Optimization and Control-Based Modeling of Oil Field Development in the Lower Kura Depression: A Case Study of the Kurovdagh Field
Energies, vol.19, no.12, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 19 Issue: 12
- Publication Date: 2026
- Doi Number: 10.3390/en19122873
- Journal Name: Energies
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Keywords: control-based modeling, decision-making, oil recovery, optimization, process control, production forecasting, reservoir modeling, Shewhart control charts
- Azerbaijan State University of Economics (UNEC) Affiliated: Yes
Abstract
This study proposes an integrated optimization and control-based approach for reservoir development analysis in the Kurovdagh oil field of the Lower Kura Depression. The methodology combines reservoir parameter evaluation with Shewhart statistical control charts to identify deviations in production performance, analyze water breakthrough processes, and support production optimization in mature reservoirs. Based on geological and production data, control charts were constructed to analyze oil production, water cut, injected water volumes, and well performance across multiple reservoir horizons, including Aghjagil, PS03, and PS06. This study further integrates production analysis with horizon-specific enhanced oil recovery (EOR) recommendations. Polymer flooding is proposed for horizon III to improve sweep efficiency, micellar waterflooding for the Aghjagil horizon, with an estimated recovery increase of 10–20%, and in situ combustion for horizon VI, with potential recovery improvements of up to 20%. Additional analysis of production fluctuations, water breakthrough processes, and reservoir heterogeneity was incorporated to improve the interpretation of abnormal production behavior. The results demonstrate that the proposed approach enhances hydrocarbon recovery efficiency, improves understanding of mature reservoir behavior, and supports data-driven optimization of production systems. The developed framework provides practical implications for long-term field management, reservoir monitoring, and production forecasting in mature oil fields.