Automatic Detection of Geoelectric Boundaries According to Lateral Logging Sounding Data by Applying a Deep Convolutional Neural Network

摘要

Lateral logging sounding (LLS) is currently the only widely used Russian method of resistivity measurements, sensitive to vertical electrical resistivity in vertical wells. However, interpreting data measured by this method in thin-layered sections is difficult and requires the utilization of resource-intensive numerical simulation algorithms. Today, the development of computational methods and an increase in computer performance allow us to invert LLS data in the class of two-dimensional axisymmetric models. However, in virtue of the large number of difficulties associated with the nonlocal responses of the probes and their asymmetry, this process requires the active participation of a log analyst. One of the first issues is the creation of an initial approximation of the geoelectric model. It consists in splitting the target interval into layers within which the properties of the medium can be considered constant in the vertical direction, since LLS signals have a very complex shape in the intervals of alternation of beds with different resistivities. We propose applying a fully connected convolutional artificial neural network to automatically create sectional layering suitable for constructing the initial approximation of the geoelectric model for two-dimensional LLS data inversion, including vertical resistivity estimation. The neural network was trained and tested on the synthetic and field data measured in West Siberia. Based on the results of the testing, we established the workability of the proposed approach.