Engineering 4D seismic monitoring: A data-driven blueprint from the Devine hydrogen test site

We present a data-driven blueprint for engineering 4D seismic monitoring by integrating ultra-dense 3D surface seismic and vertical seismic profile (VSP) data at the Devine hydrogen test site with full-waveform modeling of 4D responses from reservoir simulations. Seismic data across a wide fold range (1–1600) are mined to quantify signal-to-noise ratio (S/N) and repeatability (normalized root mean square [NRMS]), producing fold versus quality curves and maps with quantified uncertainty. This transforms 4D survey design from guesswork into a structured engineering process. The shallow, weakly reflective Olmos target requires higher fold for reliable plume detection. Spatial S/N maps also reveal site-specific near-surface “thumbprints,” areas with degraded S/N and elevated NRMS due to shallow heterogeneity, that must be empirically mapped to enable robust 4D strategies. We establish a quantitative design framework by linking NRMS, S/N, and acquisition parameters through the standard acquisition equation, forming a modern “box test” for 4D feasibility. Applying this blueprint to the upcoming hydrogen injection shows that small volumes (approximately 2 tons) of hydrogen, a highly responsive seismic tracer, can be tracked via distributed acoustic sensing (DAS) VSP, crosswell DAS, and surface seismic. Full-waveform modeling confirms strong 4D signatures across all methods, with crosswell DAS offering unmatched spatial resolution for imaging plume geometry and saturation. This integrated workflow, grounded in field data and modeling, delivers a transparent, reproducible foundation for 4D survey design. As Devine becomes a shared hydrogen injection testbed for academia and industry, it provides a unique platform for benchmarking monitoring technologies under controlled conditions, accelerating innovation and public trust in subsurface energy solutions.