Neural Network Inversion of EM39 Induction Log Data

Abstract

A modular neural network (MNN) is applied to invert well-logging curves from a Geonics EM39 induction-logging tool. In the interpretation scheme, there are several subsets of networks that depend on the relative resistivities of adjacent layers, e.g. R1>R2>R3, R1<R2>R3, etc. The well-logging curves are subdivided into fixed-length windows by each sub-network. The results are estimations of resistivity and thickness of each layer. The networks are examined for their ability to compute the correct output patterns for the corresponding input patterns of the training set, and the ability to interpret the new patterns that are not present in the training set. The networks are tested using synthetic and field data. The results show that the neural networks can facilitate interpretation of well-log data. When tested on a multi-layer case, the large shoulder effects can make the prediction difficult, especially for the thin resistive and conductive layers. However, the estimates of resistivity and thickness for each layer are sufficiently accurate that an interpreter will not be mislead as to the geological material properties. This approach could also be used for other logging tools or suites of tools.KeywordsApparent ResistivityRidge RegressionTraining PatternLocal ExpertSynthetic ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.