岩矿测试2026，Vol.45Issue(5)：1109-1123,15.DOI:10.15898/j.ykcs.202511140272
基于机器学习与多源数据的鄂尔多斯盆地南部延长组致密砂岩脆性定量评价
Quantitative Brittleness Evaluation of Tight Sandstone in the Yanchang Formation,Southern Ordos Basin,Based on Machine Learning and Multi-Source Data
摘要
Abstract
The southern Ordos Basin is rich in tight oil resources.However,the strong heterogeneity of its main reservoir,the tight sandstone of the Yanchang Formation,limits the applicability of traditional brittleness evaluation methods based on single parameters or linear combinations,affecting the accurate identification of fracturing"sweet spots".To more authentically characterize reservoir brittleness,this study focused on the tight sandstone of the Yanchang Formation in the southern Ordos Basin.Through high-temperature and high-pressure triaxial mechanical tests,Brazilian splitting tensile strength tests,and X-ray diffraction analysis,the controlling mechanisms of mineral composition,temperature-pressure conditions,and laminated structures on rock mechanical properties were systematically revealed.Furthermore,fractal geometry theory was introduced,and the fractal dimension of post-fracture rock surfaces was used as an index to quantitatively characterize the true brittleness.The gradient boosting decision tree(GBDT)machine learning algorithm was employed to integrate four fundamental brittleness indices based on energy,elasticity,strength,and shear mechanisms,constructing a nonlinear comprehensive brittleness index(CBI)prediction model.The results indicated the following:(1)The fracture fractal dimension can effectively quantify the complexity of rock failure and show a regular decrease with increasing confining pressure;(2)The GBDT model can effectively capture the complex nonlinear relationships among multi-source parameters,achieving a coefficient of determination(R2)of 0.9749 for the full sample set and a cross-validation root mean square error(RMSE)of 0.0467;(3)Feature importance analysis revealed that the contribution of the strength parameter(the ratio of tensile to compressive strength,0.4584)was significantly higher than that of other parameters,indicating that the"tensile-compressive strength difference"was the dominant mechanical mechanism controlling brittleness development in the tight sandstone of this area;(4)By applying a dynamic rock mechanical parameter interpretation model established from well-logging data to the Chang-8 Member of the Yanchang Formation in Well HH26 in Honghe Oilfield on the souhern margin of the Ordos Basin,layers with higher CBI(0.70)exhibited a significantly lower on-site fracture presure gradient(1.83 MPa/100 m)compared to layers with lower CBI(0.51,2.11 MPa/100 m),and the predicted favoable brittleness zones showed good agreement with actual fracturing responses.A quantitative brittleness evalution method for tight sandstone was established in this study,using fractal dimension as the target and machine leaning as the means,clarifying the dominant role of strength parameters in brittleness evaluation.It can provde a quantitative reference for the optimization of fracturing intervals and efficient stimulation in tight oil reservois in the southern Ordos.关键词
鄂尔多斯盆地/致密油储层/岩石力学/脆性评价/机器学习/分形维数Key words
Ordos Basin/tight oil reservoir/rock mechanics/brittleness evaluation/machine learning/fractal dimension分类
天文与地球科学引用本文复制引用
江俊樟,刘岩,何建华,兰晋,宫铭,刘家威..基于机器学习与多源数据的鄂尔多斯盆地南部延长组致密砂岩脆性定量评价[J].岩矿测试,2026,45(5):1109-1123,15.基金项目
页岩油气富集机理与高效开发全国重点实验室开发基金项目(33550000-24-ZC0699-0057) （33550000-24-ZC0699-0057）
