Petrophysical seismic images obtained with artificial neural networks as prior models for full-waveform inversion: A case study from Colombia

Iturraran-Viveros, Ursula (Facultad de Ciencias, Universidad Nacional Autónoma de México (UNAM)) | Muñoz-García, Andrés M. (Instituto de Minerales CIMEX, Universidad Nacional de Colombia, Medellín) | Parra, Jorge O. (JPGeosciences, Helotes, Texas, USA)

OnePetro 

SUMMARY A common application in seismic imaging of machine learning algorithms (Artificial Neural Networks) is to produce petrophysical models at seismic scale combining well-log information and seismic data. Here we use these resulting models as prior inputs in full-waveform inversion (FWI). We compute instantaneous seismic attributes to a stacked P-wave reflected seismic section in the Tenerife field located in Colombia and train Artificial Neural Networks (ANN's) to estimate P-wave velocity V The logs are provided by a well near the survey line, allowing images of different rock properties to be used in the inversion of velocities. This process allows us to build an initial estimate of the earth property model, which is iteratively refined to produce a synthetic seismogram (by means of forward modeling) to match the observed seismic data. A nonlinear least-squares inversion algorithm minimizes the residual (or misfit) between observed and synthetic full-waveform data improves the P-wave velocity resolution.