Zavyalov D., Zakharova A.A., Shklyar A.V., Bagutdinov R.A. —
An integrated approach to modeling by an example of a landfill of disposal of liquid oil waste
// Программные системы и вычислительные методы. – 2017. – № 1.
– С. 22 - 30.
DOI: 10.7256/2454-0714.2017.1.22156
URL: https://e-notabene.ru/itmag/article_22156.html
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Аннотация: The article proposes an integrated approach to modeling, which consists of the complement of geological models of reservoir with models of infrastructure objects. The authors also present the method of communication between these models in area of the landfill of disposal of liquid oil waste. The proposed approach allows applying the principle of multi-level hierarchy of models of objects for various tasks with an appropriate degree of detail. Using this approach, it is possible to obtain an integrated 3D-model of the deposit or landfill that includes detailed geological static model of the reservoir, dynamic forecasting model of its development or injection of waste, as well as model of ground-based infrastructure of the deposit, with varying degrees of detail. The method developed by authors allows to pair models of different types and to supplement models of geological formations by the technological models of infrastructure. The proposed integrated approach to modeling of deposits and landfills of waste disposal and to the management of the extraction implements the principle of a multi-level hierarchy of models of objects of varying degrees of detail. The developed method of data exchange between models allows supplementing geological models of layers with technological models of objects of ground infrastructure introducing the interfacing of models of different types to create a complex model. This approach allows improving the quality of risk assessment in the performance of the forecast by uniting all specialists into a single system with the aim of increasing the effectiveness of operational management. The application of this approach allows evaluating the full complex of development risks more qualitatively than separate modeling of processes.