Reconstruction of the Spatial Distribution of Filtration Properties of Heterogeneous Geologic Media Based on Variations of Microseismicity Resulting from Fluid Injection

Determining the properties of heterogeneous reservoirs based on microseismic evolution data is an important task in field development. Analyzing the propagation of microseismic events occurring during fluid injection/withdrawal provides valuable information about permeability and stress state of the reservoir. In this paper, we consider the inverse problem of determining reservoir filtration properties from microseismic event propagation data. For this purpose, the influence of various geological factors on the distribution of microseismic event sources is investigated. Machine learning methods were used to identify correlations between geologic model parameters and microseismicity evolution. Due to the insufficient variability of in-situ data, an artificial database of catalogs of microseismic events containing the coordinates of sources and their occurrence times was created to train the model. For this purpose, numerical modeling of fluid injection and generation of microseismic events in synthetic models of permeable media with different geological structure was carried out. Thus, a comprehensive approach to the restoration of filtration properties of heterogeneous reservoirs from microseismicity evolution data using machine learning methods is proposed. The proposed methodology can be applied to optimize field development, improve the efficiency of fluid extraction and reduce the risks associated with the occurrence of undesirable anthropogenic seismic activity.

anthropogenic seismicity, fluid filtration, permeability, numerical modeling, machine learning