Smart Pulses for Subsurface Engineering

用于地下工程的智能脉冲

• 批准号: EP/S005560/1
• 负责人: Zoe Shipton
• 金额: $ 329.79万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS005560%2F1
• 关键词: Smart; Pulses; Subsurface; Engineering

Geological engineering encompasses a range of applications from resource extraction (hydrocarbons, geothermal heat and power, water) to waste disposal (Carbon capture and storage, wastewater disposal) and energy storage (compressed air, hydrogen). All of these technologies rely on pumps to move fluid into or out of boreholes. This prosperity partnership brings together teams that have previously worked on pumps for well stimulation with new team members involved in geomechanics and monitoring systems. Our previous work has shown that the pumps used in well stimulation are often used in very simple ways to deliver a known pressure to the top of the wellbore, leading to inefficient processes that produce a lot of noise and waste. Our partnership aims to re-engineer such systems through three linked research themes. Firstly there is evidence that pulses in pressure or dynamic variations in mean pressure could be more effective in achieving the aims of geological engineering processes. To understand the potential of pulsed pumping we need a deeper understanding of the material response to dynamic variation of the system that is being pumped: the rock mass and the borehole (casing and cement). Secondly we need to understand how to control delivery of precise pressure variations into the borehole and how to monitor these as they travel down the bore and into the rock mass. This includes the need to monitor rock mass response to develop fully 'closed loop' control systems. Finally we want to integrate the systems understanding of the pumps, the pumped system and the control systems. We will trial our new pulse propagation and monitoring system in the UK (at a site where well stimulation will not take place) and test the new monitoring system at an active well stimulation site in N. America. A series of eight linked PhD projects will explore aspects of the problems, and investigate the application of smart pumping to other sectors such as water distribution systems or transport of mining slurry. Our overall goal is to reduce the cost and increase the efficiency of geological engineering through smart pumping, thereby reducing the environmental and social impact of such technologies. We have brought together a partnership of two industry and two university partners. The Weir Group and University of Strathclyde have a long history of collaboration on well stimulation pumps and other applications. The University of Edinburgh bring unique, world-leading geomechanical experimental capability to the partnership, and have previously collaborated with Strathclyde on carbon storage and compressed air energy storage. Silixa are young company specialising in optical fibres for sensing. Together this partnership will conduct the research that will underpin the development of smarter technologies in pumping and geological engineering.

地质工程包括一系列应用，从资源开采（碳氢化合物，地热和电力，水）到废物处理（碳捕获和储存，废水处理）和能源储存（压缩空气，氢气）。所有这些技术都依赖于泵将流体移入或移出钻孔。这种繁荣的合作伙伴关系将以前从事油井增产泵工作的团队与参与地质力学和监测系统的新团队成员聚集在一起。我们之前的工作表明，用于油井增产的泵通常以非常简单的方式使用，以将已知压力输送到井筒顶部，导致产生大量噪音和浪费的低效过程。我们的合作旨在通过三个相关的研究主题重新设计这些系统。首先，有证据表明，压力的脉冲或平均压力的动态变化可以更有效地实现地质工程过程的目标。为了了解脉冲泵送的潜力，我们需要更深入地了解材料对被泵送系统的动态变化的响应：岩体和钻孔（套管和水泥）。其次，我们需要了解如何控制精确的压力变化进入钻孔，以及如何在压力变化沿钻孔向下进入岩体时对其进行监测。这包括需要监测岩体响应，以开发完全的“闭环”控制系统。最后，我们希望整合对泵、泵送系统和控制系统的系统理解。我们将在英国（在不进行油井增产措施的地点）试用我们的新脉冲传播和监测系统，并在N.美国参考一系列八个相关的博士项目将探讨这些问题的各个方面，并研究智能泵送在其他领域的应用，如配水系统或采矿泥浆的运输。我们的总体目标是通过智能泵送降低地质工程的成本和提高效率，从而减少此类技术对环境和社会的影响。我们汇集了两个行业和两所大学的合作伙伴。威尔集团和斯特拉斯克莱德大学在油井增产泵和其他应用领域有着悠久的合作历史。爱丁堡大学为合作伙伴关系带来了独特的、世界领先的地质力学实验能力，此前曾与斯特拉斯克莱德大学在碳储存和压缩空气储能方面进行过合作。Silixa是一家年轻的公司，专门从事光纤传感。这一合作伙伴关系将共同开展研究，为泵送和地质工程领域更智能技术的发展奠定基础。

项目成果

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

• DOI: 10.1109/tgrs.2020.3032664
• 发表时间: 2021-09-01
• 期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• 影响因子: 8.2
• 作者: Li, Jiangfeng;Stankovic, Lina;Stankovic, Vladimir
• 通讯作者: Stankovic, Vladimir

Microseismic event classification with time, frequency and wavelet domain convolutional neural networks

使用时间、频率和小波域卷积神经网络进行微震事件分类

• DOI: 10.17868/strath.00084907
• 发表时间: 2023
• 作者: Jiang J

Domain Knowledge Informed Multitask Learning for Landslide-Induced Seismic Classification

滑坡诱发地震分类的领域知识告知多任务学习

• DOI: 10.1109/lgrs.2023.3279068
• 发表时间: 2023
• 期刊: IEEE Geoscience and Remote Sensing Letters
• 影响因子: 4.8
• 作者: Li J

Rate-dependence of the compressive and tensile strength of granites

• DOI: 10.5194/adgeo-62-11-2023
• 发表时间: 2023-10
• 期刊: Advances in Geosciences
• 作者: J. Kendrick;A. Lamur;Julien Mouli-Castillo;A. Fraser-Harris;A. Lightbody;K. Edlmann;Christopher McDermott-Christ

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings.

• DOI: 10.3390/s23010243
• 发表时间: 2022-12-26
• 期刊: Sensors (Basel, Switzerland)
• 作者: Li J;Stankovic L;Stankovic V;Pytharouli S;Yang C;Shi Q
• 通讯作者: Shi Q