Ensemble Kalman Filter for estimation of rock properties in geothermal reservoirs characterized by fractured rocks or fluviatile sediments

集成卡尔曼滤波器，用于估计以裂隙岩或河流沉积物为特征的地热储层中的岩石特性

基本信息

批准号：
238370553
负责人：
Professor Dr. Christoph Clauser
金额：
--
依托单位：
Institute for Applied Geophysics and Geothermal Energy (GGE)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/238370553?language=en
关键词：
Ensemble Kalman Filter estimation rock

项目摘要

The rock properties controlling hydrothermal transport are interesting in many studies of groundwater flow, but are in particular important for the assessment of geothermal reservoirs. The topic of this project is to estimate these properties using the Ensemble Kalman Filter Method (EnKF). The related physical model for porous flow is numerically provided by the in-house simulator SHEMAT-Suite. As most inversion methods, the EnKF in its original form, is based on a Gaussian distribution of the parameters to be estimated. However, the crucial rock parameter for hydrothermal fluid flow is permeability which often has a bi-modal distribution in a reservoir. This may be caused by a superposition of porous and fractured (natural or engineered) permeability or intercalations of fluvial deposits in a sedimentary reservoir. Similar bi-model distribution can be observed for thermal conductivity in lithologically heterogeneous reservoirs. For reservoir analysis, the EnKF method has the decisive advantage that jointly with a parameter estimate it provides also a measure of the error of the estimate (variance). However, small ensembles tend to underestimate this error. In the proposed project we intend to improve the EnKF method for hydrothermal parameter estimation by implementing various new methods and study their single and combined effects: (1) Normal Score EnKF for transformation between normal and bi-modal parameter distributions, (2) localizations methods, to weight the filter function depending on the distance to the observation points, (3) a method for conditioning the covariance matrix (covariance inflation) to reduce the underestimation of the variance in EnKF. Here we will extend existing methods for parameter estimation and develop new approaches, in particular for localization. Once these methods are implemented, they will be tested on synthetic reservoir models (1) for a crystalline EGS reservoir and for a sedimentary reservoir containing fluvial deposits, and (2) on time series of chemical tracer concentration in two wells of the Soultz-sous-Forêts geothermal test site.

控制热液输送的岩石特性在许多地下水流研究中都很有趣，但对于地热储层的评估尤其重要，该项目的主题是使用集成卡尔曼滤波器方法（EnKF）来估计这些特性。多孔流模型由内部模拟器 SHEMAT-Suite 提供，与大多数反演方法一样，EnKF 的原始形式基于待估计参数的高斯分布。热液流体流动的关键岩石参数是渗透率，它在储层中通常具有双峰分布，这可能是由沉积储层中河流沉积物的多孔和裂缝（天然或工程）渗透率或夹层的叠加引起的。可以观察岩性非均质储层中热导率的双模型分布对于储层分析，EnKF 方法具有决定性的优势，它与参数估计一起还提供了误差的测量。然而，小型集合往往会低估这个误差。在所提出的项目中，我们打算通过实施各种新方法来改进用于水热参数估计的EnKF方法，并研究它们的单一和组合效果：（1）正态分数EnKF。用于正态和双模态参数分布之间的转换，(2) 定位方法，根据到观察点的距离对滤波器函数进行加权，(3) 调节协方差矩阵（协方差膨胀）以减少低估的方法在这里，我们将扩展现有的参数估计方法并开发新的方法，特别是本地化方法，一旦这些方法被实施，它们将在结晶 EGS 储层和合成储层模型 (1) 上进行测试。含有河流沉积物的沉积储层，以及 (2) Soultz-sous-Forêts 地热试验场两口井中化学示踪剂浓度的时间序列。