基于局域化EnKF同时同化地下水水流和溶质运移数据的渗透系数场识别研究

局域化改进集合卡尔曼滤波（EnKF）可以克服EnKF方法在使用小集合时,对参数识别精度较低的缺陷,其能同化 地下水位观测数据有效识别渗透系数场。实际工作中,溶质运移数据也较容易获得。崔凯鹏（2013）尝试增加溶质运移 数据以改进只同化水流数据对渗透系数的估计结果,但是精度提高有限。本文在其基础上修改模型,进一步增加溶质注 入井,探究同时同化水头和溶质运移数据,对渗透系数场识别效果,之后对比了局域化EnKF与非局域化EnKF参数识别结 果,并分析了溶质影响范围与参数识别的关系。结果表明：同时同化溶质运移和水头资料,比同化单一种类观测数据识别 的渗透系数精度更高;相同实现数目下,局域化EnKF比EnKF对渗透系数场的估计结果与真实场更为接近;仅考虑溶质影 响范围内的渗透系数,同化水头数据在最后时刻参数识别结果好于同化溶质运移数据参数识别结果,但差别不大。     

考虑预报偏差的迭代式集合卡尔曼滤波在地下水水流数据同化中的应用

集合卡尔曼滤波（Ensemble Kalman Filter,EnKF）方法已广泛应用于地下水水流和污染物运移模拟相关问题的求解。但前人研究多建立在同化系统预报模型是准确的基础上,忽视了模型概化的不确定性。当模型概化不准确时,将导致预报偏差,可能带来错误的系统估计。因此,文章提出考虑模型预报偏差的迭代式集合卡尔曼滤波（Bias aware Ensemble Kalman Filter with Confirming Option,Bias-CEnKF）方法。以地下水水流数据同化为例,研究模型概化存在不确定条件下,边界条件、初始条件、源汇项概化不准确时新方法的有效性。结果表明,当预报模型概化不准确时,使用标准EnKF方法进行数据同化,可能会导致滤波发散,造成同化失败。Bias-CEnKF方法不仅保留了较好的同化性能,同时减小了参数、变量、偏差项非线性关系带来的不一致性。针对文章中4种情景,Bias-CEnKF同化获得的含水层渗透系数场以及水头场均接近真实场,且预报结果可靠。本研究进一步提升了模型概化不确定时EnKF方法的适用性,为实际野外复杂条件下地下水水流数据同化问题提供了可靠的方法。     

A stochastic collocation based Kalman filter for data assimilation

In this paper, a stochastic collocation-based Kalman filter (SCKF) is developed to estimate the hydraulic conductivity from direct and indirect measurements. It combines the advantages of the ensemble Kalman filter (EnKF) for dynamic data assimilation and the polynomial chaos expansion (PCE) for efficient uncertainty quantification. In this approach, the random log hydraulic conductivity field is first parameterized by the Karhunen–Loeve (KL) expansion and the hydraulic pressure is expressed by the PCE. The coefficients of PCE are solved with a collocation technique. Realizations are constructed by choosing collocation point sets in the random space. The stochastic collocation method is non-intrusive in that such realizations are solved forward in time via an existing deterministic solver independently as in the Monte Carlo method. The needed entries of the state covariance matrix are approximated with the coefficients of PCE, which can be recovered from the collocation results. The system states are updated by updating the PCE coefficients. A 2D heterogeneous flow example is used to demonstrate the applicability of the SCKF with respect to different factors, such as initial guess, variance, correlation length, and the number of observations. The results are compared with those from the EnKF method. It is shown that the SCKF is computationally more efficient than the EnKF under certain conditions. Each approach has its own advantages and limitations. The performance of the SCKF decreases with larger variance, smaller correlation ratio, and fewer observations. Hence, the choice between the two methods is problem dependent. As a non-intrusive method, the SCKF can be easily extended to multiphase flow problems.     

Surface electrical resistivity tomography and hydrogeological characterization to constrain groundwater flow modeling in an agricultural field site near Ferrara (Italy)

A portion of an unconfined alluvial aquifer located in the Padana Plain (Italy) was characterized following an integrated hydro-geophysical approach. Initially an electrical resistivity tomography (ERT) survey was employed to localize the boundaries of a modest paleo-channel body and to design the installation of a groundwater monitoring network. Multilevel slug-tests were performed to estimate the aquifer’s saturated hydraulic conductivities. Determined permeability values together with electrical resistivity data were correlated. The correlation resulted in a site specific bi-logarithmic linear relationship. Based on this relationship, punctually determined hydraulic conductivities were spatially extended over the studied flow domain. Finally, continuously measured piezometric heads were used to calibrate a 3D flow model. Sensitivity analysis was performed to confirm the reliability of the reconstructed permeability field, as well as, to assess the minimum number of direct measurements needed to safely characterize the selected aquifer portion. The integration of the ERT survey results with the classical hydrogeological tests can be conveniently applied to constrain the permeability field during flow model calibration. Although the applicability of the determined relationship is site specific, the followed procedure is useful especially when there is a need to optimize the available resources and in case of small-scale pilot studies.     

Ensemble filter methods with perturbed observations applied to nonlinear problems

The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g., water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble methods when applied to nonlinear problems.     

Statistical Distributions of Hydraulic Conductivity from Reliability Analysis Data

This paper describes a first-order reliability-based analysis to identify the best-fit probability distributions for hydraulic conductivity. The analysis involved the use of existing hydraulic conductivity model developed from laboratory data and applied to lateritic soils, considering variations in soil parameters. Plots of reliability indices versus coefficients of variation were first made for hydraulic conductivity as well as for initial degree of saturation, plasticity index and clay content, considering three compactive efforts and log-normally distributed hydraulic conductivity. The traditional two-parameter log-normal distribution was compared to four alternative distributions: normal, gamma, Gumbel (extreme value type I-EVT-I) and Weibull (extreme value type III-EVT-III). The analysis showed that the Weibull and normal are the best-fit probability distributions for the hydraulic conductivity based reliability data. Hydraulic conductivities predicted from reliability analysis were used to demonstrate the possibility of applying the results obtained in this research by practising engineers. Experimentally-determined hydraulic conductivities were shown to be in good agreement with predicted values.     

基于集合卡尔曼滤波的多相流模型参数估计——以室内二维砂箱中重质非水相污染物入渗为例

重质非水相有机污染物(DNAPL)泄漏到地下后,其运移与分布特征受渗透率非均质性影响显著。为刻画DNAPL污染源区结构特征,需进行参数估计以描述水文地质参数的非均质性。本研究构建了基于集合卡尔曼滤波方法(EnKF)与多相流运移模型的同化方案,通过融合DNAPL饱和度观测数据推估非均质介质渗透率空间分布。通过二维砂箱实际与理想算例,验证了同化方法的推估效果,并探讨了不同因素对同化的影响。研究结果表明:基于EnKF方法同化饱和度观测资料可有效地推估非均质渗透率场;参数推估精度随观测时空密度的增大而提高;观测点位置分布对同化效果有所影响,布置在污染集中区域的观测数据对于参数估计具有较高的数据价值。     

Mapping hydraulic fractures from tiltmeter data using the ensemble Kalman filter

Hydraulic fracturing involves the initiation and propagation of fractures in rock formations by the injection of pressurized fluid. The largest use of hydraulic fracturing is in enhancing oil and gas production. Tiltmeters are sometimes used in the process to monitor the generated fracture geometry by measuring the fracture‐induced deformations. Fracture growth parameters obtained from tiltmeter mapping can be used to study the effectiveness of such stimulations. In this work, we present a novel scheme that uses the ensemble Kalman Filter (EnKF) to assimilate tiltmeter data using a simple process model to describe the evolution of fracture growth parameters, and an observation model that maps the fracture geometry with the observed tilt. The forward observation model is based on the analytical solution for computing the displacements and tilts due to a point source displacement discontinuity in an elastic half‐space developed by Okada 1 . The displacement and tilts for any given fracture geometry are then obtained by numerical integration of this solution, by considering multiple point sources to be located at the quadrature points. The proposed method is validated using synthetic data sets generated from polygon and elliptical shaped fracture geometries. Finally, real data from a field site, where asymmetry was measured from the intersections of the hydraulic fracture with offset boreholes, have been analyzed. Preliminary results show that, in addition to extracting the fracture dip, orientation, and volume, the procedure is able to satisfactorily predict fracture growth parameters when the fracture is relatively close to the tiltmeter array and provides some insight into the development of asymmetry when the measurements are relatively far from the fracture plane. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

水动力学模型与集合卡尔曼滤波耦合的实时校正多变量分析方法

为减少非恒定水流计算中的不确定性,在水流随机运动系统状态空间模型基础上,应用集合卡尔曼滤波(EnKF)技术建立了非恒定水流分析的实时更新(校正)方法。该方法适用于非线性的随机微分方程,过程和观测噪声可以是非正态分布。同时,为充分利用水位、流量等误差量级相差巨大的观测中所蕴含的有效信息,导出了EnKF多变量分析格式。以明渠单峰洪水过程的合成数据为例,考察了运用建立的实时更新方法分析预报一维洪水演进的性能。重点对比了采用不同精度等级下的水位和流量观测资料进行滤波的效果。在中国现行标准规定的允许观测误差范围内,以水位观测进行一维洪水动力学模型的滤波分析可有效地控制误差、估计流量、识别水流运动系统状态。长江干流清溪场至万县江段实际洪水计算还证实:该方法通过插入即时观测,可实时更新模型状态,给出与实际更为接近的计算。     

不同滤波算法在土壤湿度同化中的应用

为研究不同滤波算法在土壤湿度同化中的有效性,以及土壤湿度模拟结果对模型参数的敏感性,结合简单生物圈模型SiB2,设置敏感性实验,探求土壤饱和水力传导度对土壤湿度模拟结果的影响;并在此基础上,采用集合卡尔曼滤波（EnKF）、无迹卡尔曼滤波（UKF）和无迹粒子滤波（UPF）开展土壤湿度实时同化实验。结果表明：土壤饱和水力传导度能显著影响土壤湿度模拟精度;利用EnKF、UKF、UPF同化站点观测数据,均能改善土壤湿度模拟结果;3种同化方法在不同土壤层的同化效果不同,在土壤表层,EnKF的有效性优于UKF和UPF,在根域层和土壤深层,3种滤波方法有效性在降雨前后相差较大。因此,针对性地选择同化方法,是提高土壤湿度模拟精度的有效手段。     

基于多点地质统计与集合平滑数据同化方法识别非高斯渗透系数场

在冲积含水层中,由于岩相的非均质分布,渗透系数一般呈现出明显的非高斯特性（例如砂和黏土两种岩相）,非高斯特性给地下水模型参数的推估带来了困难与挑战。目前广泛使用的集合平滑数据同化方法（ESMDA）虽然有效且计算成本较低,但仅适用于高斯场。多点地质统计方法虽已广泛用于模拟非高斯场,但其无法融入动态观测数据推估参数。基于多点地质统计方法中的直接采样法（DS）与集合平滑数据同化方法,构建一种新的数据同化框架（ESMDA-DS）,既可保持参数场的非高斯特性,又可融合多源数据精确推估非高斯场。构建三个理想算例验证ESMDA-DS对非高斯参数场的推估效果,并探讨了不同类型观测数据对推估效果、水位与浓度预测精度的影响。三个理想算例包括仅融合水位数据（Case 1）,同时融合水位与浓度数据（Case 2）,同时融合水位、浓度与对数渗透系数数据（Case 3）。结果表明:ESMDA-DS方法结合了ESMDA与DS的各自优势,能有效融合观测数据推估渗透系数场,并保持参数场的非高斯特性。通过对比三个算例推估结果,Case 3的参数场推估效果最好,水位与浓度预测精度最高,Case 2次之,Case 1最差,表明融合多源数据可改善推估效果,提高预测精度。     

基于集合卡尔曼平滑算法的土壤水分同化

为研究观测资料稀少情况下土壤质地及有机质对土壤水分同化的影响,发展了集合卡尔曼平滑(Ensemble Kalman Smooth, EnKS)的土壤水分同化方案。利用黑河上游阿柔冻融观测站2008年6月1日至10月29日的观测数据,使用EnKS算法将表层土壤水分观测数据同化到简单生物圈模型(Simple Biosphere Model 2, SiB2)中,分析不同方案对土壤水分估计的影响,并与集合卡尔曼滤波算法(EnKF)的结果进行比较。研究结果表明,土壤质地和有机质对表层土壤水分模拟结果影响最大而对深层的影响相对较小;利用EnKF和EnKS算法同化表层土壤水分观测数据,均能够显著提高表层和根区土壤水分估计的精度,EnKS算法的精度略高于EnKF且所受土壤质地和有机质的影响小于EnKF;当观测数据稀少时,EnKS算法仍然可以得到较高精度的土壤水分估计。     

An efficient matrix-free algorithm for the ensemble Kalman filter

In this work, we present an efficient matrix-free ensemble Kalman filter (EnKF) algorithm for the assimilation of large data sets. The EnKF has increasingly become an essential tool for data assimilation of numerical models. It is an attractive assimilation method because it can evolve the model covariance matrix for a non-linear model, through the use of an ensemble of model states, and it is easy to implement for any numerical model. Nevertheless, the computational cost of the EnKF can increase significantly for cases involving the assimilation of large data sets. As more data become available for assimilation, a potential bottleneck in most EnKF algorithms involves the operation of the Kalman gain matrix. To reduce the complexity and cost of assimilating large data sets, a matrix-free EnKF algorithm is proposed. The algorithm uses an efficient matrix-free linear solver, based on the Sherman–Morrison formulas, to solve the implicit linear system within the Kalman gain matrix and compute the analysis. Numerical experiments with a two-dimensional shallow water model on the sphere are presented, where results show the matrix-free implementation outperforming an singular value decomposition-based implementation in computational time.     

Application of the EnKF and Localization to Automatic History Matching of Facies Distribution and Production Data

The performance of the ensemble Kalman filter (EnKF) for continuous updating of facies location and boundaries in a reservoir model based on production and facies data for a 3D synthetic problem is presented. The occurrence of the different facies types is treated as a random process and the initial distribution was obtained by truncating a bi-Gaussian random field. Because facies data are highly non-Gaussian, re-parameterization was necessary in order to use the EnKF algorithm for data assimilation; two Gaussian random fields are updated in lieu of the static facies parameters. The problem of history matching applied to facies is difficult due to (1) constraints to facies observations at wells are occasionally violated when productions data are assimilated; (2) excessive reduction of variance seems to be a bigger problem with facies than with Gaussian random permeability and porosity fields; and (3) the relationship between facies variables and data is so highly non-linear that the final facies field does not always honor early production data well. Consequently three issues are investigated in this work. Is it possible to iteratively enforce facies constraints when updates due to production data have caused them to be violated? Can localization of adjustments be used for facies to prevent collapse of the variance during the data-assimilation period? Is a forecast from the final state better than a forecast from time zero using the final parameter fields?To investigate these issues, a 3D reservoir simulation model is coupled with the EnKF technique for data assimilation. One approach to enforcing the facies constraint is continuous iteration on all available data, which may lead to inconsistent model states, incorrect weighting of the production data and incorrect adjustment of the state vector. A sequential EnKF where the dynamic and static data are assimilated sequentially is presented and this approach seems to have solved the highlighted problems above. When the ensemble size is small compared to the number of independent data, the localized adjustment of the state vector is a very important technique that may be used to mitigate loss of rank in the ensemble. Implementing a distance-based localization of the facies adjustment appears to mitigate the problem of variance deficiency in the ensembles by ensuring that sufficient variability in the ensemble is maintained throughout the data assimilation period. Finally, when data are assimilated without localization, the prediction results appear to be independent of the starting point. When localization is applied, it is better to predict from the start using the final parameter field rather than continue from the final state.     

A comparison between a Monte Carlo implementation of retrospective optimal interpolation and an ensemble Kalman filter in nonlinear dynamics

To more correctly estimate the error covariance of an evolved state of a nonlinear dynamical system, the second and higher-order moments of the prior error need to be known. Retrospective optimal interpolation (ROI) may require relatively less information on the higher-order moments of the prior errors than an ensemble Kalman filter (EnKF) because it uses the initial conditions as the background states instead of forecasts. Analogous to the extension of a Kalman filter into an EnKF, an ensemble retrospective optimal interpolation (EnROI) technique was derived using the Monte Carlo method from ROI. In contrast to the deterministic version of ROI, the background error covariance is represented by a background ensemble in EnROI. By sequentially applying EnROI to a moving limited analysis window and exploiting the forecast from the average of the background ensemble of EnROI as a guess field, the computation costs for EnROI can be reduced. In the numerical experiment using a Lorenz-96 model and a Model-III of Lorenz with a perfect-model assumption, the cost-effectiveness of the suboptimal version of EnROI is demonstrated to be superior to that of EnKF using perturbed observations.     

三峡库区黄土坡滑坡非饱和水力参数研究

非饱和水力参数在计算滑坡降雨入渗过程与稳定性时是至关重要的材料参数。在三峡库区黄土坡滑坡上进行双环渗透试验,获取黄土坡滑坡表土层的饱和渗透系数。对黄土坡滑坡表土层的含水率和基质吸力进行实时监测,采集了黄土坡滑坡表土层中含水率和基质吸力随时间的变化数据,采用van Genuchten土-水特征曲线模型拟合了4个实时监测剖面的土-水特征曲线及其拟合参数。将饱和渗透系数与土-水特征曲线拟合参数代入van Genuchten渗透系数函数模型,求出了黄土坡滑坡表土层在非饱和条件下的渗透系数函数,为黄土坡滑坡在降雨作用下的稳定性计算提供了可靠的水力参数     

A cost-effective device and methodology to compute aquifer transmissivity and piezometry from free-flowing artesian wells

Artesian aquifers offer interesting opportunities for water supply by providing a low-vulnerability groundwater resource that is easily abstracted without any installation of pumps or power supply costs. However, hydraulic tests are challenging to perform, notably where the piezometric head is above ground level with free-flowing wells not equipped with valves and open for years. This paper describes a low-cost, easy to reproduce and adaptable device, the free-flowing artesian well device (FFAWD), which is mainly designed with a set of PVC tubes equipped with a pressure probe and a valve. This device is used to perform hydraulic tests on free-flowing artesian wells, to measure the piezometric head of the aquifer and to compute its transmissivity. The practical use of the FFAWD is described and a method is proposed to compute the piezometric head and the transmissivity of the aquifer from this data set (free-flowing well discharge and pressure increase measurements) with any adapted analytical solution, using the Houpeurt-Pouchan method. Artefacts such as post-production effects, surge effects, and the impact of a leaky well are identified to avoid any misinterpretation. The FFAWD was applied to the volcano-sedimentary artesian plain of Pasuruan (Indonesia). The advantages and limitations of using the device, along with the interpretation methodology, are also discussed.

     

Hydrogeological model of the Baltic Artesian Basin

The Baltic Artesian Basin (BAB) is a complex multi-layered hydrogeological system in the south-eastern Baltic covering about 480,000 km². The aim of this study is to develop a closed hydrogeological mathematical model for the BAB. Heterogeneous geological data from different sources were used to build the geometry of the model, i.e. geological maps and stratigraphic information from around 20,000 boreholes. The finite element method was used for the calculation of the steady-state three-dimensional (3D) flow of unconfined groundwater. The 24-layer model was divided into about 1,000,000 finite elements. A simple recharge model was applied to describe the rate of infiltration, and the discharge was set at the water-supply wells. Variable hydraulic conductivities were used for the upper (Quaternary) deposits, while constant hydraulic conductivity values were assumed for the deeper layers. The model was calibrated on the statistically weighted borehole water-level measurements, applying L-BFGS-B (automatic parameter optimization method) for the hydraulic conductivities of each layer. The principal flows inside the BAB and the integral flow parameters were analyzed. The modeling results suggest that deeper aquifers are characterized by strong southeast–northwest groundwater flow, which is altered by the local topography in the upper, active water-exchange aquifers.     

含水层水力参数的尺度效应研究现状

主要论述了国内外目前对弥散率尺度效应以及渗透系数等夺数的空间变异性研究的现状和今后的研究趋势。文中提出了含水层水力参数的尺度效应问题，并指出庄重视地下水驱动力场变化对参数的影响研究。