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The goal of this paper is to present the use of the neural networks method for the estimation of interval times. An important parameter for shale formations is the Total Organic Carbon content (TOC). Acoustic logging constitutes the data necessary in methods assessing the TOC (CARBOLOG, Passey). Newly drilled boreholes feature a complete set of geophysical logging. Nevertheless, there are archival boreholes for which acoustic logging is usually missing or was performed only for a limited depth interval. In such cases it is impossible to perform the quantitative assessment of the TOC parameter using the above mentioned methods. Therefore, in order to calculate the TOC parameter, the neural networks method can be used for the estimation of interval times in archival boreholes. Many types of networks were tested for various input variables constituted by the borehole data, obtaining high values of the correlation coefficients R (0.95÷0.97) between acoustic logging and logging resulting from the use of neural networks.

Estimation of interval times in the profile of Cambrian and Silurian sediments by means of the neural networks method