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

该项目将为地球资源自动评估的培训模型奠定基础，该模型将利用研究地球能源公司持有的全球地下数据集目录所获得的大量知识。当前全球能源气候的特点是成本相对较低的初级燃料易得。这已经影响了地能源业务，许多公司目前正在裁员。然而，在过去的20年里，计算能力的进步使得获取和发布大量的地下数据集成为可能，这些数据集可以更深入地了解地球资源的位置。这意味着自动化过程对于有效分析和充分挖掘现有大量地下数据集的潜力将变得越来越重要。本项目将重点分析水库单元。这些矿床是非常有价值的地下资产，因为它们具有较高的多孔空间，因此有可能含有碳氢化合物、水，并在“碳捕获和储存”操作中吸收二氧化碳。储层单元中孔隙空间的大小，即储层对流体和气体的储存效率，是储层若干物理性质的函数。这些相同的物理性质也被认为控制着沉积物的整体形状和大小。通过这个项目，我们将分析一些储层的形态和物理性质，以了解它们是如何相互关联的。这样，我们就可以通过查看地震数据来预测储层的质量，这是业内常用的一种提供地下图像的方法。该项目将提供一系列案例研究，记录特定的储层形态如何表明储层质量的好坏。这将为将来构建自动化过程的训练模型提供基础，该模型将能够有效地扫描大型地震数据集，以寻找具有正确形状的岩石，表明具有高质量的储层。

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.