Geologic-time-based interpolation of borehole data for building high-resolution models: Methods and applications

Abstract

Integrating borehole data into a uniformly sampled grid is an essential but challenging task for seismic data processing and reservoir characterization. We propose an efficient geologic-time-based interpolation method to build subsurface models where the structures are consistent with the seismic structures and the vertical resolution of model properties is as high as well-log records. Based on a seismic image, we first compute a relative geologic time (RGT) volume that provides an implicit map of all the geologic structures in the seismic image. We then construct an interpolated model from borehole data by following constant RGT values (each one corresponds to a same geologic layer), and thus obtain a high-resolution model honoring both seismic structures and well-log values. Such a model could provide a low-frequency control for a deep learning or conventional inversion method to estimate reservoir properties, or it can be used as a reliable initial background model to improve the performance of full-waveform inversion. We use both synthetic and field data examples to demonstrate the effectiveness of our method even when reservoir properties are only observed at sparsely scattered locations. In comparison to the existing approaches, our method can produce a more geologically consistent subsurface model which can be used as a better initial model for a deep learning method to estimate a refined rock-property model from seismic data.