Ensemble-Based Seismic and Production Data Assimilation Using Selection Kalman Model

Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data assimilation methods such as ensemble Kalman filter and ensemble smoother approaches, are based on Gaussian assumptions that are not applicable to multimodal random variables. The selection ensemble smoother is introduced as an alternative to traditional ensemble methods. In the proposed method, the prior distribution of the model variables, for example the porosity field, is a selection-Gaussian distribution, which allows modeling of the multimodal behavior of the posterior ensemble. The proposed approach is applied for validation on a two-dimensional synthetic channelized reservoir. In the application, an unknown reservoir model of porosity and permeability is estimated from the measured data. Seismic and production data are assumed to be repeatedly measured in time and the reservoir model is updated every time new data are assimilated. The example shows that the selection ensemble Kalman model improves the characterisation of the bimodality of the model parameters compared to the results of the ensemble smoother.

     

Data Assimilation in Truncated Plurigaussian Models: Impact of the Truncation Map

Assimilation of production data into reservoir models for which the distribution of porosity and permeability is largely controlled by facies has become increasingly common. When the locations of the facies bodies must be conditioned to observations, the truncated plurigaussian model has been often shown to be a useful method for modeling as it allows gaussian variables to be updated instead of facies types. Previous experience has also shown that ensemble Kalman filter-like methods are particularly effective for assimilation of data into truncated plurigaussian models. In this paper, some limitations are shown of the ensemble-based or gradient-based methods when applied to truncated plurigaussian models of a certain type that is likely to occur for modeling channel facies. It is also shown that it is possible to improve the data match and increase the ensemble spread by modifying the updating step using an approximate derivative of the truncation map.     

Simultaneous Estimation of Geologic and Reservoir State Variables Within an Ensemble-Based Multiple-Point Statistic Framework

Assessment of uncertainty due to inadequate data and imperfect geological knowledge is an essential aspect of the subsurface model building process. In this work, a novel methodology for characterizing complex geological structures is presented that integrates dynamic data. The procedure results in the assessment of uncertainty associated with the predictions of flow and transport. The methodology is an extension of a previously developed pattern search-based inverse method that models the spatial variation in flow parameters by searching for patterns in an ensemble of reservoir models. More specifically, the pattern-searching algorithm is extended in two directions: (1) state values (such as piezometric head) and parameters (such as conductivities) are simultaneously and sequentially estimated, which implies that real-time assimilation of dynamic data is possible as in ensemble filtering approaches; and (2) both the estimated parameter and state variables are considered when pattern searching is implemented. The new scheme results in two main advantages—better characterization of parameters, especially for delineating small scale features, and an ensemble of head states that can be used to update the parameter field using the dynamic data at the next instant, without running expensive flow simulations. An efficient algorithm for pattern search is developed, which works with a flexible search radius and can be optimized for the estimation of either large- or small-scale structures. Synthetic examples are employed to demonstrate the effectiveness and robustness of the proposed approach.     

资料同化在空气质量预报中的应用

随着数值模式的不断完善和观测技术的不断提高,资料同化逐渐成为能够进一步提高数值预报水平的一种有效方法。20世纪70年代,资料同化开始引入空气质量预报领域,成为当前大气环境科学研究的一个新方向。简要介绍了资料同化的含义,较详细地介绍了Kalman滤波法、四维变分同化法、牛顿松弛法的基本思想和优缺点,重点阐述了国内外资料同化在空气质量预报中的研究应用情况,最后指出资料同化应用于空气质量预报时存在的问题和今后的研究方向。     

第四系泥岩型生物气储层特征及动态成藏过程

第四系泥岩型生物气作为非常规油气领域一种新型的资源类型,具有巨大的资源潜力和勘探开发价值。以柴达木盆地三湖地区第四系泥岩型生物气为例,通过岩心观察、光学显微镜、扫描电镜、高压压汞、族组分分析等实验手段,分析了第四系泥岩型生物气储层特征及动态成藏过程。结果表明,第四系泥岩型生物气储层具有砂泥薄互层频繁交互、纵横向非均质性极强的特点。成岩作用处于早期阶段,R_o小于0.3%,岩石固结程度低,含贝类壳体没有石化,孔隙度普遍在20%以上,仍以微纳米孔隙为主,含极少量毫米孔隙。泥岩储层TOC极低,平均0.2%~0.4%,干酪根以Ⅲ型为主,主要成分是粗纤维,其次是半纤维素、有机氮,是第四系泥岩生气的主要母质来源。第四系泥岩型生物气为甲烷为主的干气,平均含量98.85%,地层水水型以CaCl₂型为主,酸碱度中等偏弱酸性。泥岩突破压力是甲烷滞留成藏的主要动力,低渗、富水和黏土的特征决定了泥岩具备自封闭能力。极低的气候温度、极高的水体盐度、充足的气源条件、有效的自封闭性是泥岩型生物气成藏的关键要素,以此建立了凹陷区自封闭富集带、斜坡区水封富集带、构造高点泥岩气-砂岩气叠合富集带等三种泥岩型生物气成藏模式。     

Inference of Global Reservoir Connectivity from Static Pressure Data with Fast Coarse-Scale Simulation Models

Mathematical Geosciences - Characterization of field-scale reservoir connectivity is critical for production optimization and field development planning. The information content of the data...     

无线气象站数据与人工观测蒸发量的资料同化——以白鹤滩电站坝址区数据为例

在MATLAB中使用最小二乘法拟合Goff-Gratch公式,得到关于气温的饱和水汽压函数。无线气象站观测得到的气象资料结合人工观测蒸发量,在MATLAB中进行回归分析,得到基于彭曼公式的日水面蒸发量计算公式。算例表明,与无线气象站的蒸发量计算值相比,回归分析得到的日水面蒸发量公式计算值与人工观测值能够同步对比。无线气象站资料和人工观测资料能够资料同化,适合在工程中推广。     

集合—变分数据同化方法的发展与应用

近年来,集合—变分数据同化方法已成为大气数据同化领域研究的热点问题.该方法能够综合利用集合卡尔曼滤波和变分同化的优势,是实现“集合预报和数据同化一体化”的有效途径.在分析变分同化和集合卡尔曼滤波优缺点的基础上引出集合—变分数据同化的概念;按照不同实现方式,将集合—变分同化分为协方差线性组合和增加控制变量2类,介绍了相应的研究进展,并将集合—变分同化概念拓展;然后介绍了集合—变分同化在英美两国的应用;最后回顾了集合—变分同化研究的主要问题,展望了未来的发展趋势.     

数据同化算法研究现状综述

近年来,全球环境变化对人类生存影响日益突出,为了加强对陆地—大气—海洋的监测,全球对地观测系统计划(GEOSS)和全球环境与安全监测计划(GMES)等相继被提出。数据同化算法作为连接观测数据与模型模拟预测的关键桥梁也得到了迅速发展,显著标志新的数学研究成果不断被引入数据同化算法中。在国家重大科技基础设施项目"航空遥感系统——地球科学数据处理软件系统"需求背景条件下,梳理遥感数据同化算法的国内外发展现状,明确数据同化算法开发的技术主线和重点内容,稳步推进数据同化算法理论研究和软件开发。     

非线性滤波方法与陆面数据同化

陆面数据同化研究近几年成为地球科学研究的新兴领域,其中以非线性滤波为代表的数据同化方法发展迅速并得到了广泛应用。在贝叶斯理论框架内,从递推贝叶斯估计理论的角度系统地分析了扩展卡尔曼滤波、无迹卡尔曼滤波、集合卡尔曼滤波、SIR粒子滤波等非线性滤波方法的异同;针对应用比较广泛的集合卡尔曼滤波和SIR粒子滤波应用中存在的问题,论述了几种提高滤波性能的实用方法,如协方差矩阵的Localization方法、协方差矩阵的Inflation方法、双集合卡尔曼滤波方法、扰动集合、扰动大气驱动和模型参数、平方根集合卡尔曼滤波以及粒子滤波算法的改进等。最后总结讨论了各种非线性滤波方法应用中的特点、难点以及各种算法在陆面数据同化中的应用前景和发展方向。     

土壤湿度遥感估算同化研究综述

土壤湿度是影响气候的至关重要的变量之一。利用数据同化方法反演大规模高精度土壤湿度数据是目前土壤水分研究的一个重要方向。结合国内外土壤湿度遥感估算研究现状,总结了土壤水分同化算法主要应用进程,梳理了目前实现土壤水分反演且应用广泛的陆面过程模型,Noah模型、通用陆面过程模型CLM、简单生物圈模型Si B2、北方生产力模拟模型BEPS,介绍了大范围卫星土壤水分数据集,包括陆面同化系统数据集、ASCAT数据集、AMSR-E数据集及SMOS数据集,最后探讨了遥感土壤水分同化过程中存在的问题及发展方向。     

Overview of Researches on All-Sky Satellite Microwave Data Variational Assimilation

As the important components of the earth’s atmospheric system, cloud and precipitation strongly affect the global hydrology and energy cycles through the interaction of solar and infrared radiation with cloud droplets and the release of latent heat in precipitation development. The microwave observations in cloudy and rainy conditions have a large amount of information closely related to the development of weather systems, especially the severe weather systems like typhoon and rainstorm. Nevertheless, satellite microwave observations are usually only assimilated in clear-sky above the ocean and their cloud and precipitation content is discarded. Over the past two decades, several Numerical Weather Prediction (NWP) centers have gradually developed the “all-sky” approach to make use of the cloud- and precipitation-affected microwave radiances. It’s been proved that the all-sky assimilation can be used to improve the first guessed mass, wind, humidity, cloud and precipitation through the tracer effect. For providing an investigated reference for the future research of all-weather assimilation in domestic numerical weather forecast, this paper reviewed the all-sky assimilation methods using microwave observation data, analyzed the advantages and disadvantages of each method, and discussed the key technical problems and the existing difficulties and challenges in this field. With the development and application of the new generation of NWP model in China, advancing the domestic research of all-weather data assimilation technology will bring more scientific and practical benefits in the future.     

陆面数据同化系统误差问题研究综述

同化系统中的误差问题一直被认为是制约数据同化性能的瓶颈问题。从分析陆面数据同化系统的误差问题研究现状出发,统一定义了同化系统的误差来源及误差表现,简要综述了顺序同化方法及连续同化方法中的误差定义和相关理论问题。从误差估计的角度,重点介绍了目前研究中各种误差估计的方法和面临的困难。针对误差处理方法的研究,介绍了在集合数据...     

关于河道型水库的防洪动库容

本文以珠江流域的大藤峡水库为例，提出如实表达河道型水库防洪库容的两个概念：防洪动库容和调洪库容。     

Simulation of Non-Gaussian Transmissivity Fields Honoring Piezometric Data and Integrating Soft and Secondary Information

The conditional probabilities (CP) method implements a new procedure for the generation of transmissivity fields conditional to piezometric head data capable to sample nonmulti-Gaussian random functions and to integrate soft and secondary information. The CP method combines the advantages of the self-calibrated (SC) method with probability fields to circumvent some of the drawbacks of the SC method—namely, its difficulty to integrate soft and secondary information or to generate non-Gaussian fields. The SC method is based on the perturbation of a seed transmissivity field already conditional to transmissivity and secondary data, with the perturbation being function of the transmissivity variogram. The CP method is also based on the perturbation of a seed field; however, the perturbation is made function of the full transmissivity bivariate distribution and of the correlation to the secondary data. The two methods are applied to a sample of an exhaustive non-Gaussian data set of natural origin to demonstrate the interest of using a simulation method that is capable to model the spatial patterns of transmissivity variability beyond the variogram. A comparison of the probabilistic predictions of convective transport derived from a Monte Carlo exercise using both methods demonstrates the superiority of the CP method when the underlying spatial variability is non-Gaussian.     

Localization Analysis of Data Assimilation Methods Coupled with Fuzzy Control Algorithms

Due to the fake correlation between distance-observations and assimilation-states during data assimilation, more attention has been paid to the localization method. Meanwhile, in the case of assimilation with a small number of sets, the observation data is difficult to be used effectively, which makes the assimilation effect not good enough. Therefore, a new fuzzy control was proposed to analyze the local method. The fuzzy control algorithm was used to judge the distance between the observation point and the status update point and to construct the fuzzy weight of the observation position. The study aimed to make use of the nonlinear Lorenz-96 model to compare the Fuzzy control combine Local Analysis algorithm (FLETKF) and Fuzzy control combine Ensemble Transform Kalman Filter method (FETKF), local Ensemble Transform Kalman Fliter (LETKF) and Ensemble Transform Kalman Filter algorithm (ETKF) when the nonlinear forced parameter changed. In addition, the strengths and weaknesses of four algorithms were discussed by different intensities. The results show that the new method can obtain more effective observation weights, avoiding the false correlation between long-distance observations and state variables, reducing the errors caused by the observation data which is difficult to be used effectively. Under different assimilation strength, FLETKF can maintain good robustness. However, in terms of assimilation time, the construction of the equivalent weight of the observation position requires additional time because the localization assimilation method of fuzzy control determines the distance between the observation point and the status update point. Parallel computing performance needs further study.     

滑坡在地形图上的表现特征和识别——以六盘水煤田为例

六盘水煤田盘县、普安、水城地区龙潭组含煤地层的上部及下部均为石灰岩等硬质岩层,易产生滑坡。区内滑坡的分布与含煤地层的分布基本一致。以区内典型滑坡为例,说明了滑坡各要素在地形图上的表现特征,总结出其识别标志,为野外滑坡的识别和滑坡体范围的圈定提供了辅助手段。