research》2006,15(2):119-127

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.  相似文献   

数据备份服务器备份性能分析     

任晖  肖广建 《测绘学院学报》2005,22(1):76-78

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。  相似文献   

C1相干算法及其在三维地震资料处理解释中的应用     

丛茂祥  李洪亮 《中国煤田地质》2005,17(2):49-50,70

C1相干算法是计算每道的横测线和相邻纵测线的互相关，依据相干值来判断地层和岩性的横向不均匀性。根据互相关原理，提出了相干体算法中二维和三维C1算法的内容及其具体实现。通过对实际三维地震资料的处理，结合已知地质资料，证明C1相干技术在三维地震资料的断层解释方面有着明显的效果。  相似文献   

一种基于InSAR相干系数的SAR阴影提取方法   总被引：1，自引：0，他引：1  

王健  向茂生  李绍恩 《武汉大学学报(信息科学版)》2005,30(12):1063-1066

给出了一种新的阴影提取方法,根据阴影区域在InSAR相干系数图上的特性,利用Sigma滤波和最优阈值分割算法实现阴影的自动提取。实验结果表明,本方法是一种高效的阴影提取方法。  相似文献   

Combination of Dependent Realizations Within the Gradual Deformation Method     

Lin Y. Hu 《Mathematical Geology》2002,34(8):953-963

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.  相似文献