【摘要】 轮南油田三叠系广泛发育低阻油层，但其成因机制尚不明确，储层流体识别准确率偏低，已成为制约该区油气高效勘探与开发的关键难题。为此，在综合岩石物理实验、地层水矿化度、测井响应及试油等资料的基础上，采用逐项排除思路，分别从储层物性与润湿性、地层水矿化度、黏土附加导电性以及钻井泥浆侵入等因素出发，对低阻响应的主控机制进行系统分析；然后提出以电阻率侵入因子Q作为区分低阻油层与正常油层的初步判别参数，并进一步引入ADASYN-LightGBM优化算法，将Q与机器学习模型联合构建低阻油层识别方法以提升干层区分能力。研究结果表明，轮南油田低阻现象主要与开发时序不一致引起的地层压力亏空及钻井液调整不及时导致的泥浆深侵入密切相关；所建模型的平均准确率与平均F1分数分别达到94.38%和94.41%，低阻油层识别准确率达到96.77%，有效提高了低阻油层与干层的识别效果。实例应用表明，Q参数可用于低阻油层的快速初判，但单参数在干层识别方面存在局限；结合ADASYN-LightGBM可显著提升复杂类别的判别能力，为轮南油田三叠系低阻油层识别与同类区块推广应用提供了方法参考。更多还原

【Abstract】 The Triassic reservoir in the Lunnan Oilfield is characterized by widespread low-resistivity oil reservoirs. However, the genesis of these reservoirs remains poorly understood, and the accuracy of reservoir fluid identification is still relatively low, posing a major challenge to efficient exploration and development in the area. Based on an integrated dataset comprising rock-physics experiments, formation-water salinity data, well logs, and well-testing results, this paper adopts an elimination-by-factors strategy to systematically investigate the dominant mechanisms controlling low-resistivity responses, with particular focus on reservoir properties and wettability, formation-water salinity, clay-related additional conductivity, and drilling-mud invasion effects. On this basis, the resistivity invasion factor Q is proposed as a preliminary indicator for distinguishing lowresistivity oil reservoirs from normal-resistivity oil reservoirs. Furthermore, an ADASYN-LightGBM optimization scheme is introduced, in which the resistivity invasion factor Q is jointly incorporated into a machinelearning framework to enhance the discrimination of dry layers. The results indicate that the low-resistivity phenomenon in the study area is primarily associated with formation pressure depletion caused by asynchronous development scheduling, as well as deep mud invasion resulting from untimely drilling-fluid adjustment. The proposed model achieves an average F1-score of 94.38% and an average accuracy of 94.41%, with anidentification accuracy of 96.77% for low-resistivity oil reservoirs, effectively improving the identification performance between low-resistivity oil reservoirs and dry layers. These findings suggest that Q can serve as a rapid screening parameter for low-resistivity oil reservoirs. However, its effectiveness is limited when used as a single parameter for dry-layer discrimination. By integrating ADASYN-LightGBM, the classification capability for complex reservoir types can be substantially improved, providing a reference for identifying Triassic low-resistivity oil reservoirs in the Lunnan Oilfield and for applications in analogous fields.更多还原