The usage of optimization algorithms for engineering calculations in high uncertainty conditions

UDK: 519.87:622.276
DOI: 10.24887/0028-2448-2019-11-149-152
Key words: conceptual design, integrated design, ERA:ISKRA information system, mathematical optimization, multivariate calculations, uncertainty, metamodeling
Authors: A.F. Mozchil (Gazpromneft NTС LLC, RF, Saint-Petersburg), D.E. Dmitriev (Gazpromneft NTС LLC, RF, Saint-Petersburg), N.Z. Bazyleva (Gazpromneft NTС LLC, RF, Saint-Petersburg), A.A. Kurilenko (Gazpromneft-Razvitie LLC, RF, Saint-Petersburg), A.D. Ledovsky (IBM Science and Technology Center, RF, Moscow), M.V. Golitsyna (IBM Science and Technology Center, RF, Moscow)

When making proved engineering decisions, it is necessary to consider relations between a geological formation, a well and a surface infrastructure. It requires conduction of multivariate calculations under uncertainty conditions. Gazprom Neft uses its own information system for integrated conceptual design – ERA:ISKRA. The technology that stands behind the system realizes batch calculation approach currently based on complete grid of all parameters combinations. When working with large datasets, the complete grid approach appears to be computationally expensive. Thus, time-effective algorithm for global optimization is needed.

Current paper formulates the problem in terms of mathematical optimization. Different approaches were analyzed: classical design of experiments, derivative-free methods, metaheuristics, and metamodeling techniques. New metamodeling approaches are proposed. All algorithms were transformed to fit the current problem in terms of business constrains. Dataset of 864 pre-calculated options was used for the testing purpose. Two metrics were used: number of iterations to convergence and convergence at the certain iteration. Testing results show that proposed algorithms provide a significant improvement in the optimal solution search time for ERA:ISKRA. This should allow to enhance the process of conceptual design and rearrange time from routine operations to detailed research of the optimal solution.


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