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Building subsurface models with horizon-guided interpolation and deep learning: Application to the Volve field

Subsurface modeling plays an important role in hydrocarbon exploration but remains a challenging task that typically requires a full and reasonable integration of geophysical observations and geologic constraints. We present a workflow to fully utilize seismic amplitudes, well-log properties, and interpreted seismic structures to build geologically reasonable models. We take the Volve field data as an example and apply our workflow step by step as follows: First, we perform some preprocessing on the provided Volve seismic data, horizons, and well-logs to remove anomalous values and adjust seismic-well ties in the depth domain. Second, we use a dynamic-programming based method to infill gaps and refine the vertical positions of the provided horizons and efficiently pick more horizons. We further use the horizon surfaces to interpolate a relative geologic time (RGT) volume which can be considered as an implicit structural model representing seismic structural and stratigraphic features. Third, we integrate the provided well-logs and the computed RGT volume to interpolate a subsurface model that conforms to both well-log properties and seismic struc-tural and stratigraphic features. Finally, we propose a multi-scale neural network to predict a final subsurface model by using a combination of the seismic data and the interpolated model as inputs, and using the well-log data as training labels. Inputing the interpolated model to the network helps to provide a low-frequency control for obtaining a more stable prediction. The results show that our workflow is able to produce geologically reasonable subsurface models with high lateral continuity and vertical resolution.