与岩溶相关的碳酸盐岩缝洞型油藏在全球油气田开发中占据重要地位,尤其在深层—超深层条件下,其内部结构和充填改造作用表现出高度复杂性.明确古岩溶洞穴中充填物类型及充填程度,对储集空间有效性评价、开发策略优化及剩余油挖潜具有重要的理论和实际意义.本文在大量文献调研的基础上,系统梳理了岩溶洞穴充填相和洞穴碎屑充填相划分的方案,总结了洞穴内部充填结构地质认识的主要理论进展.通过调研古岩溶洞穴充填物识别与预测、充填程度判识的技术进展,总结了目前塔河地区构建的岩溶洞穴充填模式.研究表明洞穴充填相的识别研究进展主要体现在:①现代地表洞穴碎屑质充填相成因类型和古岩溶洞穴充填的划分;②针对于洞穴充填物的识别与预测、洞穴充填程度的判别.早期采用的方法普遍为利用测井和地震资料的定性、半定量化方法.随着人工智能技术的兴起,利用机器学习强大的泛化能力进行充填物、充填程度的识别与预测成为该领域的前沿技术研发方向;古岩溶洞穴充填模式建议在古岩溶缝洞系统的层次性结构框架内,利用水文地貌与洞穴发育部位的耦合关系,并结合实钻井揭示(或是采用预测手段)的洞穴内部充填物类型进行构建.在岩溶洞穴充填作用研究方面存在以下问题:①古岩溶洞穴充填物类型的划分依据主要是岩石物理组分的差异,而并没有体现充填物形成的动力学机制;②针对洞穴充填物的识别精度不足,导致无法完整地识别洞穴内部充填物序列;③目前普遍利用地震反演技术得到的洞穴充填预测的结果只能对泥质含量进行预测,无法对所有充填物充填程度进行准确评价,因而古岩溶暗河网络充填程度空间差异分布预测仍待深入攻关.基于目前存在的问题,本文认为利用人工智能技术开展洞穴充填物类型和充填程度的识别与预测是大势所趋.如何提高样本集的代表性、预测网络的准确性和泛化度是未来攻关的方向.

Karst-related carbonate fracture-cavity reservoirs play a vital role in global oil and gas field development.Especially under deep to ultra-deep conditions,their internal structures and filling-modification processes exhibit extreme complexity.Identifying the types and degree of fillings in paleokarst caves carries significant theoretical and practical value for evaluating effective reservoir space,optimizing development strategies,and tapping remaining oil potential.Based on an extensive review of the literature,this study proposes a systematic classification scheme for the filling phases and detrital filling phases of karst caves,highlighting key advancements in the geological understanding of internal cave filling structures.The article summarizes the current models of karst cave filling in the Tahe Area,focusing on technological progress in identifying and predicting filling materials and determining the degree of filling in paleokarst caves.Progress in identifying cave filling facies is primarily reflected in the genetic classification of modern surface cave detrital filling facies and the categorization of paleokarst cave fillings.Early methods for identifying and predicting cave filling materials and assessing filling degrees relied on qualitative and semi-quantitative approaches using logging and seismic data.With the advent of artificial intelligence(AI)technology,the application of machine learning's powerful generalization capabilities to identify and predict filling materials and degrees has emerged as a cutting-edge research direction in this field.The classification of filling modes in paleokarst caves suggests utilizing the coupling relationship between hydrogeology and cave development within the hierarchical structure framework of the paleokarst fracture-cave system.This approach,combined with the types of internal filling materials revealed by actual drilling data,facilitates the construction of filling models.However,current classifications of filling types in paleokarst caves primarily focus on differences in rock physical components,without adequately reflecting the dynamic mechanisms of filling formation.Additionally,the accuracy of identifying cave fillings remains insufficient,hindering the comprehensive determination of the sequence of fillings within caves.Currently,seismic inversion technology,commonly used for predicting cave fillings,can only estimate mud content and fails to accurately evaluate the degree of filling for all materials.Consequently,predicting the spatial distribution of filling degrees in paleokarst underground river networks requires further research and development.In light of these challenges,this article argues that leveraging AI technology to identify and predict the types and degrees of cave filling materials represents a promising trend.Future research should focus on improving the representativeness of sample sets,as well as the accuracy and generalization capabilities of prediction networks.