Deep-Learning-Based Noniterative 2D-Inversion of Unfocused Lateral Logs

The work deals with the development of methodological and algorithmic tools for the quantitative interpretation of oil well resistivity logs. We review the results of applying the neural-network-based approach to the inversion of resistivity logs measured at thinly bedded high-contrast environments. The capabilities of the proposed approach are demonstrated by the example of the algorithm for nonin-terative express inversion of unfocused lateral logs (BKZ). BKZ is the unfocused array logging method widely used in the Commonwealth of Independent States for oil well studies. BKZ logs are known for their complexity: The signals of unfocused gradient probes are highly affected by the medium properties below and above the measuring point. The developed algorithm is based on the inversion of full logs into the parameters of a 2D axisymmetric model of the medium, which allows naturally taking into account the influence of surrounding rocks and borehole conditions. Transition from the "layered" parametrization conventional for BKZ logs interpretation to a quasi-continuous change of properties along the well axis allows extracting meaningful information at every measurement point and constructing high -reso-lution geoelectric models of the sediments. The noniterative nature of the algorithm provides a high computing efficiency. This opens up the possibility of using the 2D inversion advantages to increase the reliability of the initial express interpretation results. Testing the algorithm on the practical data from West Siberian oilfields has revealed the field of its maximum efficiency, namely, study of impermeable and low -permeability sediments, such as the complex shaly caprocks and bituminous deposits of the Bazhenov Formation. With high-quality input data, the approach is also efficient for studying permeable terrigenous sediments.