A method for automatic history matching of a compositional reservoir simulator with multipoint flux approximation

A method for history matching of an in-house petroleum reservoir compositional simulator with multipoint flux approximation is presented. This method is used for the estimation of unknown reservoir parameters, such as permeability and porosity, based on production data and inverted seismic data. The limited-memory Broyden–Fletcher–Goldfarb–Shanno method is employed for minimization of the objective function, which represents the difference between simulated and observed data. In this work, we present the key features of the algorithm for calculations of the gradients of the objective function based on adjoint variables. The test example shows that the method is applicable to cases with anisotropic permeability fields, multipoint flux approximation, and arbitrary fluid compositions.     

Field variability of landslide model parameters

 A data set of parameters (slope, soil depth and soil shear strength) relevant to spatially distributed modelling of shallow landslides triggered by rain and snowmelt events was determined from field measurements in 250 grid elements of dimensions 25 m (downslope)×10 m (across slope) in an area of 250 m×250 m on a hillslope in Scotland. These data provide an unusually detailed basis for the evaluation of spatial variability and uncertainty in model parameterisation. The variations in slope and soil strength are represented adequately by normal distributions; a Weibull distribution is suggested for the soil depth data. The factor of safety calculated at each point in the grid was shown partially to identify observed landslides, with a number of false predictions of occurrence. Trend analysis and semivariogram analysis of the data set suggest that the use of kriging could improve upon this approach to landslide prediction by providing areal estimates of parameters at the grid element scale with associated error bounds. Received: 30 October 1996 · Accepted: 25 June 1997     

巫峡老鼠错后缘陡崖滚石崩塌运动特征研究

论文针对巫峡老鼠错区域后缘陡崖滚石崩塌事件进行了详细的地质调查,并对该事件滚石崩塌运动特征进行了数值反分析计算及参数敏感性分析研究。通过对老鼠错滚石运动过程反分析并结合现场调查,可以看出老鼠错滚石运动整体呈自由跌落-撞击坡体（海拔550 m左右位置）-顺坡体滚动-二次撞击（海拔400 m左右位置）-顺坡体滚动运移模式,滚石能量急剧消减主要受滚石与坡体撞击时法向速度损失强度所影响。考虑到坡体覆盖层状态受自然界各种因素影响而变化,进而对表征滚石法向速度损失大小的法向恢复系数R_n进行敏感性分析,结果表明随着法向恢复系数R_n的增大,滚石最后阶段运动模式将由顺坡体滚动运移模式转变为顺坡体弹跳式运移模式。这导致滚石入水速度将显著增加,进而会对滚石入水处附近长江水体造成较大的冲击,影响靠近的来往船只的安全航行。论文建议未来对研究区域内高陡坡体后缘滚石崩塌灾害进行预防及评估时,需考虑坡体覆盖层实际情况及可能存在的不利情况。     

安太堡矿露井联采边坡稳定性研究及其边界参数优化

安太堡露天矿为我国最早合资开发的大型露天煤矿,近年来,该矿实施露井联采模式,给安太堡矿东南帮和南帮的边坡稳定性的保证和露井边界的确定带来了困难,严重影响安太堡矿的正常生产。通过综合研究,指出露井联采模式下的边坡主要存在两种破坏模式,其井工开采开切眼的位置是确定边坡稳定的关键因素。首次提出了以两种开采方法之间的垂直安全高度为判断依据,并以此依据确定了露井边界参数,为保证安太堡矿南帮的安全开采提供了依据。     

The parameter distributions of the integer GPS model

 A parameter estimation theory is incomplete if no rigorous measures are available for describing the uncertainty of the parameter estimators. Since the classical theory of linear estimation does not apply to the integer GPS model, rigorous probabilistic statements cannot be made with reference to the classical results. The fact that integer parameters are involved in the estimation process forces a reappraisal of the propagation of uncertainty. It is with this purpose in mind that the joint and marginal distributional properties of both the integer and non-integer parameters of the GPS model are determined. These joint distributions can also be used to determine the distribution of functions of the parameters. As an important example, the distribution of the vector of ambiguity residuals is determined. Received: 30 January 2001 / Accepted: 31 July 2001     

土石混合体砾-土接触面参数的敏感性分析

土石混合体作为一种特殊的地质体在自然界中广泛分布,由强度较高的岩石块体、细土颗粒和充填其间的孔隙组成。随着各类大型工程建设的兴起和发展,与土石混合体相关的地质灾害严重影响和制约了工程的建设和人类的安全,因而对土石混合体的研究至关重要。本文从细观角度出发着重研究土石混合体中砾石和土体之间的接触性质,利用FLAC3D接触面单元模拟了土石混合体中的砾-土界面,首先构建了包含接触面的土石混合体三维数值模型,其次选取了接触面参数的合理变化范围,通过对数值模拟结果的分析,探讨了参数的敏感性,发现刚度,特别是法向刚度对土石混合体应力-应变曲线和砾-土的相对运动影响最大,其次是黏聚力,摩擦角的影响最小。通过与无接触面模型的对比,验证了包含接触面单元土石混合体三维模型的可行性和合理性。接触面单元的引入使精确描述土石混合体的宏观变形特征及砾-土间的相互作用机制成为可能。     

承压水体上煤柱留设的理论与实践

华盖山煤矿主要可采Ⅱ号煤层下存在强岩溶含水层,为划定安全开采范围,综合应用国内相关科研成果,成功地留设防水煤柱,划定可采区域。经矿井建设检验,满足安全开采需要。     

方位指向检测系统的参数测定

针对低纬子午环的观测方式和方位定向系统的精度，叙述了配备方位指向检测系统的必要性，介绍了方位指向检测的原理和系统参数的测定方法，分析了在仪器的三个支承螺杆有晃动的情况下，系统参数测定的不可靠性，提出了克服参数测定不可靠性影响的方法。     

Transferability of hydrological model parameters between basins in data-sparse areas, subarctic Canada

Hydrological models used for the simulation of runoff are often calibrated only on the basis of data obtained at the catchment outlet but the parameters thus derived are then applied to the simulations for the subbasins. Such a practice is common for the data-sparse areas such as the subarctic. However, it may yield erroneous results when the calibrated model parameters are applied to basins of various sizes, or with divergent physical characteristics. This study assesses the feasibility of transferring parameter estimates derived for one basin of a particular size to other basins of different dimensions, using the SLURP model for simulation and the Liard and two of its subbasins as an example. Results indicate that other than the snowmelt factor, the parameter values obtained from the subbasins are similar, but values of several parameters (e.g. maximum capacity of the soil water and groundwater storage, and snowmelt factor) are different from those derived for the large basin. Compared with applying the Liard basin parameters, the subbasins parameter sets generate higher evapotranspiration, earlier termination of the snowmelt period, more soil water storage, a shorter period with significant soil water storage and a better overall agreement between the observed and simulated runoff. It is recommended that adequate attention be given to the transferability of the parameter values to improve the simulation of subbasins hydrology.     

应用并行PEST算法优化地下水模型参数

基于列文伯格-马夸尔特（Levenberg-Marquardt）算法的PEST参数优化程序具有寻优速度快、健壮性好的优点,在地下水模型参数优化研究中有许多成功的应用实例。但是,对于大尺度、高精度和高复杂性的大规模地下水模拟,使用PEST进行参数优化需要大量的计算时间,优化效率较低。本文应用OpenMP并行编程方法对PEST算法进行了并行化,使之可以在共享存储并行计算机上进行参数优化的并行计算。并将此方法应用于甘肃北山区域地下水模型的参数优化中,并行实验表明,使用并行化的PEST可以将地下水模型参数优化效率提高3.7倍。\r\n