速度分析和层析成像

地震波速度是地震勘探中最重要的参数之一。通过对第 6 5届EAEG年会上发表的 2 0多篇相关文章的归纳和总结 ,介绍了求取地震波速度的若干方法 ,如 :沿层速度分析、层析成像速度分析、波动方程速度分析及应用地质统计法对速度分析作质量控制等。层析成像是一种重要的反演方法 ,它能提供精确的旅行时和宏观速度模型 ,为复杂构造的成像创造了有利条件 ,但在应用层析成像方法时仍有许多问题有待解决 ,如 :数据拾取和计算效率问题。介绍了解决这些问题的一些方法。     

地质统计学在环境科学领域的应用进展

介绍了地质统计学的产生与发展现状,回顾了其在土壤环境、水环境等环境科学领域应用的一些成果。 这些成果表明,地质统计学方法是污染物空间分布状况精确估值的有效方法。在此基础上,就地质统计学在环 境科学领域的应用前景进行了展望。     

河流相砂岩油藏综合地质评价建模

以冀东油田庙北地区馆陶组油藏地质模型、油藏定量评价模型的建立为例,较有针对性地阐述了在河流相沉积的复杂断块区,以三维测井约束反演为基础,以产能建设为目标,按照模式约束、钻井控制、地震导向外推的技术思路,应用地质统计学技术,充分结合地质、测井、测试等信息,定量地研究储层岩性、物性和含油性。同时,在三维空间内,把构造、储层和油气水特征进行了有效地叠合,对油藏位置和特征进行精细描述,并建立起定量反映油气田特征的三维可视化模型。经实践检验,可大大提高建模精度,从而为高速、高效地开发油田,动用其地质储量提供了可靠的地质依据,为油藏数值模拟和开发方案的编制提供了地质模型,并起预测作用。     

基于区域地层压力描述的东坪区块井身结构优化

东坪区块是青海油田近年来开发的重点区块,由于地质条件复杂和前期对该区块地层压力认识不清,因此井身结构设计存在缺陷,钻井过程中频繁出现漏、喷、塌、卡、断等各种井下复杂情况和事故。针对目前东坪区块地层压力认识不清的问题,首先收集区块内已钻井的测井等资料,利用Eaton法建立已钻井的单井地层压力剖面;然后基于地质统计学和克里金插值法,将地层压力以地层层组为单元外延展开,从而实现地层压力的区域化描述;最后根据区域地层压力的预测结果,对区块内待钻井进行井身结构优化。研究结果为青海油田东坪区块井身结构优化及钻井液密度精细化设计提供了依据,可以有效降低钻井复杂事故的发生频率,对于安全钻井具有重要意义。     

基于地质统计学的资源储量估算参数优选研究

借助DIMINE矿业软件,综合运用普通克里格法和距离幂次反比法建立了某矿床钻孔数据库、矿体模型和品位块段模型,采用空间多扇区逐步搜索法获得组合样Au品位的实验变异函数,选择球状模拟进行拟合获取理论变异函数参数。针对不同储量估算方法、椭球体搜索方式、搜索距离和有无次级分块等条件,采用正交试验方法,优选6种资源储量估算方案进行估值,获得不同方案的Au品级-矿石量和Au品级-Au品位分布曲线。结果表明:采用各向异性距离能反映品位分布的各向异性;有次级分块估算的矿石量更准确;根据变程进行椭球体分次搜索可有效避免品位均化。研究成果可为基于地质统计学的矿床资源储量估算方法提供指导。     

古岩溶系统训练图像制作

训练图像的制作是多点地质统计学方法用于储层建模的必要步骤。目前,古岩溶系统建模研究中训练图像的制作方法相对较为缺乏。为此,提出了一种以GoogleEarth影像数据为基础的古岩溶系统训练图像制作方法。综合GoogleEarth影像和野外露头资料对比研究,可得到溶洞、溶蚀沟道分布位置及大小,溶蚀洼地分布、断裂—裂缝系统的位置及规模等,将采集到的定量化GoogleEarth影像原始数据,用相关算法分析处理;其中,趋势面分析得到地貌面,椭球体方程得到溶洞特征,从而可得到用于多点地质统计学建模的古岩溶系统训练图像。     

区域浅层地下水硝酸盐含量评价的指示克立格法

在进行区域水土资源特性的分析中经常会发现一些特异值，用普通克立格法来求解，通常是不稳健的。为此，本文引入了地统计学方法中的指示克立格法，详细介绍了其基本原理和计算方法。通过对华北冲击平原区曲周县667km2的139个点的浅层地下水井取样分析，发现硝酸盐含量中存在有特异值，应用指示克立格法对其进行了分析，并绘制了硝酸盐含量的分布图及其含量超过最大允许值的概率图。这为区域水土资源利用的风险性评价提供了一种新的方法，同时也为区域水土资源质量的决策管理提供了指导。     

Additional sampling based on regulation threshold and kriging variance to reduce the probability of false delineation in a contaminated site

Kriging-based delineation when used to determine a cost-effective remediation plan should be based on the spatial distribution of the pollutant. This study proposed an adaptive cluster sampling (ACS) approach based on the regulation threshold and kriging variance for additional sampling to improve the reliability of delineating a heavy-metal contaminated site. A reliability index for reducing the probability of false delineation was used to determine the size and configuration of additional samples. A data set of Ni concentrations in soil was used for illustration. The results showed that the additional sampled observations during ACS were clustered where the Ni concentrations were close to the regulation threshold of 200 mg kg(-1), and were located where the first-phased sampling density was low. Compared with a simple random sampling (SRS), the relative frequency of misclassification over the whole study area (RFMW) using ACS in a 100 replicates simulation was lower when the same sample number of pooled data was used. In addition, the spatial distribution of the local misclassification rate (LMR) showed that the area with a high-valued LMR could be reduced and that the LMR gradients in the region could be lowered by using ACS instead of SRS. The above results suggest that the proposed ACS approach could improve the reliability of kriging-based delineation of heavy-metal contaminated soils.     

变异函数在大厂锡矿田92号矿体中的应用

变异函数是地质统计学的核心内容和基本工具,它能够较好地研究一个矿床的区域化变量的基本特征,特别是能够反映变量的随机性和结构性的变化。本文在对大厂某矿体地质特征进行研究后,计算了该矿体的变异函数,并且对计算结果进行了分析。结果表明,利用变异函数建立矿床数学模型进行矿体储量估算是一种切合实际的方法。     

On the challenge of using sequential indicator simulation for the estimation of recoverable reserves

Despite its flexibility when compared with parametric methods, there are significant problems with sequential indicator simulation (SIS) for continuous variables. These come from the successive construction of the local conditional distributions by indicator kriging and the use of Monte–Carlo simulation for sampling the simulated values from such distributions. Some problems of SIS have been addressed satisfactorily; order relation deviations and the extrapolation of the distribution tails are manageable. The most important outstanding challenges are: (1) there is no resolution within a grade range, and (2) there is no correlation between different grade ranges. These problems lead to an over-smoothing of grades and a reduced, but misleading, uncertainty when point scale simulations are average up at the selective mining unit size. Attempting to account for the interclass correlation by cokriging fails because there is no adequate model for fitting the complete matrix of indicator variograms. Also, attempting to improve the within class resolution by using the class edge distance function does not provide a complete solution. In regard of these unresolved challenges, the practitioner is cautioned about the use of SIS of continuous variables for Resource Evaluation. Instead, a combination of categorical variables simulation methods (including SIS) for modeling the mineralisation domains, and Gaussian simulation of grades within these domains is recommended.