Geophysical log interpretation using neural network


Timely and effective interpretation of bore hole geophysical and formation well logs is vital in developing basic geological and hydrological data for ground water modeling. Information on local geological conditions may be estimated from many types of geophysical and formation logs; however, interpretations of these data can be subjective and time-consuming. A trained neural network can be used effectively and efficiently to complement manual log interpretation. In this paper, a neural network is developed to analyze geophysical well logs and to provide information on the subsurface strata classifications. An analysis is given on the neural network development process and data requirements. An overview is presented on the neural network optimization techniques, limitations, and the strength of the approach in well-log interpretation.

Publication Title

Journal of Computing in Civil Engineering