深层-超深层碳酸盐岩储层精细地质建模技术进展与攻关方向

深层-超深层碳酸盐岩油气是业界普遍关注的热点和重点领域。如何精准刻画深层-超深层碳酸盐岩储集体空间展布及储集参数分布特征，是高效勘探开发面临的重大技术问题。在对储层地质分析、测井评价、地震预测、地质建模等相关技术发展现状分析的基础上，针对深层-超深层碳酸盐岩储层研究面临资料少、品质差、精度低，加之储层非均质性强等难题，深入开展了深层碳酸盐岩优质储层发育机理与分布规律研究，研发集成了深层碳酸盐岩储层描述与建模的关键技术系列，包括：(1)多尺度、多属性深层碳酸盐岩储层知识库构建技术；(2)地质分析新技术——从宏观到微观的储层地质观测分析技术，储层微区原位沉积、成岩环境定性-定量分析技术，储层发育机理与过程实验和数值模拟分析技术；(3)深层碳酸盐岩储层测井解释新技术——基于全域测井仿真的储层类型识别与参数定量评价技术，基于机器学习的沉积微相识别技术；(4)深层碳酸盐岩储层地震预测新技术——深层碳酸盐岩地震岩石物理建模技术，岩石物理引导的机器学习储层参数预测与不确定性评价技术；(5)深层碳酸盐岩地质建模新技术——多点地质统计学新算法，地质过程模拟技术，人工智能地质建模技术。分别建立了面控、断控...

Progress and direction of geological modeling for deep and ultra-deep carbonate reservoirs

Exploration and development of deep and ultra-deep carbonate reservoirs have been a hot and key research topic in the industry. Accurately depicting the spatial distribution and physical property parameters of the reservoirs has been a major challenge for an efficient oil and gas exploration and development. Based on an analysis of current development of reservoir geological analysis， logging evaluation， seismic prediction， geological modeling and other related technologies， this study is focused on figuring out the development mechanisms and distribution patterns of high-quality deep carbonate reservoirs by overcoming the data issues （scarcity， low quality and inaccuracy） and the high heterogeneity nature of the reservoir. A series of key technologies for characterization and modelling of the deep carbonate reservoirs have been developed， including technologies for construction of multi-scale and multi-attribute deep carbonate reservoir knowledge base； new technologies for geological analysis such as macroscopic to microscopic geological observation， in-situ micro-area qualitative and quantitative analysis for reservoir sedimentation and diagenetic environment， experiment and numerical simulation technologies for mechanism and process of reservoir development； new logging interpretation technologies， such as reservoir type identification and quantitative parameter evaluation based on global logging simulation， and sedimentary microfacies identification based on machine learning； new seismic prediction methods， such as seismic petrophysical modeling， machine learning technologies for rock physics guided reservoir parameter prediction and uncertainty evaluation； new geological modeling technologies such as new algorithm of multipoint geostatistics， geological process simulation， and geological modeling based on artificial intelligence. The technological processes of geological modeling of carbonate reservoirs under the control of karst unconformity， fault and sedimentary facies have been established respectively and applied to oil and gas reservoirs in Tahe， Shunbei and Yuanba blocks in the Tarim Basin and the Sichuan Basin， providing scientific basis for exploration and development deployment. The future research direction of geological modeling for deep and ultra-deep carbonate reservoirs is also predicted： updating geological knowledge base to support geological modeling； expanding the modeling technology based on geological process and improving its application； developing geophysical interpretation and prediction technologies based on artificial intelligence to improve the ability to depict complex reservoirs； developing new modeling methods based on artificial intelligence to continuously improve the accuracy of reservoir characterization and the reliability of models； and establishing rapid updating technology of geological models for deep reservoirs to continuously improve the efficiency and accuracy of model updating.