CMG Research: Multiscale data integration using facies based hierarchical Bayesian models

本项目致力于综合地质相模型的不确定性量化。在许多地质环境中，地下属性的分布主要受不同地质相的位置和分布控制，相边界上的属性形成鲜明对比。在这种情况下，沟槽的方位和沟槽几何形状决定了地下流动行为，而不是沟槽内特性的详细变化。传统的地下表征地统计学技术通常依赖于不能再现河道几何形状和相结构的变异函数。最近，人们提出了基于多点统计的地统计学模型来再现复杂的河道结构。这些方法依赖于难以获得的训练图像。在这个项目中，将开发连贯的贝叶斯分层模型，通过结合现有的静态和动态信息，以地质一致的方式保存相结构并填充相内的岩石物理性质。为了保持相特性的对比度，相边界将由水平集来表示，这些水平集表示各种相拓扑，包括相边界的分裂和合并。该方法依赖于贝叶斯分层方法来扰动相边界和物性，以匹配井中的动态流动和输送数据以及多相生产历史。该方法的一个新方面是选择了相边界的Langevian型扰动方案与多尺度模拟相结合，这使得我们能够在不牺牲收敛特性的情况下实现具有更高接受率的高效MCMC方法。基于相的分层形式易于实现高效的多尺度流动模拟，具有自适应性，可以显著提高流动和输运计算的速度。分层方法将自然地整合来自不同尺度的数据，并允许以本地硬数据和软数据为条件。合理开发基于贝叶斯模拟的算法将使我们能够在该模型的基础上进行后验推断来量化不确定性。该项目开发的基本思想、新的模型和算法将把定性的地质信息融入属性的定量空间建模中，从而极大地促进了当前地下表征的发展。这些反过来又将提高模拟、扩大和设计与环境补救、污染物运输和碳氢化合物储藏层/含水层中的二氧化碳封存有关的问题的能力。应用的重点将是枯竭油气藏中的二氧化碳封存。将二氧化碳封存到地质构造中是一种很有希望的解决方案，可以减少温室气体排放到地球大气中造成的环境危害。特别是，现有的和枯竭的石油和天然气储藏是二氧化碳封存的有吸引力的候选者，主要原因有两个。首先，通过注入二氧化碳来提高石油采收率的经济效益得到了商业证明，并得到了行业的广泛实践。其次，油气藏可能为任何潜在的二氧化碳封存项目的地下特征、设计和性能评估提供丰富的数据源。

This project focuses on uncertainty quantification for integrated geologic facies models. In many geologic environments, the distribution of subsurface properties is primarily controlled by the location and distribution of distinct geologic facies with sharp contrasts in properties across facies boundaries. Under such conditions, the orientation of the channels and channel geometry determine the flow behavior in the subsurface rather than the detailed variation in properties within the channels. Traditional geostatistical techniques for subsurface characterization have typically relied on variograms that are unable to reproduce the channel geometry and the facies architecture. Recently geostatistical models based on multiplepoint statistics have been proposed for reproduction of complex channel architecture. These methods rely on training images that can be difficult to obtain. In this project coherent Bayesian hierarchical models will be developed which will preserve the facies architecture and populate the petrophysically properties within the facies in a geologically consistent manner by incorporating available static and dynamic information. To maintain the contrast in facies properties, facies boundaries will be represented by level sets which represent variety of facies topology including splitting and merging of facies boundaries. The method relies on a Bayesian hierarchical approach to perturb the facies boundaries and properties to match the dynamic flow and transport data and multiphase production history at the wells. A novel aspect of the approach is the choice of a Langevian-type proposal perturbation of facies boundaries combined with multiscale simulations that allows us to implement efficient MCMC methods with higher acceptance rate without sacrificing the convergence characteristics. The facies based hierarchical formalism lends itself readily to efficient multiscale flow simulation with adaptivity that can provide significant speed up in the flow and transport calculations. The hierarchical approach will naturally integrate data from different scales and allow to condition on local hard and soft data. Proper exploitation of Bayesian simulation based algorithm will enable us to perform posterior inference to quantify uncertainty based on this model.The basic idea, novel models and algorithms developed by the project will significantly advance the current state-of-the-art in subsurface characterization by incorporating qualitative geological information into quantitative spatial modeling of properties. These, in turn, will improve the ability to model, scale-up and design problems related to environmental remediation, contaminant transport and CO2 sequestration in hydrocarbon reservoirs/aquifers. The focus of the application will be on CO2 sequestration in depleted hydrocarbon reservoirs. The sequestration of CO2 into geologic formations is a promising solution for reducing environmental hazards created by the release of green house gases in to the earth's atmosphere. In particular, existing and depleted oil and gas reservoirs are attractive candidates for CO2 sequestration for two principal reasons. First, the economic benefits associated with enhanced oil recovery through CO2 injection are commercially proven and widely practiced by the industry. Second, oil and gas reservoirs are likely to provide abundant data sources for subsurface characterization, design and performance assessment of any potential CO2 sequestration project.