Knowledge to application: meta data approaches to improved geological model conditioning in petroleum industry workflows

知识应用：用于改进石油工业工作流程中地质模型调节的元数据方法

基本信息

批准号：
NE/M007324/1
负责人：
William McCaffrey
金额：
$ 8.81万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FM007324%2F1
关键词：
Knowledge application meta data approaches

项目摘要

Sedimentary rocks are commonly highly variable internally. For example when rivers leave behind sediments, channel deposits may sit within floodplain deposits, such that ribbons of sand become encased within a muddy background. To describe and study sedimentary systems such as these, sedimentologists use descriptive terms such as "channel" or "floodplain" to recognise building blocks that together stack up to build a rock volume. These building blocks are called architectural elements. Geologists also recognise that architectural elements may themselves stack up in organised patterns, and such organised patterns of elements can be placed in hierarchical arrangements. For example, channels may stack together to form channel complexes, and channel complexes may build channel complex sets. This hierarchical descriptive approach can work in the deposits from a wide range of sedimentary environments: e.g., rivers (fluvial rocks), shallow seas (shallow marine rocks) and deep seas (deep marine clastic rocks). Sedimentary systems built from pre-existing particles (such as sand grains, or mud grains, that might make sand- or mud-stones) are known as clastic systems. In the right circumstances, such systems may form hydrocarbon reservoirs. This happens when the sediment is buried, but with connected pores between the grains still open (i.e., without minerals growing in the pores during burial to seal the rock). If oil or gas migrates into connected pore spaces, but cannot migrate out, because the rock above is sealed in some way, the hydrocarbon filled rock volume may have the potential to be an oil or gas reservoir, if there is enough hydrocarbon in place, and the predicted flow rates are high enough.To predict whether a known hydrocarbon accumulation might make an economic reservoir - i.e., be worth developing - oil companies model its behaviour as a possible field. They may first build a sedimentary model, recognising different architectural elements and the way they are stacked together. They will then account for any deformation the rock experienced during or post deposition. Finally they will simplify this geological model to build a reservoir simulation model, in which the performance of the possible field can be predicted. Ideally the company would build the geological model using seismic reflection data that showed the basic geology (this is a remote sensing technique that builds 2- or 3D images of the subsurface geology based on processing the reflections of sound waves sent into the ground). However, the resolution of these techniques is usually not good enough to show exactly what the subsurface geology is like, and the companies have to use models to fill in the data they can't see directly. To build these models, companies commonly use computer techniques to generate synthetic geology at the finer scale, using algorithms that randomly generate patterns of architectural elements based upon modelling rules. However, problems often arise because these rules are not always based upon the way the geology actually tends to stack together in particular settings. The database approach is a new way of determining what these organisational rules should be. They provide data that are more reliable, because the data are all compiled from real world examples of geology. We have already applied this approach to rocks deposited by rivers, and in the deep sea, and many companies have used the results in their own modelling. However, the approach hasn't been tried yet for shallow marine rocks - and that's what we aim to do in this project. Shallow marine rocks host many oil and gas fields, so if we can improve the modelling of such fields, we'll have a significant impact upon the efficiency of the companies who use the technique, as we'll reduce the uncertainly they commonly experience when deciding whether or not to develop a field, and how to extend the lives of fields that are already producing.

沉积岩通常在内部变化很大。例如，当河流留在沉积物后面时，通道沉积物可能位于洪泛区沉积物中，以便在泥泞的背景中包裹着沙子。为了描述和研究诸如此类的沉积系统，沉积学家使用描述性术语，例如“渠道”或“洪泛区”来识别构建块，这些块共同堆叠以建造岩石量。这些构件称为建筑元素。地质学家还认识到，建筑要素本身可能堆积在有组织的模式中，并且这种有组织的元素模式可以放置在层次结构中。例如，通道可能会堆叠在一起以形成通道复合物，并且通道复合物可以构建通道复合体集。这种分层描述方法可以在各种沉积环境的沉积物中起作用：例如，河流（河岩），浅海（浅海岩（浅海岩）和深海（深海洋碎屑岩）。由先前存在的颗粒（例如沙粒或可能形成沙子或泥岩的泥土）构建的沉积系统被称为碎屑系统。在正确的情况下，这种系统可能形成碳氢化合物储层。当沉积物被埋葬时，这种情况就会发生，但是谷物之间的孔连接在一起（即，在埋葬期间没有矿物质在孔中生长以密封岩石）。如果石油或天然气迁移到连接的孔隙空间中，但不能迁移，因为上面的岩石以某种方式密封，则碳氢化合物填充的岩石体积可能有可能是石油或天然气储层，如果有足够的碳氢化合物，并且预测的流速足够高，并且可以预测已知的碳氢化合物积累是否能够使企业成为现场，那么它可能会制造出一种机油，即candy -evory -of Origoy -evory -of Or Oil，oil -ecoil ass of Or Oil，oil.e.e.e.e.e.e.e.e.ee of of Or。他们可能首先建立一个沉积模型，认识到不同的建筑要素以及它们在一起的方式。然后，他们将说明岩石在沉积期间或后沉积期间经历的任何变形。最后，他们将简化这种地质模型来构建储层模拟模型，其中可以预测可能的领域的性能。理想情况下，该公司将使用显示基本地质的地震反射数据来构建地质模型（这是一种基于处理发送到地面的声波的反射的遥感技术，它构建地下地质的2或3D图像）。但是，这些技术的分辨率通常不够好，无法准确显示地下地质的样子，并且公司必须使用模型来填写他们看不到的数据。为了构建这些模型，公司通常使用计算机技术在细微的规模上生成合成地质，并使用基于建模规则随机生成建筑元素模式的算法。但是，问题通常会出现，因为这些规则并不总是基于地质实际倾向于在特定设置中堆叠在一起的方式。数据库方法是确定这些组织规则应该是什么的新方法。它们提供了更可靠的数据，因为数据都是从现实世界的地质示例中汇编而成的。我们已经将这种方法应用于河流沉积的岩石，并在深海中，许多公司都在自己的建模中使用了结果。但是，该方法尚未尝试用于浅水岩石 - 这就是我们在这个项目中要做的事情。浅水岩拥有许多石油和天然气场，因此，如果我们可以改善此类领域的建模，我们将对使用该技术的公司的效率产生重大影响，因为我们将在决定是否发展领域时会降低它们通常会经历的不确定的经验，以及如何延长已经生产的领域的生活。