Towards an automated process for assessing reservoir rock quality from seismic imaging

该项目将为利用可通过研究地球能源公司持有的全球地下数据集目录而获得的大量知识对地球资源进行自动化评估的培训模式奠定基础。这对地能业务产生了影响，一些公司目前正在裁员，减少了员工人数。然而，在过去的20年里，计算能力的进步使得大量的地下数据集的获取和发布成为可能，这些数据集提供了对哪里可能找到地球资源的更深层次的低估。这意味着，要有效地分析和挖掘现有大量地下数据集的全部潜力，自动化过程将变得越来越重要。该项目将侧重于对储层单元的分析。这些矿藏是一种非常有价值的地下资产，因为它们具有隆起的多孔空间，因此有可能在“碳捕获和储存”作业中包含碳氢化合物、水以及吸收二氧化碳。储集层单元中的孔隙空间大小，因此其储集流体和天然气的效率，是这类矿床的多种物理性质的函数。这些相同的物性也被认为控制了沉积物的整体形状和大小。在这个项目中，我们将分析一些储集层的形态和物性，以了解它们之间的相互关系。在这样做的时候，我们将能够通过查看地震数据来预测储集层的质量，这是业界用来提供地下图像的一种方法。该项目将提供一系列案例研究，记录特定的储集层形态如何指示储集层质量的好坏。这将为未来建立自动化过程的培训模型提供基础，该模型将能够有效地扫描大型地震数据集，寻找具有正确形状的岩石，以指示高质量的储集层。

This project will lay the basis of a training model for automated evaluation of georesources using the vast knowledge that can be acquired studying the global catalogue of subsurface datasets held by geo-energy companies.The current global energy climate is characterised by readily available primary fuels at a relatively low cost. This has impacted the geo-energy business with a number of companies currently downsizing with reduction of their workforce. Yet, advances in computational power in the last 20 years has allowed acquisition and releasing of extensive subsurface datasets which provide a deeper understating on where georesources might be found. This means that automated processes will be increasingly important to efficiently analyse and tap the full potential of extensive subsurface datasets now available.This project will focus on the analysis of reservoir units. These deposits represent a very valuable subsurface asset as they have elevated porous space, hence have the potential to contain hydrocarbons, water as well as to absorb carbon dioxide in 'Carbon Capture and Storage' operations. The amount of porous space in reservoir units, hence their efficiency to hold fluids and gas, is a function of a number of physical properties of such deposits. These same physical properties are also thought to control the overall shape and the size of the deposits.With this project, we will analyse the morphology and physical properties of a number of reservoirs so to understand how they are interrelated. In doing so, we would be able to predict the reservoir quality by looking at seismic data, which is a method used in the industry to provide an image of the subsurface. The project will deliver a series of case studies that document how specific reservoir morphologies would indicate good or bad reservoir quality. This would provide the basics to construct, in the future, a training model for automated processes that will be able to efficiently scan large seismic dataset in search of rock with the right shape that indicates a high-quality reservoir.