风管式气举反循环钻具及其在大口径钻井施工中的应用

目前大口径钻井施工中常用的泥浆正循环钻进存在着泥浆流速慢、携渣能力差、重复破碎严重、钻井效率低、钻头磨损快、能源消耗大、钻井事故发生率高等问题,气举反循环钻进工艺可有效解决上述问题。风管式气举反循环钻井工艺简单易实现,能有效减少重复破碎,钻进效率高,能源消耗少,钻头寿命长,成井质量好,对涌水和漏失均有很好的抑制作用。介绍了风管式气举反循环钻井工艺原理、专用钻具及其现场使用情况。     

虚拟太阳天文台及其发展

伴随着太阳物理研究的深入、观测仪器的发展、观测数据的积累,促使人们去思考这样一个问题:如何使研究者能够方便地检索、分析和使用在不同时间、不同地点、不同波段上观测得到的大量与太阳有关的数据,从而能探索更多悬而未决的科学谜题。这就是虚拟太阳天文台(VSO)项目被提出后得到有关天文台、研究所和大学积极响应并迅速投入运作的原因所在。介绍了虚拟太阳天文台的由来、作用、采用的技术和发展状况等。     

新型堵漏工具——拦截式堵漏工具的研究

针对大溶洞或裂缝漏失,利用研究的拦截式堵漏工具将堵漏浆拦截在一定位置,对大裂缝漏失地层、溶洞性漏失地层进行有效封堵,从而解决该类恶性漏失问题,提高钻探施工质量和钻探效率,降低钻探成本,为钻探工程特种漏失堵漏提供新途径。     

高光谱图像波段选择的改进二进制布谷鸟算法

波段选择是高光谱遥感图像分类的重要前提,本文提出了一种用于高光谱遥感图像波段选择的改进二进制布谷鸟算法,通过使用混合二进制编码算法更新子代鸟巢和使用遗传算法交叉方式更新被发现鸟巢两个方面对二进制布谷鸟算法进行改进,找出在图像中起主要作用且相关性低的波段,实现对高光谱遥感图像降维。将本文算法运用于PaviaU数据集和AVIRIS数据集,并与二进制布谷鸟算法、二进制粒子群算法、最小冗余最大相关算法、Relief算法等进行对比分析。结果表明,改进二进制布谷鸟算法波段特征选择效率更高,且选取的波段更具代表性,能够较好地提高后续分类精度。     

多极子声波测井资料在辽河油田硬地层中的应用

通过声波全波列测井中斯通利波的反射和差异能量分析,在辽河油田中进行了裂缝识别并评价了其有效性。利用由4个波列旋转得到的交叉能量成分可以描述碳酸盐岩中的有效裂缝。通过分析计算各向异性的最小和最大能量,并通过不同井的实际产量已证实这些计算结果的一致性,从而对有效裂缝进行了描述。用多极子声波测井可以提高地震资料解释的精度,确定岩性与岩石特征,并探测气层与裂缝。编制出适合辽河油田使用的岩石力学性质参数图并应用在生产中。     

大庆朝阳沟油田浅层水平井钻井技术

介绍朝阳沟油田98区浅层水平井的井身结构特点、地层岩性特点以及该区水平井轨迹控制的难点。针对这些特点以及不同地层的造斜率,制定有效轨迹控制方案,选取合理的造斜工具及钻具组合,实施合理的钻井参数及技术措施,完成浅层水平井的施工。     

近相接双星室女座BF的轨道周期变化

收集了近相接双星ＢＦ Ｖｉｒ的光极小时刻,并对其轨道周期进行分析。结果表明该星的轨道周期在长期减小的财时也含有３０年的周期性变化,对引起轨道周期变化的各种物理机制的分析研究表明：一个质量为０．２８１Ｍ⊙、绕公共质心的轨道半径为１０．２ＵＡ的第３天体的存在能对轨道周期的周期性变化成份作出解释,如果轨道周期的周期性变化成份是由于次子星的周期性磁活动引起,那么系统总光度就应该有振幅０．０９７Ｌ⊙的周期性     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

基于 ArcG IS 和 Python 集成开发可达性分析工具的研究

为降低可达性度量的实现难度,弥补现有GIS软件没有集成可达性分析模型或方法的不足,在详尽分析有关模型或方法与GIS集成研究现状的基础上,提出以改进潜能模型作度量模型,按GIS内部集成方式,使用面向ArcGIS 10的Python脚本编程技术集成开发可达性分析工具的设想,对可达性潜能模型分析原理、工具开发环境配置、使用数据的预处理要求、工具的具体设计流程和实现方法等作了详细叙述。最后以评价某市小学教育资源空间布局合理性为例,阐述工具在实际应用中应采取的使用方法和流程,实例评价结果验证工具开发的有效性。     

高维遥感图像的快速分类算法

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。