Seismic characterization of geologically complex geothermal reservoirs by combining structure-oriented filtering and attributes analysis

Abstract

In geothermal systems, the geologic structure is a decisive factor in controlling fluid flow. Several geothermal systems around the world are located in complex geological structures, such as closely spaced and intersecting faults and fractures, folds, and dykes. Seismic data provides high-resolution images of complex structures that can be used to explore geothermal reservoirs. In fact, faults and fractures have subtle geologic edges that produce significant lateral variations in seismic data. Volumetric seismic attributes are quite effective for detecting these discontinuities in seismic data but are sensitive to noise and velocity accuracy. In North China, a deeply buried carbonate structure presents a geologically complex system with fault-controlled geothermal circulation. However, due to the deep burial depth and structural complexity, seismic reflection beneath the buried-hill has a low-quality, making it difficult to detect the characteristics of the fault edges. In this study, we first develop a structural architecture of the buried-hill carbonate reservoirs to show the development characteristics in vertical and plane directions. Next, we propose an intelligent workflow based on structure-oriented filtering and sensitive seismic attributes to investigate structural complexity (faults and fractures) in the basin for geothermal reservoir exploration. The results show that by combining the proposed structure-oriented filtering and sensitive volumetric seismic attributes, we can generate high-resolution seismic images to characterize fault edges, geometries, and fracture anomalies in structurally complex settings in order to identify upflow locations along the faults and fractures in geothermal systems. We further validated the fracture networks distribution by superimposing the extracted attributes with seismic fault networks and fracture density map using multilayer feedforward neural network. This workflow provides a reliable basis for the characterization of geothermal reservoirs in deep-buried and structurally controlled geothermal fields.