平原地区水文地质结构条件模拟及其应用:以华北平原为例

为了分析储水介质非均质性问题,采用了基于马尔柯夫地层序列分析的分布条件模拟方法,用转移概率矩阵的谱分析函数替代传统地质统计学中的变差函数,利用协同指示克里格方法和条件模拟建立结构模型。以华北平原为例,论述了模拟结构建立过程及其在水流模型中的应用。通过对模拟结果与实测数据的分析比较,认为模拟结果能够客观反映整个研究区的沉积规律,以岩性赋水文地质参数的方法更加客观准确地解决储水介质非均质性问题。分析了该方法的优、缺点,并提出了该方法深入研究的方向与思路。     

油气微渗漏扩散物理实验的再现及数值模拟

来自油气藏的烃类物质的微渗漏扩散是普遍存在的。这里主要进行油气微渗漏扩散,在计算机上的物理实验再现过程和三维数值模拟。首先把实验提供的稀疏的数据,通过Kriging插值算法计算,得到三维重建中所需具有规则、分布均匀特点的数据,所得效果较好,并且算法编写简单、容易理解。其次,建立一种关于油气微渗漏扩散的三维数学模型,进行数值模拟,较好地仿真了演示油气微渗漏随时间扩散的过程。最后,简单地探讨了虚拟现实建模语言（VRML）在三维可视化中的运用。     

条件模拟原理和技术进展及在水资源系统中的应用

在简要介绍条件模拟基本原理基础上,重点评述了国外20世纪90年代新的数学方法和计算技术进展及在国内外水资源系统应用的主要成果,并对今后学科和技术发展作了展望.     

岩爆及采矿诱发岩体失稳破坏过程数值模拟新方法

岩石介质的宏观非线性主要是由非均质性和各向异性造成的,应用新的数值计算软件RFPA(2D)——基于岩石宏观非线性行为可能是由具弹脆性特征的细观微元体不断破裂造成的,造成微元体不断破裂(并非某一时刻同时破裂)的原因是微元体材料性质(微元体强度、弹性模量和泊松比等参数)的非均匀性,是一个数学上相对简单但能充分考虑岩石介质复杂性的方法,对采动引起岩体失稳破坏的全过程进行了数值模拟研究,表明应用RFPA(2D)是切实可行的。      

超稠油油藏转小井距蒸汽驱条件及时机

蒸汽驱是稠油油藏经过蒸汽吞吐开采后进一步提高原油采收率的主要热采阶段。但不是说任何时候转驱都可取得较好的效果。笔者系统研究了影响蒸汽驱开采效果的因素:原油粘度、油层有效厚度、纯总厚度比、起始含油饱和度和油层非均质性等,确定出适宜蒸汽驱开采的油藏条件。在分析稠油油藏加密吞吐后油藏条件变化的基础上,利用油藏工程数值模拟技术研究确定出超稠油油藏转小井距蒸汽驱的合理时机。     

石油储层地球化学的原理及应用

储层地球化学是一门新兴的地学边缘学科，近几年，它已成为评价储层含油特征的重要手段，其核心内容是研究储层流体的非均质性。储层中石油组成的变化除由生物降解和水洗引起外，油源岩相和成熟度的变化同样能造成石油组成的非均质性。识别储层流体的非均质性不仅是重建油气聚集史的基础，而且还能为石油开发提供重要信息。这样能降低勘探目标的风险和油田开发费用。储层流体特征的旧纳依赖于油气、岩心抽提物的地化分析，其研究成果在勘探早期到油田生产结束过程中都能得到应用。     

采动诱发岩体移动破坏过程数值模拟研究

本文基于岩石介质的宏观非线性主要是由非均质性和各向异性造成的, 应用新的数值计算软件RFPA(2D), 对采动引起岩体失稳破坏的全过程进了数值模拟研究。     

体系域内储层物性非均质性的定量表征

采用秩相关系数分析等方法，在层序地层等时框架内，定量研究体系域内砂、砾岩储层的纵向变化特征，建立体系域内5种储层非均质定量模式．由于沉积环境，沉积物供给，所经历的构造、成岩演化等方面的差异，储层在空间上表现出不同程度的非均质性．工区内沙3段自下而上划分为5个层序，其中，1层序中高水位体系域，主要发育弱非均质性储层，Ⅴ、Ⅱ层序中水进体系域，主要发育中等非均质储层，其余各层序体系域储层主要具强非均质性．总的来说，河流作用相对较强的高水位体系域，储层较水进体系域，储层的物性非均质性要强，最大洪泛面也是物性、非均质性转换面。     

PNN及其在储层物性参数预测中的应用

概率神经网络(PNN)实际上是一种数学插值手段，它的实现引用了神经网络的结构但它通过研究数学规则，可以比多层前馈神经网络更好地实现插值功能，因而具有潜在的优势，这里简要介绍了PNN的基本原理并用于储层物性参数的预测。实践表明该方法是有效的。     

沉积物粒径趋势分析:原理与应用条件

长期以来,沉积学家尝试用粒度数据来识别沉积环境的类型或判定物质运动的方式,但只取得了部分成功。粒度参数还有一项可能的用途,即用其平面差异来获取物质输运信息。沉积学家将粒度参数的平面差异定义为“粒径趋势”,并建立了以“粒径趋势分析”为基础的定性物质输运模型。粒径趋势分析的基本科学问题包括:如何提取粒径趋势信息;如何确定含有物质输运信息的粒径趋势类型;粒径趋势分析的应用条件是什么。粒径趋势信息可通过粒径趋势矢量的定义及其各向异性显著性的检验而获得,而经验证据也显示,含有物质输运信息的粒径趋势类型是存在的。初步研究表明,粒径趋势分析应满足以下条件:粒径趋势矢量具有显著性;底质采样深度应代表同一时间尺度;平面采样间距应符合地统计法的规则;避免使用位于采样网格边缘上的采样点的粒径趋势矢量;沉积物样品应属于同一个输运体系或研究区处于堆积状态。但是,由于不同来源的物质混合、源区沉积物特征及变化、不同水动力条件下的物质输运、悬沙沉降、溶解态—颗粒态物质转换、物质输运动力的侧向分布等因素与粒径趋势形成之间的关系还不够明确,因此这些条件还不是完备的,这个问题的解决依赖于粒径趋势形成的过程和机制的进一步研究。     

HHT法的基本原理及其应用探讨

以Timoshenko单跨梁为研究对象,采用回转射线矩阵法进行动力响应计算,针对Timoshenko梁的瞬态响应信号为非平稳信号,采用基于Hilbert—Huang变换原理的信号时频分析,并与FFT的处理结果进行对比分析。结果表明,HHT方法比Frr法更适合非平稳信号的分析和处理,同时,HHT方法不仅可以将瞬态信号中低频和高频部分分离出来,还可以通过Hilbert谱展示信号能量在时频域内的分布,更为深入的了解信号的本质。     

Numerical Method for Conditional Simulation of Levy Random Fields

Stochastic simulations of subsurface heterogeneity require accurate statistical models for spatial fluctuations. Incremental values in subsurface properties were shown previously to be approximated accurately by Levy distributions in the center and in the start of the tails of the distribution. New simulation methods utilizing these observations have been developed. Multivariate Levy distributions are used to model the multipoint joint probability density. Explicit bounds on the simulated variables prevent nonphysical extreme values and introduce a cutoff in the tails of the distribution of increments. Long-range spatial dependence is introduced through off-diagonal terms in the Levy association matrix, which is decomposed to yield a maximum likelihood type estimate at unobserved locations. This procedure reduces to a known interpolation formula developed for Gaussian fractal fields in the situation of two control points. The conditional density is not univariate Levy and is not available in closed form, but can be constructed numerically. Sequential simulation algorithms utilizing the numerically constructed conditional density successfully reproduce the desired statistical properties in simulations.     

有限元数值模拟法基本原理及其在地质构造变形研究中的应用综述

在构造变形研究中,传统的构造解析法存在一定局限性,而数值模拟方法可模拟时空变化,成为地球科学研究的重要手段。有限元法是一种模拟线性、变形问题较为高效的数值模拟法。本文详细阐述了有限元法的基本原理,并从地质模型、力学模型、计算模型三个方面解释了数值模型的建立过程,以及模型建立所需要的边界条件的确定方法,重点总结了近些年来有限元法在国内外构造变形研究中的应用和发展状况,涉及盆地、造山带、断层、俯冲带以及褶皱数值模拟等几方面内容。同时,分析了有限元数值模拟法在我国大陆构造变形中的应用进展。最后,讨论了有限元方法尚存在的问题及其发展趋势。     

Conditional Simulation with Patterns

An entirely new approach to stochastic simulation is proposed through the direct simulation of patterns. Unlike pixel-based (single grid cells) or object-based stochastic simulation, pattern-based simulation simulates by pasting patterns directly onto the simulation grid. A pattern is a multi-pixel configuration identifying a meaningful entity (a puzzle piece) of the underlying spatial continuity. The methodology relies on the use of a training image from which the pattern set (database) is extracted. The use of training images is not new. The concept of a training image is extensively used in simulating Markov random fields or for sequentially simulating structures using multiple-point statistics. Both these approaches rely on extracting statistics from the training image, then reproducing these statistics in multiple stochastic realizations, at the same time conditioning to any available data. The proposed approach does not rely, explicitly, on either a statistical or probabilistic methodology. Instead, a sequential simulation method is proposed that borrows heavily from the pattern recognition literature and simulates by pasting at each visited location along a random path a pattern that is compatible with the available local data and any previously simulated patterns. This paper discusses the various implementation details to accomplish this idea. Several 2D illustrative as well as realistic and complex 3D examples are presented to showcase the versatility of the proposed algorithm.     

Conditional Indicator Simulation: Application to a Saskatchewan uranium deposit

Mineral deposits frequently exhibit a mixture of rock types in which each type can be identified by a characteristic metal concentration. Such a mixture can be correctly simulated by first reproducing the spatial and geometric configuration of the various rock types in the deposit. Then the grades for each rock type can be jointly simulated and filled in according to their specific coregionalization characteristics. The method of Conditional Indicator Simulation and an uranium—arsenic joint simulation are presented with a detailed, step-by-step application to the Midwest deposit, a high grade uranium deposit in northern Saskatchewan.     

Conditional Simulation of Nongaussian Random Functions

This paper presents a conditional simulation procedure that overcomes the limits of gaussian models and enables one to simulate regionalized variables with highly asymmetrical histograms or with partial or total connectivity of extreme values. The philosophy of the method is similar to that of sequential indicator technique, but it is more accurate because it is based on a complete bivariate model by means of an isofactorial law. The resulting simulations, which can be continuous or categorical, not only honor measured values at data points, but also reproduce the mono and bivariate laws of the random function associated to the regionalized variable, that is, every one or two-point statistic: histogram, variogram, indicator variograms. The sequential isofactorial method can also be adapted to conditional simulation of block values, without resorting to point–support simulations.     

Conditional Spectral Simulation with Phase Identification

Spectral simulation is used widely in electrical engineering to generate random fields with a given covariance spectrum. The algorithms used are fast particularly when based on Fast Fourier Transform (FFT). However, because of lack of phase identification, spectral simulation only generates unconditional realizations. Local data conditioning is obtained typically by adding a simulated kriging residual. This conditioning process requires an additional kriging at each simulated node thus forfeiting the speed advantage of FFT. A new algorithm for conditioning is proposed whereby the phase values are determined iteratively to ensure approximative data reproduction while reproducing the frequency spectrum, that is, the covariance model. A case study is presented to demonstrate the algorithm.     

层序地层学预测隐蔽油气藏的原理和方法

动用层序地层学进行隐蔽油气藏预测是目前油气勘探的热门课题和重要的发展方向。本文以吐哈盆地ＳＢ地区侏罗系为例,探讨了层序地层学与隐蔽油气藏的关系。总结了运用层序地层学进行隐蔽油气藏预测的工作流程和研究思路。     

Conditional Simulation of Random Fields by Successive Residuals

This paper presents a new approach to the LU decomposition method for the simulation of stationary and ergodic random fields. The approach overcomes the size limitations of LU and is suitable for any size simulation. The proposed approach can facilitate fast updating of generated realizations with new data, when appropriate, without repeating the full simulation process. Based on a novel column partitioning of the L matrix, expressed in terms of successive conditional covariance matrices, the approach presented here demonstrates that LU simulation is equivalent to the successive solution of kriging residual estimates plus random terms. Consequently, it can be used for the LU decomposition of matrices of any size. The simulation approach is termed conditional simulation by successive residuals as at each step, a small set (group) of random variables is simulated with a LU decomposition of a matrix of updated conditional covariance of residuals. The simulated group is then used to estimate residuals without the need to solve large systems of equations.