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首页> 外文会议>Society of Petrophysicists and Well Log Analysts, Inc.;SPWLA Annual Logging Symposium >PRODUCTION OPTIMIZATION OF SANDING HORIZONTAL WELLS USING A DISTRIBUTED ACOUSTIC SENSING (DAS) SAND MONITORING SYSTEM - A CASE STUDY FROM THE ACG FIELD IN AZERBAIJAN
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PRODUCTION OPTIMIZATION OF SANDING HORIZONTAL WELLS USING A DISTRIBUTED ACOUSTIC SENSING (DAS) SAND MONITORING SYSTEM - A CASE STUDY FROM THE ACG FIELD IN AZERBAIJAN

机译：使用分布式声学传感（DAS）砂监测系统制作闸阀水平井的生产优化 - 以阿塞拜疆的ACG字段为例

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This paper discusses results from the first successful deployment of a predictive modelling technology that informs pressure optimization procedures to help minimize sand production and increase hydrocarbon production efficiency in sand prone oil wells.The technique takes variabilities in sand production observed through time across the reservoir section, inferred from downhole sand entry logs, alongside real-time sand transportation logs that monitor sand deposition in pipe as key inputs (both of which computed using a fiber optic Distributed Acoustic Sensor (DAS) based Downhole sand monitoring system). This data is then combined with other time series sensor inputs, like choke position, Down Hole Pressure (DHP) and surface flowline acoustic measurement (sand detector) to predict drawdown pressure envelopes to improve production efficiency.This paper details observations and initial field results from the first deployment of the capability in a highly deviated sand prone oil well completed with an open hole gravel pack (OHGP) completion in the BP-operated Azeri- Chirag- Gunashli (ACG) field located in the Azerbaijan sector of the Caspian Sea. The paper will detail observations and procedures used to increase oil production by over 25% and eliminate sanding risks using the technology. The proposed workflow is part of a comprehensive suite of downhole sand surveillance and management tools fueled by streaming analytics capabilities run on DAS data that have played a key role in managing sand production challenges in the ACG field.The technology has been applied numerous times for base protection, drawdown optimization and targeted remediation. In this instance, we discuss the use of the technology to (1) identify and inform the source of sand detected at surface e.g., formation or completion accu-mulation, (2) identify formation intervals at risk of sanding, and (3) design advisory operational procedures for production optimization.
机译：本文讨论了第一次成功部署预测建模技术的结果，这些技术通知了压力优化程序，帮助最小化砂生产并提高砂易发油井中的碳氢化合物生产效率。该技术在储存器部分通过时间观察到的沙子生产中的变形性，从井下砂入口日志推断，以及实时的砂运输日志，监测管道中的砂沉积作为键输入（两者都使用光纤分布式声学传感器计算（DAS）基于井下砂监测系统）。然后将该数据与其他时间序列传感器输入相结合，如阻塞位置，下孔压力（DHP）和表面流线声学测量（沙探测器），以预测降压压力包络以提高生产效率。本文详细说明了观察和初始现场，从第一次部署高度偏离的砂易发油井中的能力，在BP操作的Azeri-Chirag-Gunashli（ACG）领域中完成了开放孔砾石包（OHGP）完成里海阿塞拜疆部门。本文将详细说明使用超过25％的石油产量的观察和程序，并使用该技术消除打磨风险。该拟议的工作流程是通过流式分析功能在DAS数据中运行的流媒体数据在管理ACG领域中发挥关键作用的关键作用来推动的井下沙枪监测和管理工具的一部分。该技术已应用于基本保护，绘制优化和有针对性的修复的许多次。在这种情况下，我们讨论技术的使用至（1）识别并通知在表面检测到的沙子源。，形成或完成ACCU-Mulation，（2）识别打磨风险的形成间隔，以及（3）设计咨询业务程序进行生产优化。

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