Uncertainty quantification in reservoirs with faults using a sequential approach
Uncertainty quantification in reservoirs with faults using a sequential approach
复制标题
使用序贯方法对断层储层的不确定性进行量化
DOI：
10.1007/s10596-020-10021-2
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发表时间：
2021
影响因子：
2.5
通讯作者：
Dawson, Clint
中科院分区：
文献类型：
--
作者：
Estes, Samuel;Dawson, Clint
油藏模拟对于油藏的优化管理至关重要。通常，模型的许多参数是未知的，不能直接测量。然后，必须从威尔斯井的生产数据推断这些参数。这是一个逆问题，可以在贝叶斯框架内将先验知识与观测数据相结合。马尔可夫链蒙特卡罗（MCMC）方法通常用于通过生成一组可用于表征后验分布的样本来解决贝叶斯逆问题。在这项工作中，我们提出了一种新的MCMC算法，它使用了顺序过渡内核，旨在利用冗余，这是经常出现在时间序列数据从水库。该方法可以有效地生成样本的贝叶斯后验的时间依赖模型。虽然这种方法是通用的，可以用于许多不同的模型。我们考虑一个贝叶斯逆问题，其中我们希望推断故障transmissance从测量的压力在威尔斯使用两相流模型。我们演示了如何顺序MCMC算法在这里可以更有效地比一个标准的大都会黑斯廷斯MCMC方法，这个逆问题。我们使用集成的自相关时间沿着与均方跳跃距离，以确定每种方法的逆问题的性能。
Reservoir simulation is critically important for optimally managing petroleum reservoirs. Often, many of the parameters of the model are unknown and cannot be measured directly. These parameters must then be inferred from production data at the wells. This is an inverse problem which can be formulated within a Bayesian framework to integrate prior knowledge with observational data. Markov Chain Monte Carlo (MCMC) methods are commonly used to solve Bayesian inverse problems by generating a set of samples which can be used to characterize the posterior distribution. In this work, we present a novel MCMC algorithm which uses a sequential transition kernel designed to exploit the redundancy which is often present in time series data from reservoirs. This method can be used to efficiently generate samples from the Bayesian posterior for time-dependent models. While this method is general and could be useful for many different models. We consider a Bayesian inverse problem in which we wish to infer fault transmissibilities from measurements of pressure at wells using a two-phase flow model. We demonstrate how the sequential MCMC algorithm presented here can be more efficient than a standard Metropolis-Hastings MCMC approach for this inverse problem. We use integrated autocorrelation times along with mean-squared jump distances to determine the performance of each method for the inverse problem.
DOI：
10.1201/9781482296426
发表时间：
2006-05
期刊：
--
影响因子：
--
作者：
D. Gamerman;H. Lopes
通讯作者：
D. Gamerman;H. Lopes
通过多点、模拟和混合方法对故障进行精确建模
影响因子：
3.6
作者：
H. Nilsen;J. R. Natvig;Knut
通讯作者：
Knut
DOI：
10.1002/nme.2579
发表时间：
2009-09-10
影响因子：
2.9
作者：
Geuzaine, Christophe;Remacle, Jean-Francois
通讯作者：
Remacle, Jean-Francois