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Abstract

Summary

Methods of manual, algorithmic and neural network modeling technology are considered when solving the problem of detailed lithological dissection of a well section. The input data consists of the results from a typical set of well logging studies. The research object includes complex terrigenous-carbonate-chemogenic-magmatogenic formations of Mesozoic-Cenozoic age in the Transcarpathian Basin. The complexity of the problem lies in the significant variety of lithological rock types and facies conditions within the 3-kilometer well section, resulting in low contrast differentiation of geophysical parameters for many lithological types. This leads to uncertainty and reduced reliability in solving the geological problem using well logging data. The authors conducted manual geological interpretation based on core study results, typical geophysical characteristics for individual rock types, and reference literature. Special interpretation techniques and machine learning significantly reduced uncertainty and improved the accuracy of lithological subdivision.

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2025-04-14
2026-02-11
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References

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