An approach based on regularized back-propagation neural networks can be used to estimate the missing logs, or parts of those logs, in wells with incomplete log suites. This is done by first analyzing the interdependence of the various log types in a training well that has a complete suite of logs, and then applying the network to nearby wells whose log suites are incomplete to estimate the missing logs in these wells. The accuracy of the method is evaluated by blind tests conducted on real well-log data. These tests indicate that the method produces accurate estimates that are close to the measured log values, and the method can thus be an effective means of enhancing limited suites of wire-line logs. Moreover, this approach has several advantages over the ad hoc practice of manually patching the missing logs from the complete log suites of proximate wells because it is automatic, objective, completely data driven, inherently nonlinear, and does not suffer from the overfitting difficulties commonly associated with conventional back-propagation networks. Additionally, it seems that an accurate selection of the optimal input log types is not necessary because redundant input containing several logs yields reasonably accurate results as long as some of the logs in the input are sufficiently correlated with the missing log.