Stochastic reservoir characterization, a critical aspect of subsurface exploration for oil and gas reservoirs, relies on stochastic methods to model and understand subsurface properties using seismic data. This paper addresses the computational challenges associated with Bayesian reservoir inversion methods, focusing on two key obstacles: the demanding forward model and the high dimensionality of Gaussian random fields. Leveraging the generalized Bayesian approach, we replace the intricate forward function with a computationally efficient multivariate adaptive regression splines method, resulting in a 34 acceleration in computational efficiency. For handling high-dimensional Gaussian random fields, we employ a fast Fourier transform (FFT) technique. Additionally, we explore the preconditioned Crank-Nicolson method for sampling, providing a more efficient exploration of high-dimensional parameter spaces. The practicality and efficacy of our approach are tested extensively in simulations and its validity is demonstrated in application to the Alvheim field data.

翻译：随机储层表征是油气储层地下勘探的关键环节，它依赖随机方法，利用地震数据对地下属性进行建模和理解。本文针对贝叶斯储层反演方法中存在的计算挑战，聚焦两个关键障碍：计算成本高昂的正演模型和高斯随机场的高维特性。借助广义贝叶斯方法，我们用计算效率高的多元自适应回归样条方法替代复杂的正演函数，实现了34倍的计算效率提升。在处理高维高斯随机场方面，我们采用了快速傅里叶变换技术。此外，我们探索了预条件Crank-Nicolson采样方法，以实现对高维参数空间更高效的探索。通过大量模拟实验验证了本方法的实用性和有效性，并通过对Alvheim油田数据的实际应用证明了其可行性。