首页> 外文会议>SPE Russian Petroleum Technology Conference >SNL Application for Production Logging in Hard-to-Recover Gas Reserves Wells
【24h】

SNL Application for Production Logging in Hard-to-Recover Gas Reserves Wells

机译:SNL用于生产登录的难以回收的天然气储量井

获取原文

摘要

Determination of Unit T1-2(Turonian)flow characteristics in Yuzhno-Russky oil and gas condensate field is a priority area in the development process monitoring.This paper is focused on the informative value of downhole logging surveys conducted for assessment of the reservoir and wellbore flow geometry in the well after hydraulic fracturing.A more detailed consideration of this matter has become possible due to the extensive use of integrated logging suite data during estimation of input parameters for interpretation of pressure transient analysis results.The difficulty of this task was due to the fact that the extent of the vertical fracture in the Turonian formation was several times larger than the perforation zone and,as a result,estimation of the actual net pay based on the conventional logging suite data was hardly practicable.Besides that,due to poor perforation quality the geometry of fracture flow from the reservoir was affected.In view of this,Spectral Noise Logging technique was applied to estimate the reservoir effective thickness contributing to the flow through the fracture created as a result of the hydraulic fracturing job.The integrated logging suite supplemented with SNL-HD tool was run at several differential pressures.As a result of SNL-HD and HPT surveys and subsequent flow modelling,valuable information on fracture flow rates was successfully obtained.
机译:测定单位T1-2(Turonian)yuzhno-Rustrky油和天然气凝析田的流动特性是开发过程监测中的优先区域。本文专注于对水库和井筒评估进行井下测井调查的信息价值液压断裂后井中的流动几何形状。由于在估计压力瞬态分析结果的输入参数估计期间,由于集成的测井套件数据广泛使用,因此可以详细考虑这一问题。这项任务的难度是由于此任务的难度导致垂直骨折的程度比穿孔区域大数倍,因此,基于常规伐木套件数据的实际净支付估计几乎没有实用。基于穷人穿孔质量来自储层的裂缝流动的几何形状受到影响。在此方面,光谱噪声测井技术的视图被应用于估计由于液压压裂工作而产生的骨折有助于流动的储层有效厚度。补充SNL-HD工具的综合测井套件以几种差分压力运行。SNL-HD和SNL-HD的结果HPT调查和随后的流量建模,成功获得了关于骨折流速的有价值信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号