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Cointerpretation of Distributed Acoustic and Temperature Sensing for Improved Smart Well Inflow Profiling

机译:分布式声学和温度传感的共同诠释,改进智能井流入谱

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Current advances in the well completion technology have allowed for more complex smart well instrumentations with marginal additional cost. As an example, optical fibers can be run along horizontal wells to provide acoustic and temperature data that are distributed both in time and space. With such data at our disposal, an immediate evaluation of the well response is possible as changes in the reservoir or well occur. Most current work in distributed measurements looks at Distributed Acoustic Sensing (DAS) or Distributed Temperature Sensing (DTS) data individually, which limits our inferences about the multiphase flow problem. The objective of this work was to look at the two pieces of data together and determine what improvements can be achieved in multiphase flow problems compared to the conventional methods of looking at each DAS and DTS alone. The study began by evaluating the performance of DAS in analyzing two-phase flow; a process which begins by extracting the speed of sound within the fluid medium from the acoustic signal, then obtaining the phase fraction combination that obtains this speed of sound reading. Another procedure is explained to obtain similar results from DTS measurements. In this case, however, the in-situ phase fractions are correlated to the Joule-Thomson effect as reservoir fluids enters the wellbore. As both these procedures are limited to one-and two-phase flow applications, we extended the solution to work in three-phase flow problems by combining information from DAS and DTS. The flow profiling procedure was applied to two smart wells in the Middle East. Flowrates from different segments of the well were calculated and results were in close agreement with a surface flowmeter for most sections of the well. In cases where both DAS and DTS were not available for the same well, a commercial compositional and thermal reservoir simulator was used to generate synthetic examples. By applying the developed procedure, we found that cointerpretation of DAS and DTS data yields accurate in-situ three-phase fractions for all ranges of water cuts and gas volume fraction. In comparison, analyzing DAS or DTS individually is usually not sufficient to fully determine a three-phase flow problem.
机译:井井井完井技术的当前进步允许更复杂的智能井仪表,边际额外成本。作为示例,光纤可以沿水平孔运行,以提供分布在时间和空间中的声学和温度数据。通过我们所处理的这些数据,随着水库或良好发生的变化,可以立即评估井响应。分布式测量中的大多数当前工作都在单独地看出分布式声学传感(DAS)或分布式温度传感(DTS)数据,这限制了我们对多相流动问题的推论。这项工作的目的是将两条数据一起看,并确定了与单独看每个DAS和DTS的传统方法相比如何在多相流动问题中实现的改进。该研究开始通过评估DAS分析两相流的性能;通过从声学信号提取流体介质内的声音速度开始的过程,然后获得获得这种声音读取速度的相位馏分组合。解释另一种过程以获得来自DTS测量的类似结果。然而,在这种情况下,当储层流体进入井筒时,原位相位与Joule-Thomson效应相关。由于这两种程序都限于单相流程,我们通过将信息与DAS和DTS组合结合来扩展到三相流问题的解决方案。流程分析程序应用于中东的两个智能井。计算来自井的不同段的流量,结果与井大部分部分的表面流量计密切一致。如果DAS和DTS在相同的情况下,商业成分和热储存器模拟器的情况下用于产生合成实例。通过应用开发的程序,我们发现DAS和DTS数据的共同诠释为所有水切口和气体体积分数产生精确的原位三相级分。相比之下,单独分析DAS或DTS通常不足以完全确定三相流动问题。

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