International Journal of Technology | 2026
Authors: Burlutskaya Z.V.; Sharko P.A.; Gintciak A.M.; Zakharov P.A.
DOI: 10.14716/ijtech.v17i2.8242
Journal: International Journal of Technology
Year: 2026
Publisher: Faculty of Engineering, Universitas Indonesia
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 1
This study develops an intelligent decision support tool for managing complex production systems using the example of a field development system. The problem solved in the study lies in the limitations of existing tools for generating optimal and realistic scenarios for managing production systems due to the lack of consideration of local knowledge and target functions of individual production sites. The relevance of the work lies in the need to improve the quality of management decisions in complex production systems through decision support systems that consider the multi-agent nature of the interaction of system elements, considering the multiplicity of target functions and incomplete awareness of individual agents. The result of the work is the developed multi-agent field development system, consisting of an environment presented as a set of computational models and intelligent agents that control production elements at different levels of the hierarchy. A hierarchical MAS with region–field–bush–crew agents was built using BDI and integrated with a Python-based simulation model. The effectiveness of the developed solution is verified by comparing the predicted oil flow rates for alternative optimization algorithms. Optimization of the distribution of teams across the region’s fields and the schedules of GTO and drilling using the MAS has increased the region’s production rate by 4.5%, ensured the feasibility of field plans, and reduced the costs of well logging and drilling. The testing results demonstrate the effectiveness of using multi-agent systems to generate field development scenarios. © 2026 Faculty of Engineering, Universitas Indonesia. All rights reserved.
Complex technical and socio-economic systems; Intelligent decision support system; Multi-agent interaction; Oil and gas industry; Simulation modeling