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Effective approaches and solution techniques for conditioning, robust design and control in the subsurface

用于地下调节、鲁棒设计和控制的有效方法和解决技术

基本信息

批准号：
195436228
负责人：
Professor Dr. Hermann Georg Matthies
金额：
--
依托单位：
Department of Energy Resources Engineering
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2011
资助国家：
德国
起止时间：
2010-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/195436228?language=en
关键词：
Effective approaches solution techniques conditioning

项目摘要

When predicting processes in the subsurface, the need for uncertainty quantification and risk assessment is evident. Yet, this is merely the first within a full spectrum of tasks in stochastic modelling, which includes calibration, robust design, optimal monitoring and predictive control. Monte-Carlo simulation is the most simple and universally applicable option for stochastic modelling, but its computational costs become strictly prohibitive when joining it with the above follow-up tasks. Polynomial chaos expansions (PCE) are computationally much more efficient, and receive a quickly increasing attention. However, only little work has been done to make PCE available to the full spectrum of tasks. The proposed work will make PCE accessible for the full spectrum of tasks named above. We will develop a new, integrative and efficient framework, where all involved quantities will be treated via an overall functional approximation that represents the system’s behaviour within the entire range of un-certain parameters, design or control variables. Thus, the strongly increased computational costs of follow-up tasks will be drastically mitigated. We will further reduce storage requirements and improve computational efficiency via data-sparse and low-rank tensor representations throughout all tasks. The drastic gain in computational efficiency will finally allow tackling advanced follow-up tasks for full-scale, complex and real-world problems, even under uncertainty. We will demonstrate this by application to CO2 injection into the deep subsurface. Site characterization and selection, design and control of injection strategies under uncertainty, as well as optimal monitoring of CO2 leakage to the surface will be performed within the new framework, leading to better assessment, management and reduction of the involved risks.

在预测地下过程时，显然需要进行不确定性量化和风险评估。然而，这仅仅是随机建模中的一系列任务中的第一个，这些任务包括校准、鲁棒设计、最优监控和预测控制。蒙特-卡罗模拟是最简单和普遍适用的选择随机建模，但它的计算成本变得严格禁止时，将其与上述后续任务。多项式混沌展开（PCE）具有计算效率高的优点，受到了越来越多的关注。然而，只有很少的工作已经完成，使PCE提供给全方位的任务。拟议的工作将使PCE能够完成上述所有任务。我们将开发一个新的，综合的和有效的框架，其中所有涉及的数量将通过一个整体的功能近似，代表系统的行为在整个范围内的不确定参数，设计或控制变量。因此，后续任务的计算成本大幅增加将得到大幅缓解。我们将进一步降低存储需求，并通过所有任务中的数据稀疏和低秩张量表示来提高计算效率。计算效率的大幅提高最终将允许处理全面，复杂和现实世界问题的高级后续任务，即使在不确定性下。我们将证明这一点的应用到CO2注入到深地下。将在新的框架内进行场地定性和选择、不确定情况下注入战略的设计和控制，以及对CO2泄漏到地面的最佳监测，从而更好地评估、管理和减少所涉及的风险。