基于贝叶斯推理的地质力学与渗流耦合模拟中的参数反演

Juarez S. Azevedo , Jarbas A. Fernandes
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引用次数: 0

摘要

在许多情况下,由于缺乏数据和空间变化,周围土壤的力学性质存在不确定性,需要使用通过随机变量或随机函数研究参数的工具。通常只有一些参数的测量值,如渗透率或孔隙度,可用于建立模型,并且需要一些地质力学行为的测量值(如位移、应力和应变)来检查/校准模型。为了在地质力学分析中引入这种类型的建模,考虑到土壤参数的随机性,在高度不均匀的多孔介质中实现了贝叶斯推理技术。在耦合算法的框架内,这些被纳入反孔弹性问题中,孔隙度、渗透率和杨氏模量被视为通过移动平均(MA)方法获得的平稳随机场。为此,选择Metropolis–Hasting(MH)算法来寻找产生最低失配的地质力学参数。在三维域中进行了与注入问题和流体抽取相关的数值模拟,以比较该方法的性能。最后,我们对数值实验做了一些评论。
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The parameter inversion in coupled geomechanics and flow simulations using Bayesian inference

In many situations, uncertainty about the mechanical properties of surrounding soils due to the lack of data and spatial variations requires tools that involve the study of parameters by means of random variables or random functions. Usually only a few measurements of parameters, such as permeability or porosity, are available to build a model, and some measurements of the geomechanical behavior, such as displacements, stresses, and strains are needed to check/calibrate the model. In order to introduce this type of modeling in geomechanical analysis, taking into account the random nature of soil parameters, Bayesian inference techniques are implemented in highly heterogeneous porous media. Within the framework of a coupling algorithm, these are incorporated into the inverse poroelasticity problem, with porosity, permeability and Young modulus treated as stationary random fields obtained by the moving average (MA) method. To this end, the Metropolis–Hasting (MH) algorithm was chosen to seek the geomechanical parameters that yield the lowest misfit. Numerical simulations related to injection problems and fluid withdrawal in a 3D domain are performed to compare the performance of this methodology. We conclude with some remarks about numerical experiments.

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