A sedimentary mineral prediction analysis method and system based on sedimentary microfacies and knowledge graph

Abstract

The application discloses a sedimentary mineral prediction analysis method and system based on sedimentary microfacies and a knowledge graph, relates to the technical field of mineral prediction analysis, and can recognize the time sequence evolution process of microfacies from lithological profiles, logging data, sequence structures and other multi-source data through a sedimentary evolution sequence extraction module, and construct a structured evolution matrix, thereby reconstructing the evolution logic of a sedimentary environment in the time dimension. In the original method, parameters such as porosity and permeability are usually processed separately in reservoir evaluation, and are discontinuous in space and not coupled between attributes. However, the system matches key reservoir parameters in seismic inversion and logging interpretation with a sedimentary evolution structure through a multi-dimensional reservoir attribute coding module, generates a three-dimensional attribute tensor RS, abstracts sedimentary evolution from artificially divided sequence units into an evolution structure matrix Mac, avoids subjective dependence of traditional qualitative description, establishes an evolution model in a data-driven manner, and enhances the restoration capability of a dynamic paleoenvironment.

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