基于主成分分析和人工神经网络的数据同化

IF 2.1 4区 工程技术 Q3 ENERGY & FUELS
C. Maschio, G. Avansi, D. Schiozer
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引用次数: 0

摘要

利用油藏模拟模型减少不确定性的数据同化(DA)通常需要较高的计算时间;根据油藏模型的特点,运行一个油藏应用程序可能需要几天甚至几周的时间。因此,加快这一过程,使其在实际研究中更加可行,特别是那些需要大量仿真运行的研究。一种可能的方法是在某些耗时的过程中使用代理模型代替油藏模拟器。然而,代理模型固有的主要挑战是将3D地质统计实现(块对块的网格属性,如孔隙度和渗透率)作为代理构建中的不确定属性。在大多数情况下,由于高维问题,不可能将所有网格属性的值显式地视为代理构建过程的输入。本文提出了一种结合主成分分析(PCA)和人工神经网络(ANN)的数据分析方法来解决这一问题。应用主成分分析技术对问题进行降维处理,使网格属性在代理建模中的应用成为可能和可行的。训练后的人工神经网络用作油藏模拟器的代理,目的是减少应用程序的总计算时间。我们使用复杂的实际油藏模型运行了三个数据分析过程来验证该方法。第一个(DA1)用作参考解决方案,是DA方法显式更新所有网格属性值的常规过程。第二个(DA2)仅用于通过PCA验证提议的参数化。DA1和DA2都只使用油藏模拟器来生成油藏输出。在第三步(DA3)中,人工神经网络取代了水库模拟器以节省计算时间。重要的是,在DA3之后,结果(后验集合)与油藏模拟器进行了验证。DA3虽然比DA1的准确性略低,但总体结果还是不错的。因此,为决策者提供在常规方法(DA1)和建议的DA3之间进行选择的可能性似乎是合理的,前者通常更准确,但速度较慢,而后者比DA1快得多(总体效果良好)。这种选择可能取决于储层研究的目标、可用资源和进行研究的时间。本文的关键贡献在于提出了一种实用的数据分析方法,将PCA[用于降维(DR)]和ANN(用于减少计算时间)结合起来,适用于实际领域,填补了该研究领域文献的空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Assimilation Using Principal Component Analysis and Artificial Neural Network
Data assimilation (DA) for uncertainty reduction using reservoir simulation models normally demands high computational time; it may take days or even weeks to run a single reservoir application, depending on the reservoir model characteristics. Therefore, it is important to accelerate the process to make it more feasible for practical studies, especially those requiring many simulation runs. One possible way is by using proxy models to replace the reservoir simulator in some time-consuming parts of the procedure. However, the main challenge inherent in proxy models is the inclusion of 3D geostatistical realizations (block-to-block grid properties such as porosity and permeability) as uncertain attributes in the proxy construction. In most cases, it is impossible to treat the values of all grid properties explicitly as input to the proxy building process due to the high dimensionality issue. We present a new methodology for DA combining principal component analysis (PCA) with artificial neural networks (ANN) to solve this problem. The PCA technique is applied to reduce the dimension of the problem, making it possible and feasible to use grid properties in proxy modeling. The trained ANN is used as a proxy for the reservoir simulator, with the goal of reducing the total computational time spent on the application. We run three DA processes using a complex real-field reservoir model for validating the methodology. The first (DA1), used as the reference solution, is the conventional process in which the DA method updates all grid property values explicitly. The second (DA2) is only executed to validate the proposed parameterization via PCA. Both DA1 and DA2 use only the reservoir simulator to generate the reservoir outputs. In the third (DA3), the ANN replaces the reservoir simulator to save computational time. It is important to mention that after DA3, the results (the posterior ensemble) are validated with the reservoir simulator. The DA3, although a little bit less accurate than the DA1, allowed good overall results. Therefore, it seems reasonable to offer the decision-makers the possibility of choosing between the conventional approach (DA1), normally more accurate but slower, and the proposed DA3, much faster than DA1 (with overall good results). This choice may depend on the objective of the reservoir study, available resources, and time to perform the study. The key contribution of this paper is a practical methodology for DA combining PCA [for dimensional reduction (DR)] and ANN (for computational time reduction) applicable in real fields, filling a gap in the literature in this research area.
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
68
审稿时长
12 months
期刊介绍: Covers the application of a wide range of topics, including reservoir characterization, geology and geophysics, core analysis, well logging, well testing, reservoir management, enhanced oil recovery, fluid mechanics, performance prediction, reservoir simulation, digital energy, uncertainty/risk assessment, information management, resource and reserve evaluation, portfolio/asset management, project valuation, and petroleum economics.
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