A comprehensive logging evaluation method for identifying high-quality shale gas reservoirs based on multifractal spectra analysis

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.