Application of Advanced Data Analytics for Gas Reservoirs and Wells Management

A. Srinivasan, Gaurav Modi, R. Agrawal, V. Kumar
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引用次数: 1

Abstract

The amount of time and effort required to access and integrate Subsurface data from multiple sources is significant. Using Advanced Data Analytics, mainly python, an integrated subsurface dashboard titled Hybrid Integrated Visualization Environment (HIVE) was created using Spotfire to empower the integrated Exploration, Development and Well Reservoir and Facilities Management (WRFM) subsurface teams in: Professionalizing data and knowledge management to have "one" version of the truth. Data consolidation and preparation to avoid repetitive manual work & Enhancing opportunity identification to optimize production and value The approach of subsurface data integration can be broken down into 4 major steps, namely: Step 1: Python programming was used to pre-process, restructure and create unified data frames. Use of python significantly reduces the time required to pre-process a diverse number of subsurface data sources consisting of static, dynamic reservoir models, log data, historical production & pressure data and wells & completion data to name a few. Step 2: - Standard diagnostic industry recognized diagnostic plots were automated using advanced analytic techniques in HIVE with the help of unified data frames. Step 3: HIVE was created to link various internal corporate data stores like pressure, temperature, rate data from PI System (stores real time measured data), Energy Components (EC) and Oil Field Manager (OFM) in real time. This was done to ensure that data from various petroleum engineering disciplines could now be visualized and analyzed in a structured manner to make integrated business decisions. Step 4: One of the key objectives of pursuing this initiative was to ensure that subsurface professionals in Shell Trinidad and Tobago were trained and upskilled in the use of python as well visualization tools like Spotfire and Power BI to ensure the maintenance and improvement of HIVE going forward. The development of HIVE has made it easier and more efficient to access and visualize subsurface data, which was extremely time consuming earlier while using older conventional techniques. Standard diagnostic plots and visuals were developed and are now used to drive integrated decision making, with key focus being water and sand production management from a production management perspective. Consequently, HIVE also drives enhanced integration between disciplines (Petrophysics, Petroleum Geology, Production Technology, Reservoir Engineering and Production operations) and departments (Developments, Upstream and Exploration). The petroleum industry has started to embrace the application of advanced data analytics in our day-to-day work. A successful application of these techniques results in transforming the ways of working by increasing efficiency, transparency and integration among teams.
先进数据分析在气藏和油井管理中的应用
从多个来源获取和整合地下数据需要大量的时间和精力。利用先进的数据分析技术(主要是python),利用Spotfire创建了一个名为混合集成可视化环境(HIVE)的集成地下仪表板,为综合勘探、开发和油井、油藏和设施管理(WRFM)的地下团队提供了专业化的数据和知识管理,使其拥有“一个”版本的真相。地下数据整合的方法可分为4个主要步骤,即:第一步:使用Python编程进行预处理、重构和创建统一的数据框架。python的使用大大减少了预处理各种地下数据源所需的时间,这些地下数据源包括静态、动态油藏模型、测井数据、历史生产和压力数据以及井和完井数据等。步骤2:在HIVE中使用先进的分析技术,在统一数据框架的帮助下,将标准诊断行业认可的诊断图自动化。第三步:HIVE的创建是为了实时连接来自PI系统(存储实时测量数据)、能源组件(EC)和油田管理器(OFM)的各种内部企业数据存储,如压力、温度、速率数据。这样做是为了确保来自各种石油工程学科的数据现在可以可视化,并以结构化的方式进行分析,从而做出综合的业务决策。第四步:实施该计划的关键目标之一是确保壳牌特立尼达和多巴哥的地下专业人员接受培训,提高python以及Spotfire和Power BI等可视化工具的使用技能,以确保HIVE的维护和改进。HIVE的开发使得访问和可视化地下数据变得更容易、更高效,而以前使用旧的传统技术是非常耗时的。开发了标准诊断图和视觉效果,现在用于推动综合决策,从生产管理的角度关注水和砂的生产管理。因此,HIVE还促进了学科(岩石物理学、石油地质学、生产技术、油藏工程和生产作业)和部门(开发、上游和勘探)之间的整合。石油行业已经开始在日常工作中应用先进的数据分析技术。这些技术的成功应用可以通过提高团队之间的效率、透明度和集成来改变工作方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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