| 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. |
