A computer implemented method for converting three-dimensional seismic data from a time domain to a depth domain, which comprises: training an artificial neural network using interval transit times from sonic logs for selected wells; predicting the interval transit times for selected wells without sonic logs within or near a reservoir interval of interest using the artificial neural network; producing for each selected well in the reservoir interval of interest time-depth pairs by numerically integrating the interval transit times for the selected wells with the sonic logs and the predicted interval transit times for the selected wells without sonic logs; converting the time-depth pairs for the selected wells to time-depth pairs along a seismic time horizon selected within the reservoir interval of interest; forming a reference horizon by realigning seismic traces in a three-dimensional seismic time volume to align the seismic time horizon with a time zero on each trace; assigning a relative depth to each seismic sample value and respective seismic attribute value at or near the reservoir interval of interest using the converted time-depth pairs; forming multiple structurally correct surfaces representing a time-depth horizon volume based on the relative depth assigned to each seismic sample amplitude value and respective seismic attribute value and on a corresponding time-depth pair along the seismic time horizon; and transferring each seismic sample value and respective seismic attribute value at or near the reservoir interval of interest from the seismic time volume to the multiple structurally correct surfaces in the time-depth horizon volume; constructing a three-dimensional geocellular model that contains the time-depth horizon volume using the multiple structurally correct surfaces; and transferring each seismic sample value and respective seismic attribute value from the multiple structurally correct surfaces in the time-depth horizon volume to the three-dimensional geocellular model. training an artificial neural network using interval transit times from sonic logs for selected wells; predicting the interval transit times for selected wells without sonic logs within or near a reservoir interval of interest using the artificial neural network; producing for each selected well in the reservoir interval of interest time-depth pairs by numerically integrating the interval transit times for the selected wells with the sonic logs and the predicted interval transit times for the selected wells without sonic logs; converting the time-depth pairs for the selected wells to time-depth pairs along a seismic time horizon selected within the reservoir interval of interest; forming a reference horizon by realigning seismic traces in a three-dimensional seismic time volume to align the seismic time horizon with a time zero on each trace; assigning a relative depth to each seismic sample value and respective seismic attribute value at or near the reservoir interval of interest using the converted time-depth pairs; forming multiple structurally correct surfaces representing a time-depth horizon volume based on the relative depth assigned to each seismic sample amplitude value and respective seismic attribute value and on a corresponding time-depth pair along the seismic time horizon; and transferring each seismic sample value and respective seismic attribute value at or near the reservoir interval of interest from the seismic time volume to the multiple structurally correct surfaces in the time-depth horizon volume; constructing a three-dimensional geocellular model that contains the time-depth horizon volume using the multiple structurally correct surfaces; and transferring each seismic sample value and respective seismic attribute value from the multiple structurally correct surfaces in the time-depth horizon volume to the three-dimensional geocellular model.