松辽盆地古龙地区页岩矿物成分与孔隙智能识别方法OA

With the continuous advancement in lacustrine shale oil exploration and development in China,traditional reservoir evaluation methods are facing a series of challenges in microscale characterization.In this study,we analyze the advantages and limitations of existing reservoir evaluation techniques and methods.Accordingly,a more intelligent,comprehensive shale reservoir evaluation method that integrates multidimensional data is proposed.Based on the Adaptive Pyramid Context Network(APCNet)for semantic segmentation,combined with the previously independently developed shale pore-fracture segmentation network(ShaleSeger),this method enables intelligent segmentation of minerals and pore structures within reservoirs.By further integrating image processing techniques with mathematical statistics,the method allows for both the quantitative calculation of the shale brittleness index and the fine-scale characterization of pore structures.The analytical results indicate that this proposed method serves to provide more specific,comprehensive,and quantitative analytical data for shale oil and gas exploration and development,as well as sweet spot identification.These analytical data in turn facilitate the quantitative evaluation of resource potential in shale hydrocarbon reservoirs and assist in the comprehensive assessment of relevant technical difficulties and economic benefits.The systematic solution established based on the proposed method offers a reliable basis for intelligent decision-making in the efficient exploration of lacustrine shale oil and gas.Additionally,this study presents a thorough analysis of challenges associated with current intelligent analysis techniques for lacustrine shale reservoirs and points out focus for future research.