缝洞型碳酸盐岩储层地质建模研究进展

Advance in geological modeling methods of fracture-cavity carbonate reservoirs

缝洞型碳酸盐岩储层空间分布复杂、非均质性强,建立准确可靠的三维地质模型,是该类油藏高效开发的基础和前提。总结了缝洞型碳酸盐岩储层建模技术方法的发展历程,可分为3个阶段:第1阶段提出了“分区分带”和“岩溶相控”等储集体建模方法,以基于变差函数的两点统计学为主要模拟算法;第2阶段提出了溶洞内部结构建模方法,划分了溶洞类型,总结出不同类型溶洞组合方式,通过成因约束构建不同的岩溶相控模式,建模算法以基于目标与多点地质统计学为主;第3阶段进一步细化储集体成因,针对地下暗河等特定成因的溶洞储集体,通过野外露头与溶洞数据构建训练图像,将先验地质成因模式和后验地震响应相整合,构建综合约束概率体,建立的模型更加精细,能表征出暗河储集体的内部结构要素。对未来缝洞型碳酸盐岩储层地质建模的技术发展进行展望,指出断控岩溶储层建模方法亟需进一步研究,基于深度学习的人工智能地质建模方法是未来的主攻方向。

The fracture-cavity carbonate reservoirs is characterized by complex spatial distribution and high heterogeneity.The establishment of an accurate and reliable three-dimensional geological model is fundamental and essential for the efficient development of such reservoirs.This paper presents a comprehensive overview of the developing stages in the technology and methods employed for modeling fracture-cavity carbonate reservoirs.The evolution of fracture-cavity reservoir modeling can be delineated into three distinct phases:In the first phase,reservoir modeling techniques introduce concepts like "zone division" and "karstic control" as methods for modeling reservoir bodies,with a primary reliance on variogram-based statistical algorithms.In the second phase,it is emphasized of the modeling of internal cave structures,which involves categorizing cave types and summarizing different combinations of cave types.These endeavors are underpinned by the application of geological constraints to construct various karstic control models,with a predominant focus on target-based and multi-point geological statistics as modeling algorithms.In the third phase,the researcher further delve into the causal factors governing the formation of reservoir bodies,specifically focusing on factors such as underground rivers.For these unique causal factor-driven cave reservoirs,field outcrop and cave data were employed to construct training images.Mathematical integration of prior geological causative models and posterior seismic responses result in the development of comprehensive constraint probability bodies.The models generated in this phase exhibite finer detail and have the capacity to represent internal structural elements within underground river reservoirs.This paper concludes by offering a forward-looking perspective on the technological advancements in geological modeling of fracture-cavity carbonate reservoirs.It highlights the imperative need for further research in fracture-controlled karst reservoir modeling methods and underscores that the future trajectory lies in artificial intelligence geological modeling methods based on deep learning.