Comprehensive interpretation of geochemical parameters for geosteering optimization on the example of the Bazhenov suite

UDK: 550.832.5
DOI: 10.24887/0028-2448-2026-3-26-30
Key words: geosteering, Bazhenov suite, pulsed neutron gamma-ray spectrometry logging, geochemical ratios, elemental composition, paleogeographic markers, machine learning, oil recovery factor, horizontal drilling, sedimentation
Authors: A.G. Manikin (WORMHOLES Research and Development, RF, Moscow; Saratov National Research State University named after N.G. Chernyshevsky, RF, Saratov); K.V. Andryukhin (WORMHOLES Research and Development, RF, Moscow; Saratov National Research State University named after N.G. Chernyshevsky, RF, Saratov); A.V. Gribanov (WORMHOLES Research and Development, RF, Moscow); O.N. Zhuravlev (WORMHOLES Research and Development, RF, Moscow); T.V. Khismetov (Research and Engineering Center GeotechnoKIN LLC, RF, Moscow); I.A. Usov (RussNeft PJSC, RF, Moscow); V.I. Zverev (Dukhov Automatics Research Institute, RF, Moscow; National Research Nuclear University МEPhI, RF, Moscow); A.S. Khomiakov (Dukhov Automatics Research Institute, RF, Moscow); A.A. Ryazanov (RITEK LLC, RF, Volgograd)

The article presents a comprehensive method for interpreting geochemical data to reconstruct paleogeographic sedimentation conditions and optimize geosteering. The relevance of the work stems from the increasing volume of detailed geochemical information (from pulsed neutron gamma-ray spectrometry logging) and the need for its effective use in petroleum geology. Using a case study of the elemental composition analysis of rocks from the Bazhenov suite (well XX9, Western Siberia), the authors demonstrate a transition from merely recording the concentrations of individual elements (Si, Al, Fe, Mn, S, etc.) to constructing a system of interconnected geochemical coefficients. Key ratios (Si/Al, Fe/Mn, (Mg+Ca)/Al, S/Fe, K/Al, and others) are interpreted as indicators of paleogeographic environments. They enable the reconstruction of oxidation-reduction conditions in bottom waters, changes in biological productivity, sources of terrigenous material, and sea-level fluctuations. Based on the analysis of coefficient trends throughout the section, six paleogeographic intervals were identified, reflecting the evolution of the Bazhenov basin. The practical outcome of the work is the identification of stable geochemical markers. Quantitative criteria are proposed for identifying siliceous (Si/Al > 5), argillaceous (Al/Fe > 3), carbonate ((Mg+Ca)/Al > 0,5), and organic-rich (Fe/Mn > 15 and S/Fe > 100) layers. These markers can be used for real-time lithological identification of rocks and for geosteering. Integration of geochemical logging into the drilling process and the application of the interpretation method enable the creation of intelligent dynamic reservoir models. This directly contributes to increasing the contact ratio with the productive reservoir and improving the efficiency of field development.

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