Understanding Subsurface Fracture Evolution Dynamics Using Time‐Lapse Full Waveform Inversion of Continuous Active‐Source Seismic Monitoring Data

Abstract

Predicting the behavior, geometry, and flow properties of subsurface fractures remains a challenging problem. Seismic models that can characterize fractures usually suffer from low spatiotemporal resolution. Here, we develop a correlative double‐difference time‐lapse full waveform inversion of continuous active source seismic monitoring data for determining high‐spatiotemporal‐resolution time‐lapse Vp models of in‐situ fracture evolution at a shallow contamination site in Wyoming, USA. Assisted by rock physics modeling, we find that (a) rapidly increasing pore pressure initializes and grows the fracture, increasing the porosity slightly (from ∼13.7% to ∼14.6%) in the tight clay formation, thus decreasing Vp (∼50 m/s); (b) the fluid injection continues decreasing Vp, likely through the introduction of gas bubbles in the injectate; and (c) final Vp reductions reach over ∼150 m/s due to a posited ∼4.5% gas saturation. Our results demonstrate that high‐resolution Vp changes are indicative of mechanical and fluid changes within the fracture zone during hydrofracturing.

Seismological Constraints on the Causes of Intraplate Earthquakes in Yangjiang Region, South China

“…Noticing that most portions of the LVZ lie under the water of Yangbian Bay, we further speculate that the seawater could probably penetrate to the depth over time in the long term through the existing fractures along the fault zones, especially in the intersection region of multiple faults under the Yangbian Bay. In this case, seawater that saturates the fractures would further alter the elastic properties of rocks in the fault zone (Liu et al., 2023) and contribute to the low velocities from the surface to 4 km depth.…”

Seismic Monitoring at the Farnsworth CO2-EOR Field Using Time-Lapse Elastic-Waveform Inversion of 3D-3C VSP Data

“…Generally, there are two effective strategies for the implementation of the second step [7]: (1) sequential inversion, and (2) double-difference inversion. The latter strategy has a theoretically better performance, obtained by removing the negative influence of not matched baseline data [7,8], and [11]; however, it is less robust as it requires that time-lapse seismic data have excellent repeatability between surveys [10]. Despite this, most TLFWI experiments still utilize acoustic assumption because of the limit of computational capability.…”