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Vertical Lift Performance using System Analysis (Investigating active variables on a flowing well performance) Base Case

机译:使用系统分析的垂直举升性能(研究流动井性能中的活动变量)基本案例

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摘要

As muvh as 80% of the total pressure is consumed in lifting fluids from the reservoir to the surface. Therefore, this project carefully analyzes the variables that affect the performance of flowing wells.;Coupled with System Analysis, which offers a means of recognizing quickly the components of the production system that are restricting its performance, an empirical equation is proposed that can be used to predict the producing rate of naturally flowing wells. Consequently, the performance of wells can be monitored and improved significantly.;In this research, a base case was considered and data available was used to create a vertical lift model of well of interest using an industry standard production software. Different correlations were used to match the well test results. Correlations closest to the data, were considered for this analysis.;Some variables that affect flowrate were identified; theoretical and sensitivity analysis was carried out to see the effect of these variables using these correlations. Dimensionless analysis was carried out on these variables identified like fluid density, tubing diameter, and wellhead pressure by deriving the coefficient of these variables with respect to flowrate. These variables, with their respective flowrates, were trained in Artificial Neural Network to study these variables.;Results from these analyses distinguished some correlations from others based on their error histogram, performance plot, and training fit, and were retrieved from Artificial Neural Network.
机译:从储层到地面的提升流体消耗了总压力的80%。因此,该项目仔细分析了影响流动井性能的变量。结合系统分析,该方法提供了一种快速识别生产系统中限制其性能的组件的方法,提出了一个经验公式,可以使用预测自然流动井的生产率。因此,可以对井的性能进行监控和显着改善。;在本研究中,考虑了一个基本案例,并使用可用数据使用行业标准的生产软件来创建感兴趣井的垂直举升模型。使用不同的相关性来匹配试井结果。该分析考虑了最接近数据的相关性。确定了一些影响流量的变量;使用这些相关性进行了理论和敏感性分析,以查看这些变量的影响。通过推导这些变量相对于流量的系数,对这些变量(如流体密度,油管直径和井口压力)进行了无量纲分析。这些变量及其各自的流量在人工神经网络中进行了训练,以研究这些变量。这些分析的结果基于其误差直方图,性能图和训练拟合,将某些相关性与其他相关性进行了区分,并从人工神经网络中进行了检索。

著录项

  • 作者单位

    University of Louisiana at Lafayette.;

  • 授予单位 University of Louisiana at Lafayette.;
  • 学科 Petroleum engineering.
  • 学位 M.S.
  • 年度 2014
  • 页码 98 p.
  • 总页数 98
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:53:45

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