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

通过地震成像评估储层岩石质量的自动化过程

• 批准号: NE/R013411/1
• 负责人: Nicola Scarselli
• 金额: $ 4.19万
• 依托单位: Royal Holloway University of London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR013411%2F1
• 关键词: Towards; automated; process; assessing; reservoir

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.

该项目将利用研究地球能源公司持有的全球地下数据集目录所能获得的大量知识，为自动评估地球资源的培训模型奠定基础。这影响了地球能源业务，许多公司目前正在裁员。然而，在过去20年中，计算能力的进步已经允许获取和发布大量的地下数据集，这些数据集可以更深入地了解地质资源的位置。这意味着自动化流程对于有效分析和挖掘现有大量地下数据集的全部潜力将变得越来越重要。本项目将侧重于油藏单元的分析。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，因此有可能包含碳氢化合物，水以及在“碳捕获和储存”操作中吸收二氧化碳。储层单元中的多孔空间的量，因此它们保持流体和气体的效率，是这种沉积物的许多物理性质的函数。这些相同的物理性质也被认为控制了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，以便了解它们是如何相互关联的。在这样做的过程中，我们将能够通过查看地震数据来预测储层质量，这是一种在行业中用于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，以寻找具有指示高质量储层的正确形状的岩石。

项目成果

Exploring the Predictive Power of Seismic Geomorphology to Assess Reservoir Quality of Gravity-Flow Sandstones.

探索地震地貌预测重力流砂岩储层质量的能力。

• 发表时间: 2018
• 作者: Scarselli N

Exploring the predictive power of seismic geomorphology to assess reservoir quality of deepwater gravity-flow sandstones. A step towards automated interpretation of seismic data?

探索地震地貌学的预测能力来评估深水重力流砂岩的储层质量。

• 发表时间: 2018
• 作者: Scarselli N

Exploring the predictive power of seismic geomorphology to assess sedimentary characteristics of gravity-flow deposits: examples from offshore East and West Africa reservoirs

探索地震地貌学的预测能力以评估重力流沉积物的沉积特征：东非和西非近海水库的例子

• DOI: 10.1144/sp525-2021-58
• 发表时间: 2022
• 期刊: Geological Society, London, Special Publications
• 作者: Scarselli N