Development of mixed field radiation detection techniques for oil and gas well logging.

油气测井混合场辐射探测技术发展

• 批准号: 2576737
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2576737
• 关键词: Development; mixed; field; radiation; detection

The NuSec-mediated workshop in January 2018 produced a report in which the need to develop alternative detection and data interpretation methods in well logging was highlighted.The detector development group at the University of Sheffield has a long track record of detector development for a range of particles including muons, neutrons and gammas. Our recent STFC-supported work on the development of neutron-sensitive detectors for water content monitoring has resulted in new expertise in the fabrication of neutron- sensitive foils being developed in house. This new capability sits alongside previous expertise in prototyping borehole detectors for CCS monitoring. Furthermore, due to a long-standing relationship with LabLogic we have access to a scintillator fabrication facility enabling short turnaround prototyping and development cycles. Along with our range of neutron sources (pulsed DT, AmBe, 252Cf) this places us in a unique position to be able to develop low-cost plastic scintillator and neutron-sensitive foil based detection systems for application in borehole configurations.Via the studentship, we intend to explore the possibility of using scintillator blocks doped with different materials to produce finely segmented radiation detectors that can fit down a standard borehole. There are several advantages to this approach over standard methods, e.g.:- use of a single detector that covers mixed fields using position-sensitive detection/ coincidence. On-site expertise in many different detector types is thus not required;- deployment of a finely segmented detector made of many similar stacked "modules"or "blocks" means the system has built in redundancy;- the time from initial neutron generator pulse to detection of neutrons/gammas canprovide additional fine grained information not available when using radiological sources.A further issue with current well logging technologies, as noted in papers, is the analysis of data. Gamma logs, e.g., are used as almost a qualitative measure. Similarly, neutron porosity measurements require further calibration to be effective. The inclusion of pulsed generators and associated particle detection yields supplementary information, however, this is at the cost of additional processing time and training. A solution for this is to use machine learning to extract quantities of interest, e.g. the total detected neutron/gamma counts from a borehole detector observed in a gated window after the initial generator pulse would serve as suitable input to a multivariate analysis (MVA) method such as a neural network. Such an MVA could be trained, via simulations, to extract relevant quantities of interest. These simulations could then be experimentally validated using a scenario in which a detector is placed in parallel with the Sheffield DT source and surrounded by volume of material (e.g. soil and/or rock) of known properties.Whilst the borehole detector is the primary hardware focus of this application it is acknowledged that the fusion of plastic scintillator with neutron-sensitive foils may afford opportunities in other application areas such as environmental monitoring and the nuclear industry, where alternative format detectors employing the same underlying technology may be of interest.The project output is therefore expected to comprise an operational borehole detector prototype with empirical comparisons with "standard" (e.g. 3He) methods as well as MVA-based software tools for signal discrimination.

2018年1月，nusec主持的研讨会发表了一份报告，其中强调了在测井中开发替代检测和数据解释方法的必要性。谢菲尔德大学（University of Sheffield）的探测器开发小组在一系列粒子（包括介子、中子和伽马）的探测器开发方面有着悠久的历史。我们最近在stfc的支持下，开发了用于水含量监测的中子敏感探测器，从而在内部开发了制造中子敏感箔的新专业知识。这项新功能与之前用于CCS监测的井眼探测器原型设计的专业知识相结合。此外，由于与LabLogic的长期合作关系，我们可以使用闪烁体制造设备，实现短周期的原型设计和开发周期。随着我们的中子源范围（脉冲DT， AmBe, 252Cf），这使我们能够开发低成本的塑料闪烁体和基于中子敏感箔的探测系统，用于井眼配置。通过这项研究，我们打算探索使用掺杂不同材料的闪烁体块来生产精细分割的辐射探测器的可能性，这些探测器可以安装在标准钻孔中。与标准方法相比，这种方法有几个优点，例如：-使用单个检测器，使用位置敏感检测/巧合覆盖混合场。因此，不需要许多不同类型探测器的现场专业知识；-由许多相似的堆叠“模块”或“块”组成的精细分段探测器的部署意味着系统已内置冗余；-从初始中子发生器脉冲到探测中子/伽马的时间可以提供使用放射源时无法获得的额外细粒度信息。正如论文中所指出的，当前测井技术的另一个问题是数据分析。例如，伽马测井几乎被用作定性测量。同样，中子孔隙度测量需要进一步校准才能有效。包含脉冲发生器和相关的粒子检测产生补充信息，然而，这是以额外的处理时间和训练为代价的。对此的解决方案是使用机器学习来提取感兴趣的数量，例如，在初始发生器脉冲后，在门通窗口观察到的钻孔探测器检测到的总中子/伽马计数将作为多变量分析（MVA）方法（如神经网络）的合适输入。这样的MVA可以通过模拟进行训练，以提取相关的兴趣量。然后，这些模拟可以通过实验验证，使用一个探测器与谢菲尔德DT源平行放置，并被已知性质的材料（例如土壤和/或岩石）包围的场景。虽然井眼探测器是该应用的主要硬件焦点，但人们承认，塑料闪烁体与中子敏感箔的融合可能会在其他应用领域提供机会，例如环境监测和核工业，在这些领域，可能会对采用相同底层技术的替代格式探测器感兴趣。因此，项目产出预计将包括一个可操作的井眼探测器原型，与“标准”（例如3He）方法进行经验比较，以及基于mva的信号识别软件工具。