龙马溪页岩水平井高清油基微电阻率测井识别页岩气靶区

Honglin Shu, Gaocheng Wang, Yuan Xiaojun, Yin Kaigui, Qin-Fei Li, Luo Yufeng, Da-li Wang
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引用次数: 1

摘要

龙马溪页岩水平井邻近层的伽马射线读数相似,且构造断层复杂,地震数据受分辨率限制无法探测,这给页岩气靶区识别带来了挑战。这使得页岩气测井评价与水平分支间实际产气量的相关性变得困难。垂直邻井和先导井的页岩气靶层平均厚度约为5 m。根据垂直邻井的伽马测井资料和地震资料,规划了龙马溪页岩水平井的井眼轨迹,并利用随钻伽马测井工具进行了水平井的布置。本案例研究的目的是确定两口油基泥浆水平井的页岩气目标层和优化增产阶段设计。水平井长度分别为1821 m和1300 m。下志留统页岩气目标层和上奥陶统五峰页岩层的伽玛射线读数相似。层与层之间为广泛分布的上五峰组顶部壳灰岩,现场研究厚度为0.2 m ~ 0.6 m。相似的伽马射线读数表明,页岩气目标沿水平井方向存在多种可能性,其中部分在页岩气目标带外,部分在页岩气目标带内。此外,横断水平井的构造断层使多种可能性的判别变得更加复杂。在两口油基泥浆水平井中进行了新一代高清油基微电阻率成像测井。最初的目标是识别天然裂缝,这些信息通常用于射孔簇设计和增产阶段。然而,高分辨率油基微电阻率成像测井可以提供更详细的水平井筒结构信息,包括位移断层和构造倾角。借助三维结构建模技术,获得了水平井的真实地层钻井极性和结构模型,揭示了水平井相对于页岩气目标层的位置。根据构造模型,确定了页岩气靶区内和区外的部分。新一代高清油基微电阻率成像测井是龙马溪页岩水平井页岩气目标识别的良好解决方案。它消除了沿水平井的伽马射线测井中页岩气目标的多种可能性。更详细的裂缝、断层和页岩气目标区内部的结构信息被用于优化增产阶段设计。此外,利用油基微电阻率成像测井资料进行反演处理,区分开缝和胶结裂缝。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Shale Gas Target by High Definition Oil Based Microresistivity Image Logs in Horizontal Longmaxi Shale Wells
The identification of the shale gas target in the Longmaxi shale horizontal wells presents challenges due to the similar gamma ray readings of neighboring layers and the complex structural faults that seismic data cannot detect because of resolution limitations. This makes the correlations of the shale gas log evaluation and actual gas production between horizontal laterals difficult. The average thickness of the shale gas target in vertical offset wells and pilot wells is about 5 m. The well trajectories of the horizontal wells in the Longmaxi shale were planned from the gamma ray logs of vertical offset wells and seismic data, and the placement of the horizontal wells was performed by a gamma ray log measurement-while-drilling tool. Identifying the shale gas target layer and optimum stimulation staging design in two oil-based mud horizontal wells were the objectives in this case study. The lengths of the horizontal wells were 1821 m and 1300 m. The similar gamma ray readings were in the Lower Silurian shale gas target layer and the Upper Ordovicican Wufeng shale layer. Between the layers was the widespread shell limestone of the top of the Upper Wufeng Formation, from 0.2 m to 0.6 m thick in the field studied. The similar gamma ray readings indicated multiple possibilities for the shale gas target along the horizontal wellbores, among which were portions that were out of the shale gas target zone and portions that were inside the shale gas target zone. In addition, the structural faults that cut through the horizontal wells made discriminating among the multiple possibilities more complex. New-generation high-definition oil-based microresistivity image logs were run in the two oil-based mud horizontal wells. The objective was originally to identify natural fractures, which information was commonly used in perforation cluster design and stimulation staging. However, the high-definition oil-based microresistivity image logs provided more detailed structural information along the horizontal wellbores, including displacement faults and structural dips. With the help of 3D structural modeling techniques, the true stratigraphic drilling polarity and structural model of the horizontal wells revealed the position of the horizontal wellbores relative to the shale gas target layer. The portions inside and outside the shale gas target zone were identified from the structural model. The new-generation high-definition oil-based microresistivity image logging was a good solution for the identification of the shale gas target in the Longmaxi shale horizontal wells. It eliminates the multiple possibilities of the shale gas target from gamma ray logs along the horizontal wellbores. The more detailed structural information about fractures, faults, and the portions inside the shale gas target zone was used in optimum stimulation staging design. In addition, the oil-based microresistivity image logs were used to distinguish between open fracture and cemented fracture by inversion processing.
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