Abstract
This paper is related to neural network processing of near well bore geological logging data. The paper has been prepared to address the increased use and interest in artificial intelligence technologies in the interpretation of open-hole and/or casedhole formation evaluation data.
A typical neural network model is developed from a training well or wells where measurements are available. The model is tested on a validation well or wells before it is applied to a specific field /reservoir. Quite often models are developed with limited data: for example single well data should be retrained with multi well data to improve its scope and applicability. Unfortunately, some efforts to upgrade can result in choosing to use sub-optimal training algorithms because of the need to deal with an extensively increased data size. The consequence is a reduction in predictive accuracy. To improve accuracy, it is wise to have ways (1) remove faulty, redundant and insignificant data, (2) detect inconsistent data, (3) have the ability to "add", i.e., duplicate samples in key target zones.
This paper discusses how cluster analysis can be integrated with graphic visualization and fuzzy decision making to support training-data selection in field model development based on multiple well data. A case study using cased hole logging data from a pulsed neutron tool demonstrates the method and its application in predicting the response of open hole triple-combo logs (neutron porosity, formation density and deep resistivity).
