Study on detection methods of abnormal structural planes in shale reservoirs in Hongxing area
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摘要:
页岩储层异常结构面分为强硬面与软弱面，前者以灰岩夹层为代表，后者包含层理缝、天然裂缝2类，其发育特征与页岩储层成藏、压裂改造及油气产出效率密切相关。精准识别异常结构面的类型与发育段，对页岩油气勘探开发具有重要指导意义。以红星地区页岩储层为研究对象，首先利用阵列声波测井快慢横波能量差实现灰岩夹层强硬面的精准识别；其次引入电成像测井基尼系数，通过计算基尼系数的震荡程度量化表征层理缝的发育特征；最后结合阵列声波相关系数等测井参数，及声波波形图中的深色条带信息，实现天然裂缝的定性识别与定量刻画。研究表明，能量差信息对灰岩夹层强硬面具有良好的响应特征；基尼系数法有效改进了层理缝以往单条识别工作量大、效果不佳的问题；阵列声波相关系数的异常低值特征与波形图深色条带相结合，能更直观地表征天然裂缝发育特征，可视化判定天然裂缝的发育位置与规模大小。现场应用验证表明，该方法对直井可实现灰岩夹层、层理缝及天然裂缝发育段的准确、快速识别；对水平井，可通过阵列声波测井参数与波形图完成灰岩夹层的判断及天然裂缝的定量识别，结合井漏资料验证，其结果与裂缝识别结论相互佐证，证实了该方法在水平井应用中的可行性与可靠性。
Abstract:ObjectiveAbnormal structural planes in shale reservoirs are divided into hard planes and weak planes. The former is typically represented by limestone interbeds, and the latter includes bedding fractures and natural fractures. Their development characteristics are closely related to reservoir accumulation, hydraulic fracturing, and oil and gas production efficiency. Accurate identification of the types and development intervals of these planes is of great practical significance for the exploration and development of shale oil and gas. Taking shale reservoirs in the Hongxing area as the research target, this study aims to propose a set of effective logging-based detection methods for different types of abnormal structural planes, so as to address the difficult classification and low identification accuracy of these planes in continental shale reservoirs.
MethodsUsing the sensitivity of array sonic logging and electrical imaging logging to the lithology and structural characteristics of shale reservoirs, a combined logging detection method based on array sonic logging and electrical imaging logging was adopted for the identification of abnormal structural planes. Firstly, the energy difference between fast and slow shear waves from array sonic logging was used to accurately identify the limestone interbeds and other hard planes. Secondly, the Gini coefficient of electrical imaging logging was introduced, and the development characteristics of bedding fractures were quantitatively characterized by calculating the degree of fluctuation of the Gini coefficient. Finally, combined with the array sonic correlation coefficient and other logging parameters, as well as the dark strips in the acoustic waveform logs, the qualitative identification and quantitative characterization of natural fractures were realized. In the research process, the acoustic energy values and attenuation coefficients were calculated using Fourier transforms and the strain tensor using the computational Fourier transform moiré (STC) method. The variance of the Gini coefficient was used to quantify the degree of fluctuation, and the Pearson correlation coefficient of the frequency spectra of fast and slow shear waves was defined as the core parameter for identifying natural fractures.
ResultsThe research results showed that the energy difference effectively responded to the hard planes of limestone interbeds in the Hongxing area, clearly reflecting their development intervals. The Gini coefficient method addressed the heavy workload and poor performance in traditional single-strip identification of bedding fractures, enabling efficient and quantitative characterization of fracture development. The combination of the abnormally low values of the array sonic correlation coefficient and the dark strips in waveform logs provided an intuitive representation of natural fracture development, allowing visual determination of their positions and scales. Field testing in well Hong A showed clear differentiation of logging responses for each type of abnormal structural plane, and the identification results were highly consistent with core and lithofacies column data.
ConclusionField verification shows that this method enables accurate and rapid identification of the development intervals of limestone interbeds, bedding fractures, and natural fractures in vertical wells. For horizontal wells, due to the lack of electrical imaging logging data, the bedding fracture development intervals cannot be identified, but limestone interbeds and natural fractures can still be quantitatively detected using array sonic logging parameters and waveform logs. Verification with lost circulation data from the horizontal section of well Hong A confirmed consistency with fracture identification results, demonstrating the method’s feasibility and reliability in horizontal wells. The proposed logging detection method enriches the diversity of identification methods for abnormal structural planes in shale reservoirs and provides a reliable technical reference for precise layer selection and optimized fracturing design in the Hongxing area and similar continental shale reservoirs.
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图 1 红星地区红A井灰岩夹层岩心照片
Figure 1. Core photograph of limestone interbeds of Well Hong A in Hongxing area
图 3 红星地区红A井天然裂缝示例照片
Figure 3. Representative photograph of natural fractures of well Hong A in Hongxing area
图 4 阵列声波测井远近探头测量示意图
Rn. 第n个接收器；R1. 某个接收器；T. 发射器；L1. 发射器与某接收器距离；L2. 某接收器与第n个接收器间距
Figure 4. Schematic diagram of array sonic logging with far and near receivers
图 5 基于导电率的基尼系数计算示意图
Figure 5. Schematic diagram of Gini coefficient calculation based on conductivity
图 6 声波变密度图上的“V”字形条带（红色框中）
FREQ. 主频率；ACAS. 套管声幅；TT2. 慢波到达时间；WF2. 波形；WAMF. 振幅频率
Figure 6. V-shaped stripes on acoustic variable density map
图 7 异常结构面井下识别流程图
DifEng. 声波系数能量差；CO_WP. 快慢横波频谱的波尔逊相关系数；下同
Figure 7. Downhole identification workflow of abnormal structural planes
图 8 红星地区红A井综合测井解释图
GR.自然伽马；Abs_FS.慢横波能量；Abs_FF.快横波能量；R.基尼系数；Stdeva_R.基尼系数的方差；下同
Figure 8. Comprehensive logging interpretation of Well Hong A in Hongxing area
图 9 红星地区红A井综合柱状图
LLD. 深电阻率；LLS. 浅电阻率；AC. 声波时差；AC. 声波时差；DEN. 密度；CNL. 中子
Figure 9. Comprehensive histogram of Well Hong A in Hongxing area
图 10 红星地区红A井水平段阵列声波测井解释图
Figure 10. Array sonic logging interpretation of horizontal section of Well Hong A in Hongxing area
表 1 异常结构面检测方法总结
Table 1. Summary of detection methods for abnormal structural planes
异常结构面 地质特征 识别原理 识别方法优缺点 灰岩夹层 多呈灰白色，与深色泥岩混杂，表现出一定层状、纹层状层理构造。发育溶蚀孔，呈蜂窝状或星散状，孔径一般较大且孔隙边缘不规则 红星地区灰岩夹层的孔径变化差异大，声波经过夹层时，岩性突变引起波阻抗差异，增强了反射与透射效应，能量衰减大，利用声波能量信息定性判别夹层 识别简单准确，可以直观看到地层中灰岩夹层段的发育。能量差的反应存在多解性，需结合实际情况判别 层理缝 高密度，平行或近似平行于页理面，沿页理面呈断续、分枝状、尖灭等分布的特点 由于层理发育层段非均质性强，表现为快慢横波起伏较大，基尼系数忽高忽低，用标准差来表征基尼系数的震荡程度，震荡越剧烈，层理越发育 避免了人工单条拾取工作量大且繁杂的问题；对基尼系数震荡程度的判断存在主观性 天然裂缝 主要发育在高碳页岩中，基本被泥炭质半充填、方解石半充填 地层存在各向异性时，慢横波与快横波正交，各向异性的大小可以反映裂缝发育的程度。天然裂缝发育段与快慢横波频谱信号紧密相关，皮尔逊相关系数表征了快慢横波频谱信号之间的相关性，利用相关性的变化识别天然裂缝发育段；天然裂缝常常导致声波的传播速度减慢或发生折射，从而产生波形的“偏移”现象，根据偏移波形条带的物理特性定量拾取裂缝 识别简单准确，结合声波系数与波形信息可实现天然裂缝的定性定量识别 
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