Bi Xueli, Li Juhua, Lian Cuihao
School of Petroleum Engineering, Yangtze University, Wuhan, 430100, China.
Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering, Yangtze University, Wuhan, 430100, Hubei Province, China.
Sci Rep. 2024 Oct 30;14(1):26107. doi: 10.1038/s41598-024-77300-1.
Conventional logging interpretation methods qualitatively identify shale reservoirs using shale attribute parameters and interpretation templates. However, improving the identification accuracy of complex shale reservoirs is challenging due to the numerous evaluation parameters and the complexity of model calculations. To quantitatively characterize high-quality shale reservoirs effectively, this study utilizes two wells in the Fuling shale gas field as examples and establishes a comprehensive evaluation method for identifying high-quality shale gas reservoirs utilizing multi-fractal spectral analysis of well logs. First, the conventional well logs are qualitatively analyzed and evaluated via multiple fractals and R/S analysis. Subsequently, a gray relational analysis is employed to combine the production well logging, which reflects dimensionless productivity contributions, with the fractal characteristics of conventional well logs to obtain the corrected weight multifractal spectrum width ∆α' and fractal dimension D'. Comprehensive fractal evaluation indices λ and γ are introduced, forming three categories of productivity evaluation standards for shale gas reservoirs characterized by fractals. Finally, a validation well is employed to demonstrate the effectiveness of the evaluation method. The results indicate that the identification of high-quality shale gas reservoirs based on the above comprehensive fractal evaluation method can reflect the productivity classification level of fractured well sections, simplify the calculation of formation evaluation parameters, and avoid the problem of poor correlation between predicted sweet spot zones and gas production. This approach has wide applicability and value for identifying high-quality reservoir areas in shale gas reservoirs and provides technical support for the effective large-scale development of shale reservoirs.
传统测井解释方法利用页岩属性参数和解释模板对页岩储层进行定性识别。然而，由于评价参数众多且模型计算复杂，提高复杂页岩储层的识别精度具有挑战性。为了有效定量表征优质页岩储层，本研究以涪陵页岩气田的两口井为例，建立了一种利用测井曲线的多重分形谱分析来识别优质页岩气储层的综合评价方法。首先，通过多重分形和R/S分析对常规测井曲线进行定性分析和评价。随后，采用灰色关联分析将反映无量纲产能贡献的生产测井与常规测井的分形特征相结合，得到校正权重多重分形谱宽度∆α'和分形维数D'。引入综合分形评价指标λ和γ，形成以分形为特征的页岩气储层三类产能评价标准。最后，利用一口验证井验证了评价方法的有效性。结果表明，基于上述综合分形评价方法识别优质页岩气储层，能够反映压裂井段的产能分级水平，简化地层评价参数计算，避免预测甜点区与产气之间相关性差的问题。该方法在页岩气储层优质储层区域识别方面具有广泛的适用性和价值，为页岩储层的有效大规模开发提供了技术支持。
