A shale oil ‘sweet-spot’ evaluation factor construction method based on quantitative analysis of the influence degree of geological controlling factors

Significant breakthroughs have been made in shale oil exploration within the Upper Member of the Lower Ganchaigou Formation (E 3 2 ) in the Yingxi area, Qaidam Basin. However, due to the poor continuity and strong heterogeneity of mixed shale oil reservoirs, the distribution of shale oil remains highly complex. Although existing technologies have supported commercial shale oil development in specific blocks across multiple basins, accurately evaluating and predicting ‘sweet spots’ in such mixed continental shale rock system with extremely strong heterogeneity still poses considerable challenges for achieving efficient large-scale development. In this study, a novel data-driven mathematical statistical evaluation method based on the quantitative analysis of the influence degree of geological controlling factors was proposed for the comprehensive classification and quantitative evaluation of shale oil ‘sweet spots’. Maximal information coefficient analysis (MIC) was applied to optimize five key evaluation parameters. Total organic carbon content (TOC) representing oil-bearing property, movable oil porosity (MOP) reflecting reservoir property, formation pressure coefficient (α p ) revealing mobility, normalized mineral brittleness index (B Norm ) indicating fracability, and buried depth (H) demonstrating recoverability. Based on the consistency between MIC scores and the corresponding fitting coefficients (R 2 ), optimal functional relationships between selected parameters and productivity (Jo) were identified. Entropy-Critic weight coefficient method (E/CWC) was employed to assign the weights to these parameters, geological significance revealing the order of importance on shale oil ‘sweet spots’ potential as: MOP > TOC>α p > B Norm > H. A comprehensive Q-value was constructed by integrating the optimal functional relationships with their corresponding weights, and the Q-values obtained from the E/CWC method were classified using hierarchical cluster analysis (HCA), identifying four types of shale oil reservoirs, of which the first three were defined as ‘sweet spots’. After verification through threshold determination based on the maximum Youden's Index, the accuracy rate reached as high as 96.36 %, which is 7.27%∼29.09% higher than that of previous methods. Finally, the rationality of MIC-E/CWC-HCA was further confirmed through ‘blind wells’, demonstrating that the objective weighting method with multiple parameters has high credibility. Type A and B ‘sweet spots’ demonstrate significantly higher drilling ratios in horizontal wells across the study area, which is related to the development of high-quality reservoir and productivity. The research results have excellent generalizability and extensibility, and provide the reliable methodological reference for the quantitative evaluation and optimized development of lacustrine mixed shale oil ‘sweet spots’.