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.

工程师越来越多地引起了自然地下的通知，以实现非常不同的应用程序类型。一方面，地下包含自然资源。另一方面，它用于暂时或永久存储废物和天然气。对于所有这些相互竞争的使用类型，我们的社会评估其绩效，局限性，风险和相互限制是必不可少的。模型预测的质量在很大程度上取决于模型参数的质量。在该提案中，我们重点关注地下的气体存储，特别是我们专注于深盐水形成中的二氧化碳存储，因为我们在该领域具有强大的背景和经验。但是，我们强调，用于此工程应用领域应用和开发的方法可以直接转移到其他相关领域。从以前的研究中可以知道，在与储气相关的地下中模拟过程中的主要预测错误和不确定性，或者与注入液体相关的更一般是源于地下结构和材料参数的不确定性。一个最新的例子是勃兰登堡/德国州的Ketzin Pilot存储站点的建模和模拟。为了根据最新状态提供最佳数据，已经进行了全面的探索和监测计划。在该提案的背景下，最重要的是观察数据的历史匹配，即（）主要观察到的压力的时间序列和在两个观察井中注入CO2的到达时间。要求模型具有提高信心的储层的未来行为的预测能力，以便可以用来为管理注入和存储提供强大的决策支持。该建议旨在开发计算高效且可靠的方法，以与应用于地下CO2存储的应用相匹配。量化历史匹配中不确定性和参数敏感性的方法可以分为两类：（1）基于统计/随机的方法（例如，从条件分布中绘制了多个样本）和（2）基于确定性优化的方法（例如，在其中校准了单个最佳模型，并提供了一些后校准协方差的估计值）。当前项目将讨论这两种方法。该项目的目的是对基于模型校准和历史匹配中不确定性定量的随机和优化方法的比较和杂交，从而结合了两个世界的最佳方面。