| Autor: |
Petr Andriushchenko, N. Bukhanov, Anton Voskresenskiy, Anna V. Bubnova, Anna V. Kalyuzhnaya, Irina Deeva |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Data Science in Oil & Gas. |
| DOI: |
10.3997/2214-4609.202054026 |
| Popis: |
Summary In this paper, the authors investigated the approach of Bayesian networks to the analysis of parameters of oil and gas fields. The study of existing approaches and methods of probabilistic modeling in relation to the problems of analyzing the parameters of oil and gas fields based on production data showed that the best approach should have a number of important properties: the interpretability of the model, the ability to work with various types of data, and the ability to process distributions of sufficient dimension in a reasonable time. Bayesian networks were chosen as the main tool of work, since they allow to develop models that are understandable to the specialist and allow to do this entirely on data with minimal involvement of expert knowledge. The experiments have shown that Bayesian networks are able to simulate multidimensional distributions of field parameters; the developed model can also be used to reconstruct data gaps and assess the significance of variables. A comparison was made of different architectures of Bayesian networks with different score functions. It also shows an example of assessing the information significance of the parameters of deposits. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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