Intelligent Modeling of Energy Production-Consumption Cycle Management in the Oil Industry Using a Hybrid Deep Learning and Circular Economy Approach

The oil industry, as one of the largest energy producers and consumers globally, plays a pivotal role in global energy balance. Despite its resource richness, energy waste across production, refining, transportation, and internal consumption remains a critical challenge to sustainable development and energy security. This research presents an innovative intelligent model for managing the energy production-consumption cycle in the oil industry by integrating operational oil industry data, deep learning algorithms (LSTM and Transformer), and circular economy principles to predict, optimize, and reallocate energy resources system-wide. Data were collected from five major Iranian oil complexes including upstream fields, refineries, and energy intensive units over the period ۲۰۱۸–۲۰۲۳. After preprocessing, the dataset was used to train and validate the proposed hybrid model. Results demonstrate that the model achieves ۹۴.۷% prediction accuracy for energy consumption and enables up to ۲۳% reduction in energy use and ۱۸% reduction in greenhouse gas emissions through optimization scenarios. Furthermore, by applying circular economy concepts such as waste heat recovery, cogeneration, and internal energy recycling the model recovers ۱۲% of consumed energy from within-system sources. This study is the first to comprehensively integrate Artificial Intelligence, Energy Management, and Circular Economy in the oil industry, offering a scalable and generalizable framework for other energy-intensive sectors.

Keywords:

Energy Management , Oil Industry , Deep Learning , LSTM , Transformer , Circular Economy , Energy Optimization , AI in Oil & Gas , Waste Heat Recovery