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对油藏进行表征。静态数据是指测井、岩心、地震和地质信息等时不变数据。动态数据是指压力瞬态响应、示踪数据、多相生产历史等时变信息。石油储层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，如水力导度（渗透率）、储度（孔隙度）和流体饱和度。然后将这些属性作为输入输入到由各种流动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“可信”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来再现复杂的地质/形态模式和在储层中观察到的各种各样的建筑非均质性。为了减少不确定性，将静态和动态数据源结合起来，得出一个综合的油藏描述。这项任务很重要，因为这些数据源跨越了不同的异构长度尺度，并且可能具有不同的精度。构建了一个层次贝叶斯模型，将不同尺度的数据在层次结构的不同阶段通过条件模型相互关联。为了保持极值属性的连通性，从而保证流体流动路径（通道）和屏障的优先性，利用划分模型探索了非平稳和非高斯方法。将模型扩展到具有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是明确可用的，因此将进行基于马尔可夫链蒙特卡罗的计算，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种系统的方法来使用不同的数据源来描述油藏。这项工作将有助于提高石油采收率，并通过更好地表征地下非均质性，对含水层修复方法的设计产生直接影响。目标问题具有实际的国家利益，因为更好地描述石油储层将导致更好的管理和开发战略，从而提高石油采收率。目前，国内大部分石油产量来自老油田和部分枯竭油田，这些油田的数据量很大。系统的方法可以更好地描述储层，这将对当前的行业实践产生直接影响。尽管最近人们对替代能源产生了兴趣，但对石油的需求仍在继续增加，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。国内油藏中确实仍有大量可采石油，但这些储量大多位于高度非均质油藏或偏远且昂贵的位置，例如墨西哥湾深水区。开采这些石油面临的挑战是：(1)能够绘制非均质性地图，以改善油藏管理的油井位置，并能够定位未被扫描的储量；(2)识别地图中的不确定性，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.