断裂(地质)
偏移量(计算机科学)
垂直的
地质学
水力压裂
数据采集
低频
断裂力学
声学
材料科学
岩土工程
计算机科学
复合材料
几何学
数学
电信
物理
操作系统
程序设计语言
作者
Masaru Ichikawa,Shinnosuke Uchida,Masafumi Katou,Isao Kurosawa,Kohei Tamura,Akira Kato,Yoshiharu Ito,Mike de Groot,Shoji Hara
出处
期刊:The leading edge
[Society of Exploration Geophysicists]
日期:2020-11-01
卷期号:39 (11): 794-800
被引量:13
标识
DOI:10.1190/tle39110794.1
摘要
Distributed acoustic sensing (DAS) is an effective technique for hydraulic fracture monitoring. It can potentially constrain fracture propagation direction and time while monitoring strain perturbation, such as stress shadowing. In this study, we acquired passive DAS and distributed temperature sensing (DTS) data throughout the entire fracturing operations of adjacent production wells with varying offset lengths from the fiber-optic cable in the Montney tight gas area. We applied data processing techniques to the DAS data to extract low-frequency components (less than 0.5 Hz) and to construct the strain rate and cumulative strain maps for detecting responses related to fracture hits along the fiber-optic cable. We used low-frequency DAS (LF-DAS) results to estimate the fracture hit position and time, and in certain cases, to additionally estimate the fracture connection. By integrating LF-DAS results with DTS results, we detected the temperature changes around the compression response near the fracture hit position and time. Furthermore, we observed that timing of the fracture hit can be constrained more precisely by using high-frequency DAS data (greater than 10 Hz). We estimated the fracture propagation direction and speed from the estimated fracture hit position and time. The fracture propagation direction deviated slightly from a perpendicular line to the fiber direction. In addition, as estimated from the first fracture hit time, the fracture length and fluid injection volume had a proportional relationship. Due to challenges associated with the data, it is important to design data acquisition geometry and fracturing operations on the premise of acquiring LF-DAS data. It is also important to apply an additional noise reduction process to the data.
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