Forward modeling study of seismic acquisition for fractured soft structures in deep geothermal reservoirs

Abstract

Deep geothermal reservoirs are expected to serve as a sustainable resource for clean energy production, contributing to the achievement of global dual-carbon targets. This study analyzes the seismic acquisition method for soft-structure fracture zones in deep geothermal reservoirs through forward modeling analysis. Based on geological data from the Baoying area, China, a 2D geological model—integrating formation velocities, densities, and stochastic fracture media within the Upper Sinian-Middle Ordovician strata—was constructed for the forward modeling. To enhance the accuracy of seismic simulations and reduce numerical dispersion, high-order finite-difference methods were employed. A detailed theoretical analysis of seismic dispersion characteristics indicates that higher-order spatial and temporal differences can effectively mitigate numerical dispersion. Numerical seismic forward simulations were performed using a 10th-order difference accuracy, with a detailed analysis of acquisition survey parameters such as trace spacing, shot spacing, maximum offset, and record length. Simulated records for the geological model with and without fracture zones were compared, revealing distinct differences, particularly when fracture zones are located within high-velocity layers. Further analysis of pre-stack depth migration profiles with varying offsets, trace spacings, and shot intervals indicates that a maximum offset above 7000 m, a trace spacing of 5 m (or 10 m as a cost-effective option), and a shot interval of 40 m provide optimal imaging accuracy for fracture zones. These findings offer guidance for improving seismic imaging and interpretation of soft structures within fracture zones, thereby enhancing seismic exploration of deep geothermal reservoirs.

Keywords

Deep geothermal reservoirs / Soft structures / Fracture zones / Forward modelling / High-order finite-difference / Seismic acquisition

Funding

This study was funded by the Jiangsu Province Carbon Peak Neutral Technology Innovation Project in China (BE2022034).

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