DeepNRMS: Unsupervised Deep Learning for Noise-Robust CO2 Monitoring in Time-Lapse Seismic Images

Monitoring stored CO2 in carbon capture and storage projects is crucial for ensuring safety and effectiveness. We introduce DeepNRMS, a novel noise-robust method that effectively handles time-lapse noise in seismic images. The DeepNRMS leverages unsupervised deep learning to acquire knowledge of time-lapse noise characteristics from pre-injection surveys. By utilizing this learned knowledge, our approach accurately discerns CO2-induced subtle signals from the high-amplitude time-lapse noise, ensuring fidelity in monitoring while reducing costs by enabling sparse acquisition. We evaluate our method using synthetic data and field data acquired in the Aquistore project. In the synthetic experiments, we simulate time-lapse noise by incorporating random near-surface effects in the elastic properties of the subsurface model. We train our neural networks exclusively on pre-injection seismic images and subsequently predict CO2 locations from post-injection seismic images. In the field data analysis from Aquistore, the images from pre-injection surveys are utilized to train the neural networks with the characteristics of time-lapse noise, followed by identifying CO2 plumes within two post-injection surveys. The outcomes demonstrate the improved accuracy achieved by the DeepNRMS, effectively addressing the strong time-lapse noise

Moment tensor inversion of perforation shots using distributed acoustic sensing

Distributed acoustic sensing (DAS) fibers have enabled various geophysical applications in unconventional reservoirs. Combined with perforation shots, a DAS fiber can record valuable guided waves that propagate in the reservoir formation and carry information about its properties. However, the representation of perforation shots as seismic sources, needed to conduct quantitative analysis, remains unknown. We model such sources using a superposition of three mechanisms for which we derive the moment tensor representation. Using field DAS data recorded in the same well where the perforations are located, we establish a workflow to invert the resolvable components of the total moment tensor for 100 different perforation shots. By scrutinizing the inversion results, we conjecture that the moment tensor can indicate how effectively a perforation shot creates micro-cracks in the surrounding rock. Furthermore, our inverted moment tensors form the basis for a subsequent elastic full-waveform inversion.Comment: This work has been submitted for publication in Geophysics under the reference GEO-2023-004

Urban Seismic Site Characterization by Fiber‐Optic Seismology

Accurate ground motion prediction requires detailed site effect assessment, but in urban areas where such assessments are most important, geotechnical surveys are difficult to perform, limiting their availability. Distributed acoustic sensing (DAS) offers an appealing alternative by repurposing existing fiber‐optic cables, normally employed for telecommunication, as an array of seismic sensors. We present a proof‐of‐concept demonstration by using DAS to produce high‐resolution maps of the shallow subsurface with the Stanford DAS array, California. We describe new methods and their assumptions to assess H/V spectral ratio—a technique widely used to estimate the natural frequency of the soil—and to extract Rayleigh wave dispersion curves from ambient seismic field. These measurements are jointly inverted to provide models of shallow seismic velocities and sediment thicknesses above bedrock in central campus. The good agreement with an independent survey validates the methodology and demonstrates the power of DAS for microzonation.Key PointsWe demonstrate the potential of DAS for site effect analysisDAS recordings are used to compute dispersion curves and horizontal‐to‐vertical spectral ratio (HVSR)Joint inversions suggest that the crystalline bedrock lies 115 m beneath Stanford University central campusPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154310/1/jgrb54043.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154310/2/jgrb54043-sup-0001-Text_SI-S01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154310/3/jgrb54043_am.pd