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

CMG 研究：使用基于相的分层贝叶斯模型进行多尺度数据集成

• 批准号: 0724704
• 负责人: Bani Mallick
• 金额: $ 65万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0724704&HistoricalAwards=false
• 关键词: CMG; Research; Multiscale; data; integration

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.

本项目的重点是综合地质相模型的不确定性量化。在许多地质环境中，地下性质的分布主要由不同地质相的位置和分布控制，在相边界上具有鲜明的性质对比。在这样的条件下，通道的取向和通道几何形状决定了地下的流动行为，而不是通道内性质的详细变化。地下表征的传统地质统计学技术通常依赖于变异函数，其无法再现河道几何形状和相结构。近年来，基于多点统计的地质统计模型被提出来用于复杂河道结构的再现。这些方法依赖于可能难以获得的训练图像。在本项目中，将开发连贯的贝叶斯分层模型，该模型将通过结合可用的静态和动态信息，以地质一致的方式保留相结构并填充相内的岩石物理特性。为了保持相属性的对比度，相边界将由水平集表示，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性质，以匹配威尔斯井处的动态流动和输送数据以及多相生产历史。该方法的一个新的方面是选择一个Langevian型的建议相边界扰动结合多尺度模拟，使我们能够实现高效的MCMC方法，具有较高的接受率，而不牺牲收敛特性。 基于相的分层形式主义本身很容易有效的多尺度流动模拟与自适应性，可以提供显着的速度在流动和输运计算。分层方法将自然地整合来自不同尺度的数据，并允许对本地硬数据和软数据进行调整。适当利用贝叶斯模拟算法将使我们能够执行后验推理，以量化基于此模型的不确定性。该项目开发的基本思想，新的模型和算法将显着推进当前的地下表征的最新技术，将定性地质信息纳入定量空间建模的属性。这些反过来又将提高模拟、扩大和设计与环境补救、污染物迁移和碳氢化合物储层/含水层中的二氧化碳封存有关的问题的能力。应用的重点将是在枯竭的油气藏中的CO2封存。将CO2封存到地质构造中是减少由绿色室内气体释放到地球大气中所造成的环境危害的有希望的解决方案。特别是，现有的和枯竭的石油和天然气储层是有吸引力的候选人的二氧化碳封存的两个主要原因。首先，与通过注入CO2提高石油采收率相关的经济效益在商业上得到了证实，并被工业界广泛实践。其次，石油和天然气储层可能为任何潜在的CO2封存项目的地下表征、设计和性能评估提供丰富的数据源。