沙子管理/控制

出砂预测、监控的自动化工作流程提高了运营效率

本文的作者提出了一种自动化的出砂预测和控制监测方法，该方法通过减少缅甸油田手动分析和决策过程所花费的时间来提高运营效率。

方法概述。
图 1——方法概述。
资料来源：IPTC 22989。

完整论文中描述的研究目标是通过使用出砂预测模型根据当前操作条件估计每口井的出砂量并计算每口井的出砂作业范围以指导规划和管理，从而提高运营效率。作者发现，砂预测和控制监测 (SPCM) 的简化代表性基本体积 (SREV) 方法通过减少通过带有预测结果的仪表板进行手动分析和决策所花费的时间，从而提高了运营效率。我们鼓励读者查看完整的论文，其中包含详细的工作流程，其中包括相关的方程和步骤。

学习目的

  • 实施 SREV 方法
  • 使用出砂预测模型根据当前操作条件估算每口井的出砂量
  • 动态估计从井口到入口分离器的积砂量
  • 预测从井口到生产和测试管汇的主要管线的壁厚
  • 考虑储层条件变化时预测出砂量
  • 使用自动化工作流程运行网络模型并获取流动条件，根据井设计和油藏信息生成井的出砂预测模型，并根据最新的经过验证的测试或传感器校准井的出砂模型数据
  • 实施基于 Web 的界面

研究区

Zawtika气田位于缅甸Moattama湾。该研究共使用了123口井和11个平台。

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原文链接/jpt
Sand management/control

Automated Work Flow for Sand Prediction, Monitoring Improves Operational Efficiency

The authors of this paper propose an automated approach to sand prediction and control monitoring that improved operational efficiency by reducing time spent on manual analysis and the decision-making process in a Myanmar field.

Overview of the methodology.
Fig. 1—Overview of the methodology.
Source: IPTC 22989.

The objective of the study described in the complete paper is to increase operational efficiency by using a sand-prediction model to estimate sand production per well based on current operating conditions and to calculate a sand-operating envelope for each well to guide planning and management. The authors found that the simplified representative elementary volume (SREV) approach to sand prediction and control monitoring (SPCM) resulted in an improvement in operational efficiency by reducing time spent on manual analysis and decision-making through dashboards with predictive results. The reader is encouraged to review the complete paper, which contains detailed work flows that include associated equations and steps.

Study Objective

  • Implement SREV approach
  • Use a sand-prediction model to estimate sand production per well based on current operating conditions
  • Estimate sand accumulation from the wellhead to the inlet separator dynamically
  • Predict wall thickness of the main flowlines from the wellhead to the production and test manifold
  • Forecast sand production when considering changes in reservoir conditions
  • Use an automated work flow to run the network model and obtain flow conditions, generate the well’s sand-prediction model based on well design and reservoir information, and calibrate the well’s sand-production models based on the latest validated test or sensor data
  • Implement Web-based interfaces

Study Area

The Zawtika gas field is in the Gulf of Moattama, Myanmar. A total of 123 wells and 11 platforms was used in the study.

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