Predicting missing sonic logs with seismic constraint

Abstract

Compressional and shear sonic transit time logs (DT and DTS, respectively) provide important petrophysical and geomechanical information for subsurface characterization. However, they are often not acquired in all wells because of cost limitations or borehole problems. We develop a method to estimate the DT and DTS simultaneously from other commonly acquired well logs such as gamma ray, density, and neutron porosity. Our method consists of two consecutive models to predict the sonic logs and the seismic traces at the well locations. The model predicting the seismic traces adds a spatial constraint to the model predicting the sonic logs. Our method also quantifies the uncertainties of the prediction, which come from the uncertainties of neural network parameters and input data. We train the network on four wells from the Poseidon data set located on the Australian shelf in the Browse Basin. We test the network on the other two wells from the Browse Basin. The test results indicate better predictions of sonic logs when we add the seismic constraint.