利用人工智能工具生成结构化数据的井眼示意图

Kemajou Vanessa Ndonhong, A. Bao, O. Germain
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引用次数: 2

摘要

井筒示意图对于井的规划和作业至关重要,因为它详细描述了井的设计、完井,有时还包括生产机制。井筒示意图有多种格式和类型;然而,它们通常由井图和附注表组成,附注表列出了组件和设备细节,如深度和直径。纸质井眼示意图报告通常是油气运营商获得的有关老井技术信息的主要说明。对这些井进行任何干预或进一步的操作都需要对这些报告进行彻底的人工解释,这可能会很长,而且容易出错。因此,为了将图表和注释自动转换为可读的数据库,必须开发一种实用的技术或工具。人工智能(AI)驱动的图像分析为其他工程学科和行业解决了类似的问题,随着软件和计算机硬件能力的最新进展,有可能为石油和天然气行业设计专门的解决方案。因此,研究人员定义并实施了一种方法,导入可用的机器学习技术,以自动解释和分析井筒示意图。使用这种新型工具,扫描基于纸张的井眼示意图可以获得数字化且易于共享的结构化数据,这些数据可用于重新生成数字井眼示意图。该方法对井眼图和井眼图文件上的注释进行分析,并将分析结果与周围的注释和工程约束条件进行匹配。该方法在一组井眼示意图文件上进行了测试,并生成了数字示意图。检测到的基本部件和设备在深度和直径方面与原始原理图相匹配。设计的工具节省了大量的时间和精力,同时提供了准确性和可重复性。这些结果突出了将多学科数据管理思想应用于行业的一些好处。图像分析中的目标检测技术是石油和天然气行业用于识别井图中组件的新技术。此外,这个项目是全面的,因为它标识了图和相关的注释。将讨论本研究中遇到的挑战和突破。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wellbore Schematics to Structured Data Using Artificial Intelligence Tools
Wellbore schematics are essential to well planning and operations because they detail well design, completions, and sometimes the production mechanism. There are multiple formats and types of wellbore schematics; however, they generally consist of a well diagram accompanied by tables of annotations listing components and equipment details such as depths and diameters. Paper-based wellbore schematic reports are often distributed as the primary account of technical information concerning old wells after being acquired by oil and gas operators. Any intervention or further operation on those wells would require a thorough and manual interpretation of those reports, which can be lengthy and prone to errors. Therefore, to automatically convert the diagram and annotations into a readable database, a practical technique or tool has to be developed. Artificial intelligence (AI)-powered image analysis addresses similar problems for other engineering disciplines and industries, and with the latest advances for software and computer hardware capabilities, it is possible to design specialized solutions for the oil and gas industry. Therefore, a methodology was defined and implemented to import the available machine learning technology for automating the interpretation and analysis of wellbore schematics. With this novel tool, scanning the paper-based wellbore schematic results in digital and easily shareable structured data that can be used to regenerate a digital wellbore schematic. This method analyzes the diagram and the annotations on the wellbore schematic file and then combines the analysis results by matching the diagram with the surrounding annotations and engineering constraints. The methodology was tested on a set of wellbore schematic files, and digital schematics were regenerated. Fundamental components and equipment were detected that matched the original schematics in terms of depths and diameters. The designed tool saves considerable time and effort while providing accuracy and repeatability. These results highlight some of the benefits of applying multidisciplinary ideas for data management to the industry. The object detection technique in image analytics is new to the oil and gas industry for identifying components in well schematics. Further, this project is comprehensive because it identifies the diagram and related annotations. Challenges and breakthroughs experienced in this research will be addressed.
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