1887

Abstract

Summary

In the processes of exploration, allocation of producing intervals, and development of hydrocarbon deposits, the important part is the accurate determination of the poro-perm properties. According to the complex of laboratory studies of the reservoir rocks from the West-Shebelynska area (depth interval 4929–5380 m), the authors predicted the permeability coefficient using the quantitative distribution of different types of voids in (the reservoirs of different types (intergranular, secondary, and fractured). With the help of Multiple Linear Regression (MLR), it was established that the filtration of fluid in the investigated complex reservoir rock samples occurs both in intergranular and secondary voids as well as in fractures. It is shown that Artificial Neural Network (ANN) and Multiple Nonlinear Regression (MNLR) algorithms can provide a stable model with a high degree of confidence that can be used to predict the permeability coefficient at the intervals studied.

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/content/papers/10.3997/2214-4609.201903205
2019-11-12
2024-04-29
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References

  1. Antoniuk, V. V, Bezrodna, I. M., & Petrokushyn, O. Y.
    (2019). Comparison of the methods for reservoir properties evaluation and prediction of permeability by the void space structure of the reservoir rocks (on the example of the West-Shebelynska area). 18th International Conference on Geoinformatics-Theoretical and Applied Aspects. https://doi.org/10.3997/2214-4609.201902127s
    [Google Scholar]
  2. Bezrodna, I. M.
    (2014). Evaluation of pore space structures of carbonate rocks by results of acoustic research under variable pressure. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 23–30.
    [Google Scholar]
  3. Chollet, F.
    (2018). Deep learning with Python. Manning Publications.
    [Google Scholar]
  4. Cranganu, C., Luchian, H., & Breaban, M. E.
    (2015). Artificial intelligent approaches in petroleum geosciences. Springer.
    [Google Scholar]
  5. Rybalka, S., & Karpenko, O.
    (2016). Central part of Dnieper-Donets basin: Reservoir properties of deep-laid terrigenous rocks. Visnyk of Taras Shevchenko National University of Kyiv. Geology, (1), 56–59.
    [Google Scholar]
  6. Urdan, T. C.
    (2011). Statistics in plain English. Routledge Taylor & Francis Group.
    [Google Scholar]
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