Application of Euler deconvolution and a neural network system as interpretation aids for three component downhole TEM data

Abstract

The relationship between the secondary field due to a TEM source and a dipolar potential field has been documented (Dyck and West, 1984; Grant and West, 1965). In light of this relationship it is possible to utilise potential field interpretation aids when dealing with TEM data. Where only the axial component of the anomalous field is available, in the case of downhole TEM data, the implementation of these techniques is very restricted and may yield ambiguous model parameters, hence alternative techniques must be considered. Euler deconvolution may be used to obtain the approximate coordinates of the source even in the presence of an overburden response. A neural network system has been trained to output the dip and strike direction of a plate model using scaled field data as input. Utilising both the neural network and Euler deconvolution techniques, approximate model parameters may be computed directly from the field data. Synthetic examples are presented, showing that these techniques are applicable even in the presence of noise and conductive overburden. A field example from the Flying Doctor prospect is also presented. The computed model parameters were found to be consistent with previously published data (Cull, 1993).