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

沉积岩通常内部变化很大。例如，当河流留下沉积物时，河道沉积物可能位于洪泛平原沉积物中，使得沙带被包裹在泥泞的背景中。为了描述和研究这样的沉积体系，沉积学家使用诸如“河道”或“河漫滩”之类的描述性术语来识别堆积在一起形成岩石体积的建筑块。这些构建块称为体系结构元素。地质学家还认识到，建筑元素本身可能会以有组织的模式堆叠起来，而这种有组织的元素模式可以被放置在层次结构中。例如，通道可以堆叠在一起以形成通道复合体，并且通道复合体可以构建通道复合体集合。这种分层描述方法可以适用于各种沉积环境的沉积物：例如，河流（河流岩石）、浅海（浅海岩石）和深海（深海碎屑岩）。由预先存在的颗粒（如砂粒或泥粒，可能形成砂岩或泥岩）构成的沉积系统被称为碎屑系统。在适当的情况下，这些系统可能形成油气藏。这发生在沉积物被掩埋时，但颗粒之间的连接孔仍然打开（即，在埋藏期间没有矿物在孔隙中生长以密封岩石）。如果石油或天然气迁移到连通的孔隙空间中，但不能迁移出来，因为上面的岩石以某种方式被密封，如果在适当的位置有足够的烃，并且预测的流速足够高，则烃填充的岩石体积可能具有成为石油或天然气储层的潜力。值得开发-石油公司将其行为作为一个可能的领域。他们可能首先建立一个沉积模型，识别不同的建筑元素和它们堆叠在一起的方式。然后，它们将解释岩石在沉积期间或沉积后经历的任何变形。最后，他们将简化这个地质模型，建立一个油藏模拟模型，在其中可以预测可能的油田的动态。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（这是一种遥感技术，基于处理发送到地面的声波反射来构建地下地质的二维或三维图像）。然而，这些技术的分辨率通常不足以准确显示地下地质情况，公司必须使用模型来填充他们无法直接看到的数据。为了建立这些模型，公司通常使用计算机技术来生成更精细规模的合成地质，使用基于建模规则随机生成建筑元素模式的算法。然而，问题往往出现，因为这些规则并不总是基于地质实际上倾向于在特定环境中叠加在一起的方式。数据库方法是确定这些组织规则应该是什么的一种新方法。它们提供的数据更可靠，因为这些数据都是从真实的地质学实例中汇编而成的。我们已经将这种方法应用于河流和深海沉积的岩石，许多公司也在自己的建模中使用了该结果。然而，这种方法还没有在浅海岩石中尝试过-这就是我们在这个项目中的目标。浅海岩石承载着许多石油和天然气田，因此如果我们能够改进这些油田的建模，我们将对使用该技术的公司的效率产生重大影响，因为我们将减少他们在决定是否开发油田时通常遇到的不确定性，以及如何延长已经生产的油田的寿命。

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.