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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements
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A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

机译：利用时移地震测量技术有效恢复储层通道的稀疏贝叶斯成像技术

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摘要

Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the $ell _{2}$-norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.
机译：地下储层流道的特征在于高渗透率值，并作为流体传播的首选途径。因此，准确估算它们的地球物理结构对石油工业至关重要。集成卡尔曼滤波器（EnKF）是一种广泛使用的统计技术，用于估算地下储层模型参数。但是，使用EnKF准确重建地下地质特征具有挑战性，因为从井中获得的测量数据有限，并且更新步骤的$ ell _ {2} $-范数性质也带来了平滑效果。 Sana等人介绍了一种基于稀疏域表示的新EnKF方案。 （2015年）在评估过程中纳入有用的先验结构信息，以有效恢复地下通道。在本文中，我们通过两种方式扩展这项工作：1）研究时移地震数据的合并对通道重建的影响； 2）探索具有降低计算需求的潜在能力的贝叶斯稀疏重构算法。数值结果表明，新的基于稀疏贝叶斯的EnKF方案的性能随着地震测量的可用性而增强，从而进一步改善了流道结构的恢复。稀疏贝叶斯方法还提供了一种用于执行稀疏解的计算有效框架，尤其是通过包含地震数据来使用高稀疏率的可能性。

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