Open Access

ARTICLE

An Improved Parameter Dimensionality Reduction Approach Based on a Fast Marching Method for Automatic History Matching

Hairong Zhang¹, Yongde Gao², Wei Li², Deng Liu^3,*, Jing Cao³, Luoyi Huang³, Xun Zhong³

1 Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang Bay Laboratory, Zhanjiang, 524000, China
2 Zhanjiang Branch of China National Offshore Oil Corporation, Zhanjiang, 524000, China
3 College of Petroleum Engineering, Yangtze University, Wuhan, 430100, China

* Corresponding Author: Deng Liu. Email:

(This article belongs to the Special Issue: Meshless, Mesh-Based and Mesh-Reduction Methods Based Analysis of Fluid Flow in Porous Media)

Fluid Dynamics & Materials Processing 2022, 18(3), 609-628. https://doi.org/10.32604/fdmp.2022.019446

Received 25 September 2021; Accepted 26 October 2021; Issue published 22 February 2022

Abstract

History matching is a critical step in reservoir numerical simulation algorithms. It is typically hindered by difficulties associated with the high-dimensionality of the problem and the gradient calculation approach. Here, a multi-step solving method is proposed by which, first, a Fast marching method (FMM) is used to calculate the pressure propagation time and determine the single-well sensitive area. Second, a mathematical model for history matching is implemented using a Bayesian framework. Third, an effective decomposition strategy is adopted for parameter dimensionality reduction. Finally, a localization matrix is constructed based on the single-well sensitive area data to modify the gradient of the objective function. This method has been verified through a water drive conceptual example and a real field case. The results have shown that the proposed method can generate more accurate gradient information and predictions compared to the traditional analytical gradient methods and other gradient-free algorithms.

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Keywords

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