CMG: Research on Multiscale Spatial Models for Petroleum Reservoir Mapping Using Static and Dynamic Data

该项目涉及使用不同尺度和精度的静态和动态数据进行油藏表征。 静态数据是指时不变数据，如测井、岩心、地震和地质信息。 动态数据是指随时间变化的信息，如压力瞬态响应、示踪剂数据和多相生产历史。 油气藏是一种复杂的地质构造，具有广泛的物理和化学非均质性。 储层表征的目标是提供储层属性的数值模型，如水力传导率（渗透率）、储集率（孔隙度）和流体饱和度。 然后，这些属性被用作输入到由各种流模拟器表示的复杂传递函数中，以匹配动态数据。 在预测未来储层动态时，必须有一个地质模型，该模型可以被认为是实际储层的“合理”复制品，具有可接受的不确定性。 为了实现这一目标，提出了几种灵活的空间建模方法，以重现复杂的地质/形态模式和各种各样的建筑非均质性观察到的水库。 为了减少不确定性，静态和动态数据源相结合，以获得综合油藏描述。 这项任务是不平凡的，因为这些数据源跨越不同的异质性长度尺度，并且可以具有不同的精确度。 一个分层贝叶斯模型的构建，其中来自不同尺度的数据将通过层次结构的不同阶段的条件模型相互关联。 为了保持极端值属性的连通性，从而保持优先的流体流动路径（通道）和障碍，使用分区模型探索非平稳和非高斯方法。 该模型被扩展到具有logit或probit结构的分类相数据。 由于问题的复杂性，未知参数的后验分布不太可能明确可用，并且将进行基于马尔可夫链蒙特卡罗的计算以从后验分布中抽取样本以量化不确定性。这项工作的目标是提供一个系统的方法，利用不同的数据源石油储层表征。 这项工作将有助于提高石油采收率，并通过更好地描述地下非均质性，对含水层补救方法的设计产生直接影响。 所针对的问题具有实际的国家利益，因为更好地描述储油层的特性将导致更好的管理和开发战略，从而提高石油采收率。 目前，国内大部分石油生产来自老油田和部分枯竭的油田，这方面有大量数据。 一个系统的方法，导致一个更好的油藏描述将有直接的影响，目前的工业实践。 尽管最近人们对替代能源很感兴趣，但对石油的需求继续增加，国内供需之间的严重失衡对国家安全和经济都是一个问题。 大量可开采的石油确实留在国内的储层中，但这些储量主要位于高度非均质的储层或偏远和昂贵的地点，如墨西哥湾的深水之下。 开采这些石油的挑战是：（1）能够绘制非均质性图，以改善威尔斯的位置，以便进行油藏管理，并能够找到未波及的储量;（2）识别绘图中的不确定性，以便评估风险，做出合理的经济决策。

This project concerns petroleum reservoir characterization using static and dynamic data with varying scales and precision. Static data refers to time-invariant data such as well logs, cores, and seismic and geologic information. Dynamic data refers to time-varying information such as pressure transient response, tracer data, and multiphase production history. Petroleum reservoirs are complex geological formations encompassing a wide range of physical and chemical heterogeneities. The goal of reservoir characterization is to provide a numerical model of reservoir attributes such as hydraulic conductivities (permeability), storativities (porosity), and fluid saturation. These attributes are then used as input into complex transfer functions represented by various flow simulators to match the dynamic data. In predicting future reservoir performance, it is imperative to have a geological model that can be considered a "plausible" replica of the actual reservoir with acceptable uncertainty. Towards this objective, several flexible spatial modeling approaches are proposed to reproduce complex geological/morphological patterns and the wide variety of architectural heterogeneities observed in reservoirs. To reduce the uncertainty, both static and dynamic data sources are combined to derive an integrated reservoir description. The task is non-trivial because these data sources span different length scales of heterogeneity and can have different degrees of precision. A hierarchical Bayesian model is constructed where the data from different scales will be related to each other by conditional models at different stages of the hierarchy. To preserve the connectivity of the extreme value attributes, and thus preferential fluid flow paths (channels) and barriers, non-stationary and non-Gaussian approaches are explored using partitioning models. The models are extended to categorical facies data with logit or probit structure. Due to the complexity of the problems, the posterior distributions of the unknown parameters are not likely to be explicitly available, and Markov Chain Monte Carlo based computations will be carried out to draw samples from the posterior distributions for quantifying uncertainty. The goal of this work is to provide a systematic approach to petroleum reservoir characterization using diverse data sources. This work will aid in improved oil recovery and will also have direct impact on the design of aquifer remediation methods through better characterization of subsurface heterogeneities. The targeted problem is of practical national interest, as better characterization of petroleum reservoirs will lead to better management and development strategies, leading to increased oil recovery. Currently most of the domestic oil production is from old and partially depleted fields, for which a large amount of data are available. A systematic approach leading to a better reservoir description will have a direct impact on the current industry practice. Despite recent interest in alternative sources of energy, the demand for oil has continued to increase, and the large imbalance between domestic supply and demand is an issue for both national security and the economy. Large amounts of recoverable oil do remain in domestic reservoirs, but those reserves are largely located in highly heterogeneous reservoirs or in remote and expensive locations, such as beneath the deep water of the Gulf of Mexico. The challenges for recovering this oil are to (1) be able to map heterogeneities to improve the locations of wells for reservoir management and to be able to locate unswept reserves, and (2) identify uncertainties in mapping so that risk can be assessed for sound economic decisions.