Automating seismic-well tie via self-supervised learning

As an essential process in subsurface interpretation, seismic-well tie aims at calibrating the well measurements in depth domain with the seismic records in time domain for reliable reservoir mapping and modelling. Such a process usually requires massive manual efforts, primarily extracting source wavelets to synthesize seismograms from well measurements and stretching/squeezing synthetic seismograms to match actual seismic signals, and thus becomes time-consuming and labour intensive with the amount of wells expanding in a field. This paper formulates the seismic-well tie problem from the perspective of computer vision and, considering the lack of manually prepared training labels in most of real projects, proposes automating it by a self-supervised workflow of two key components. The first component is to extract time-variant wavelets in the target interval using a dual-task autoencoder, which is optimized by maximizing the spectrum similarity between the extracted wavelet and the actual seismic. The second component is to estimate the corresponding time-shift between a pair of the synthetic seismogram and the actual seismic using a flow net, which is trained by a pseudo dataset derived from the actual well measurements. More specifically, the data generation consists with defining one-dimensional time-shift curves to warp the original less accurate time-depth relationships and then synthesizing the corresponding seismograms based on the convolutional model at all available wells. When turning to the stage of inference at a target well, the proposed workflow automatically extracts a representative wavelet, generates the corresponding synthetic seismogram from the original time-depth relationship and estimates the time-shift curve necessary for revising the original time-depth relationship that would lead to an optimal tying with the actual seismic at the well. The proposed workflow is tested on two field datasets, and both results demonstrate improved tying over traditional methods such as statistical wavelet extraction and dynamic time warping.