Digital Twin–Driven Optimization for Energy-Efficient and Sustainable Oil and Gas Production

Abstract

Declining tendency in production efficiency, deeper and more complex wellbores, and downhole conditions are faced at mature oil and gas fields. Additionally, the conditions are not favorable for direct observation, while large volumes of operational data remain fragmented and under-exploited. In this scenario, Digital Twin (DT) technology, which is a high-fidelity virtual replica dynamically synchronized with its physical counterpart, optimizes the pathway for real-time, data-driven optimization of the entire production chain. Addressing these issues, the research paper presents a DT-driven framework for oil and gas production optimization. The presented framework couples a layered cyber–physical architecture with an intelligent algorithm core. Additionally, Internet-of-Things (IoT) data acquisition, mechanism, and data-driven models, and a closed-loop optimization engine based on nodal (supply–discharge) analysis are the major components of this framework. Moreover, production has been optimized by treating recovery efficiency and output as objectives. The constraints have been equipment and operating limits, along with the design variables being the pumping parameters. The number of reported field deployments on 35 wells is used as a validation case study: the system efficiency rose from 11.02% to 16.43%, the power factor improved from 0.55 to 0.66, and the average daily power consumption and the lifting energy per unit liquid both fell substantially. Sensitivity analyses and recovery projections further put forward how DT-based control sustains higher production and recovery. As per the results, DT-driven optimization is an effective enabler of efficient, intelligent oil and gas production.