A two‐dimensional walkaway vertical seismic profiling survey using distributed acoustic sensing was conducted at an onshore site in Japan. The maximum depth and the deviation of the observation well were more than 4,000 m and 81 degrees, respectively. Among the several methods for installing fibre optic cables, we adopted the inside coiled tubing method, in which coiled tubing containing a fibre optic cable is deployed. The signal‐to‐noise ratio of the raw shot gather was low, possibly due to poor coupling between the fibre optic cable and the subsurface formation resulting from the fibre optic cable deployment method and the existence of considerable tubewave noise. Nevertheless, direct P‐wave arrivals, P–P reflections and P–S converted waves exhibited acceptable signal‐to‐noise ratios after careful optimization of gauge length for distributed acoustic sensing optical processing and the application of carefully parameterized tubewave noise suppression. One of the challenges in current distributed acoustic sensing vertical seismic profile data processing is the separation of P‐ and S‐waves using only one‐component measurements. Hence, we applied moveout correction using two‐dimensional ray tracing. This process effectively highlights only reflected P‐waves, which are used in subsequent subsurface imaging. Comparison with synthetic well seismograms and two‐dimensional surface seismic data confirms that the final imaging result has a sufficiently high quality for subsurface monitoring. We acquired distributed acoustic sensing vertical seismic profile data under both flowing conditions and closed conditions, in which the well was shut off and no fluid flow was allowed. The two imaging results are comparable and suggest the possibility of subsurface imaging and time‐lapse monitoring using data acquired under flowing conditions. The results of this study suggest that, by adopting the inside coiled tubing method without drilling a new observation well, more affordable distributed acoustic sensing vertical seismic profile monitoring can be achieved in fields such as CO2 capture and storage and unconventional shale projects, where monitoring costs have to be minimized.
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