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Дата издания
30.11.2025На каком языке издана
ISSN
2071-2227Область исследования
Тип исследования
Аннотация (краткое описание)
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 explorationDOI
10.33271/nvngu/2025-3/021Тип публикаций
Практическое значение
The practical significance of the developed methodology lies in its direct application to hydrocarbon exploration and field development where reservoirs and host rocks exhibit weak acoustic contrast. By integrating inversion data with a Bayesian probabilistic approach, the workflow successfully minimizes geological risks and reduces uncertainties in lithotype classification. In practice, this optimizes the selection of drilling sites, prevents non-productive drilling costs, and enhances the overall economic efficiency of geological exploration.Квартиль
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SCImago (Scopus)Impact Factor
1.8Количество цитирований
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es1605.pdf