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.

在许多地下水流量的研究中，控制水热运输的岩石性能很有趣，但对于评估地热储层特别重要。该项目的主题是使用集成Kalman滤波器方法（ENKF）估算这些属性。多孔流的相关物理模型是由内部模拟器Shemat-suite数值提供的。作为大多数反转方法，其原始形式的ENKF基于要估计的参数的高斯分布。但是，水热流动流的关键岩石参数是渗透率，通常在储层中具有双模式分布。这可能是由多孔和破裂（天然或工程）渗透性的叠加或沉积物储层中河流沉积物的插入的叠加引起的。在岩性异质储层中，可以观察到类似的双模型分布。对于储层分析，ENKF方法具有决定性的优势，即与参数估算的共同衡量估计误差（方差）。但是，小合奏倾向于低估此错误。在拟议的项目中，我们打算通过实施各种新方法并研究其单一和组合效果来改善ENKF方法的估算方法：（1）正常得分ENKF ENKF在正常和双模式参数分布之间转换，（2）局部参数方法，（2）根据距离的距离，取决于距离的距离（3）covriance（3）covriance（3）covriance（3）（3）（3） ENKF的差异。在这里，我们将扩展现有的参数估计方法，并开发新方法，特别是用于本地化。一旦实施了这些方法，它们将在合成储层模型（1）上进行结晶的EGS储层和含有河流沉积物的沉积物储层的合成储层模型（1），以及（2）在Soultz-Sous-Sous-Sous-Sous-Forêtseothermal测试地点的化学示踪剂浓度的时间序列中。