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Feasibility study of seismic AVO-inversion and seismic inversion capabilities in conditions of acoustically weak-contrast reservoirs and host rocks

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Category: Content №3 2025

Last Updated on 25 June 2025

Published on 30 November -0001

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Authors:

A.Jangirov, orcid.org/0009-0003-5668-1896, “Professional Geo Solutions Kazakhstan” LLP, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

G.Umirova, orcid.org/0000-0001-5185-3132, Satbayev University, Almaty, the Republic of Kazakhstan

A.Abdullina*, orcid.org/0000-0003-0483-2798, Karaganda Technical University, Karaganda, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it., e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Karpenko, orcid.org/0000-0001-7753-9010, M.P.Semenenko Institute of Geochemistry, Mineralogy and Ore Formation of the National Academy of Sciences of Ukraine, Kyiv, Ukraine; NJSC “Naftogaz of Ukraine”, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (3): 021 - 028

https://doi.org/10.33271/nvngu/2025-3/021

Abstract:

Purpose. To improve the accuracy of lithotype classification and reduce uncertainty in hydrocarbon prospecting predictions under conditions of weak contrast in the elastic properties of reservoirs and the surrounding rock by testing a probabilistic approach to seismic data interpretation.

Methodology. The study applies a technological approach that includes:

1.    Constructing probability distribution functions for lithotypes based on lithological trend data from wells.

2.    Using inversion to create cubes of elastic properties and their further analysis using Bayes’ principle.

3.    Lithotype classification based on the probabilistic approach by aligning inversion data with probability distributions.

Findings. High-resolution seismic data processing was carried out successfully, which ensured their high resolution. Elastic property volumes were obtained and used to assess the probability of various lithotypes. The Bayesian approach to lithotype classification was implemented, demonstrating improved interpretation accuracy in conditions of low elastic property contrast. Promising structures were identified, confirming the effectiveness of the proposed approach in complex geological settings.

Originality. A probabilistic method for seismic data analysis was tested, based on the integration of probability distribution functions and inversion data. This methodology allows for accounting for lithological variability and uncertainties during interpretation, representing a novel approach for conditions of low elastic property contrast.

Practical value. The developed methodology can be applied in hydrocarbon exploration and field development when reservoir and host rock physical properties exhibit weak contrast. It helps reduce geological risks, optimize drilling site selection, and enhance the economic efficiency of geological exploration.

Keywords: hydrocarbon deposit, dynamic interpretation, seismic pre-stack inversion, probability-density functions, reservoir

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