权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

A hybrid stochastic-deterministic model calibration method with application to subsurface CO2 storage in geological formations

一种混合随机-确定性模型校准方法，应用于地质构造中地下二氧化碳封存

基本信息

批准号：
288483442
负责人：
Professor Dr.-Ing. Wolfgang Nowak
金额：
--
依托单位：
Fachgebiet Wasserversorgung und Grundwasserschutz
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/288483442?language=en
关键词：
hybrid stochastic deterministic model calibration

项目摘要

Engineers increasingly attract notice to the natural subsurface for very different and possibly competing kinds of applications. On the one hand, the subsurface contains natural resources. On the other hand, it is used for temporary or permanent storage of waste and gas. For all of these competing use types, it is indispensable for our society to assess their performance, limitations, risks and mutual restrictions. The quality of model predictions depends strongly on the quality of the model parameters. Within this proposal, we have a major focus on gas storage in the subsurface, and in particular we focus on CO2 storage in deep saline formations since we have a strong background and experience in this field. However, we emphasize that the methods applied and developed for this field of engineering application can be transferred to other related fields in a straightforward way. From previous studies it is known that the main prediction errors and uncertainties in simulating processes in the subsurface associated with gas storage, or more general with injection of a fluid, arises from uncertainties in the subsurface structure and material parameters. A most recent example is the modelling and simulation for the Ketzin pilot storage site in the state of Brandenburg/Germany. A comprehensive exploration and monitoring program has been conducted in order to provide best possible data according to the state of the art. Most important in the context of this proposal is the history matching of the observation data, i.e.(,) mainly observed time series of pressure and the arrival time of injected CO2 in two observation wells. Models are required to have predictive power for the future behavior of the reservoirs with increased confidence so that they can be used to provide robust decision support for managing the injection and storage. This proposal aims to develop computationally efficient and reliable method for history matching with application to subsurface CO2 storage. The methods of quantifying uncertainties and parameter sensitivities in history matching can be divided into two classes: (1) statistics/stochastic-based approaches (e.g., in which multiple samples are drawn from conditional distributions) and (2) deterministic optimization-based approaches (e.g., in which a single optimal model is calibrated and some estimates of post-calibration covariance are provided). The current project will discuss both the approaches. The goal of this project is a comparison and hybridization of stochastic and optimization-based methods for uncertainty quantification in model calibration and history matching, thus combining the best aspects of both worlds.

工程师们越来越多地注意到自然地下的各种不同的、可能是相互竞争的应用。一方面，地下蕴藏着自然资源。另一方面，它用于临时或永久储存废物和气体。对于所有这些相互竞争的使用类型，我们的社会评估它们的性能、局限性、风险和相互限制是必不可少的。模型预测的质量很大程度上取决于模型参数的质量。在该方案中，我们主要关注地下储气，特别是深层盐层的二氧化碳储气，因为我们在该领域拥有强大的背景和经验。然而，我们强调，在这个工程应用领域应用和开发的方法可以以一种直接的方式转移到其他相关领域。从以往的研究中我们知道，与储气或流体注入相关的地下模拟过程的主要预测误差和不确定性来自地下结构和材料参数的不确定性。最近的一个例子是对德国勃兰登堡州的Ketzin试验储存场址进行建模和模拟。开展了一项全面的勘探和监测方案，以便根据最新技术状况提供尽可能最好的数据。在本方案中最重要的是观测数据的历史匹配，即（,）主要是观测到的两口观测井的压力时间序列和注入CO2到达时间。模型需要对储层的未来动态具有更强的预测能力，以便为管理注入和储存提供强大的决策支持。本课题旨在开发计算高效、可靠的历史拟合方法，并将其应用于地下CO2封存。历史匹配中不确定性和参数敏感性的量化方法可分为两类：(1)基于统计/随机的方法（例如，从条件分布中提取多个样本）和(2)基于确定性优化的方法（例如，校准单个最优模型并提供一些校准后协方差的估计）。当前的项目将讨论这两种方法。本项目的目标是比较和融合随机和基于优化的方法在模型校准和历史匹配中的不确定性量化，从而结合两者的最佳方面。