西北区东部一次暴雨的数值模拟试验

运用双向嵌套的中尺度数值预报模式MM5，对1998年7月上旬西北区东部一次暴雨过程进行了高分辨率数值模拟和敏感性试验。结果表明，该模式能较好地模拟这次暴雨过程，对这次暴雨过程相关的中尺度系统的发生发展也作出了较成功的模拟；大尺度及积云对流尺度的凝结潜热在降水过程中是一个主要因子，潜热释放将加热中高层大气，促使高层大气辐散，低层辐合，垂直运动加强，导致较大的降水；初始时刻不同地区低层大气水汽含量的多寡直接对本次暴雨产生影响，并为这次暴雨提供了水汽源；地面水汽和感热的垂直输送为暴雨的发生发展补充了能量。     

长江三峡工程奉节库岸边坡地下水渗流场模拟分析

长江三峡工程正常蓄水位为175m，在蓄水过程中随着库水位的抬升，必然会引起库岸边坡地下水渗流场的变化。运用3D－Modflow软件模拟了三峡工程的蓄水过程中奉节库段边坡岩土体内部地下水位的动态变化，为长江三峡工程奉节库区段移民迁建地质灾害预测与防治提供科学依据。     

确定边坡最危险滑动面的计算机模拟

边坡稳定性分析 ,关键是确定潜在的滑动面 ,目的是有利于工程安全。本文编制了二、三维计算机模拟程序 ,在二维模拟中提出了四种计算模式 ,三维模拟中考虑了空间、荷载、渗流效应 ,结合工程实例对边坡进行模拟并对土层抗剪强度参数进行了反演 ,模拟计算与实测结果吻合 ,模拟出的抗剪强度参数平均值与实测的抗剪强度参数标准值相当     

具有地形坡折带的坳陷湖盆层序地层模拟

层序地层学研究已在陆相湖盆中取得良好的应用效果，在陆相湖盆的地层对比、沉积体系空间分布预测中发挥重要作用。坳陷湖盆在基底受压变形过程中，常在盆缘形成地形坡折带，以坡折为界，基底的沉降速率和原始地形坡度等控制层序发育的重要因素在其两侧均存在显著的差异，这在很大程度上控制了层序的发育模式和沉积体系的空间配置关系。通过对基底沉降速率、湖平面变化速率、沉积物充填速率、沉积物充填准则、岩相确定原则等数学模 型的建立，模拟了具有地形坡折带的坳陷湖盆的层序发育和相演化过程。实际资料与模拟结果的对比分析表明，模拟具有很好的效果。     

原油在储层介质中的加水裂解生气模拟实验

采用高温高压热模拟实验方法,开展了原油在砂岩和火成岩储层介质中的加水裂解生气模拟实验研究.结果表明,原油开始大量裂解的温度是400℃,随模拟温度增加,甲烷相对含量增大,乙烷以上重烃气尤其是丙烷相对含量减小.其中砂岩的油水混合物裂解生气主要发生在450～500℃之间,生气窗范围小,对应的烃气产率高,火成岩的油水混合物裂解生气主要发生在450～600℃之间,生气窗范围大,对应的烃气产率小.模拟烃气的组分碳同位素分馏显著,随模拟温度增加呈变重趋势.在裂解生气过程中,水解加氢和催化作用对烃气的组成、产率和碳同位素分布有重要影响。     

沉积盆地深部流体的地球化学特征及油气成藏效应初探

以济阳坳陷东营凹陷和塔里木盆地塔中地区为例，在前人深部流体研究的基础上，应用同位素地球化学、有机地球化学及热力学定量模型，对沉积盆地深部流体的活动特征及其油气成藏效应进行了初步的探讨。研究表明，在东营凹陷不仅存在着幔源富二氧化碳流体（H2O＋CO2）的活动，而且还存在着幔源富氢流体（H2O＋CH4＋H2）的活动。塔里木盆地塔中地区也发现了幔源富二氧化碳的活动。深部流体上升过程中热能传递的定量研究表明，幔源流体是良好的热能载体。东营凹陷和塔中地区的有机质异常热变现象证实了深部流体的热效应。有机质热演化生烃不仅需要热，而且是个缺氢的过程，富氢流体注入沉积盆地势必对油气的生成产生影响。加氢热模拟实验结果表明，加氢可大幅度提高烃源岩的产烃率；对腐泥型干酪根而言，加氢生烃效应最显著的阶段是在生烃高峰之后，产率可增加147％以上；腐植型干酪根的加氢生烃效应在各个阶段都较显著。在东营凹陷和塔中地区分别发现了深部流体促进烃源岩生烃的现象。因此，深部流体在能量上和物质上对油气的生成均可构成重要的影响。     

DNDC模型对我国旱地N2O释放的拟合对比分析

本文根据贵州省玉米－油菜轮作田、休耕地和大豆－冬小麦轮作田的N2O释放通量及其影响因子季节变化的是观测结果，对比分析了DNDC模型计算与三试验田田间观测在N2O释放通量及其主要影响因子季节变化上的拟合程度。结果表明，对N2O释放通量、土壤NO3^-和NH4^ 含量、土壤湿度和土温，模型计算结果与田间观测具有相似的季节变化模式和相近的数值，模型较好地反映了上述参数的动态变化过程。据此，推算出贵州省农业土壤N2O年释放量，并分析了气候变化和农业活动对释放量的潜在影响。     

地下水数值计算中断层处理的"切割-导通法"

从对断层导水性能的分析出发 ,提出了地下水数值计算中断层处理的“切割—导通法” ,既考虑断层侧向导水性 ,又考虑了其垂向导水性 ,更符合断层导水的实际情况 ,也易于描述较大断裂在走向上的导水性差异。     

Evaluation of the Reduction in Uncertainty Obtained by Conditioning a 3D Stochastic Channel to Multiwell Pressure Data

A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters.     

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.