Research on the comprehensive dessert evaluation method in shale oil reservoirs based on fractal characteristics of conventional logging curves

Abstract The traditional logging evaluation of comprehensive sweet spots in shale oil reservoirs has problems such as complex explanatory parameters, incompatible quantitative characterization scales, and low-cost efficiency. A method based on the fractal characteristics of conventional logging curves is proposed to evaluate the comprehensive sweet spots of fractured horizontal wells in shale oil reservoirs. Firstly, the existing evaluation parameters and methods were reviewed, pointing out the limitations of traditional logging evaluation methods. Furthermore, we analyzed 63 fractured sections from three horizontal fractured wells in the Yingxiongling shale oil reservoir of the Qinghai Oilfield, using tracer monitoring data. By applying wavelet transform to reduce noise in high-frequency signals from conventional logging curves, we then used multifractal spectrum analysis and R/S analysis to extract the multifractal spectrum width (∆α) and fractal dimension (D) from four conventional logging attributes: natural gamma logging (GR), acoustic time difference logging (AC), density logging (DEN), and neutron logging (CNL). A multi-attribute comprehensive fractal evaluation index was developed by using the post-fracturing tracer monitoring profile as a constraint and applying the grey relational analysis method. This approach enabled a quantitative classification and evaluation of the key sweet spots in shale oil reservoirs after fracturing. The results show that the comprehensive fractal evaluation index of the high-yield well section after Class I layering is 0.75<∆ α‘<1, 0 < D‘<0.25; 0.35<∆ α‘<0.75, 0.25 < D‘<0.8 in the middle well section of Class II layer; Class III low production well Sect. 0<∆ α‘<0.35, 0.8<∆ α‘<1. Finally, a prediction model for physical property parameters characterized by fractals was introduced using machine learning algorithms, which is 31.9% more accurate than the conventional interpretation physical property parameter prediction model for the comprehensive sweet spot of fracturing. This evaluation method is a concise approach to comprehensively evaluate the sweet spot area based on the extraction of multifractal spectral characteristic parameters from conventional logging data. It is of great significance for characterizing the volume fracturing effect of shale oil and providing technical support for the effective development of shale on a large scale.