The rapid growth of the “Belt and Road” initiative has significantly boosted international oil and gas cooperation in resource-rich regions such as the Middle East, Central Asia, and Africa. However, these international projects often encounter challenges like complex geological environments, remote operation and maintenance needs, and varying technical standards. In this context, accelerating digital and intelligent transformation is crucial to help oilfields reduce costs, improve efficiency, and develop innovative industrial models. Traditional development methods struggle to handle the multi-source, high-dimensional data produced in modern oilfields. To promote the smart development of global oilfields, it is essential to build a digital-intelligent integrated cloud platform that combines data-driven decision-making, digital twin technology, and cross-disciplinary expertise.

We created a multi-module integrated cloud platform utilizing the interdisciplinary resources of the China–Saudi Petroleum Energy Belt and Road Joint Laboratory. The platform’s architecture is based on a cloud-native framework, comprising a data layer, an AI algorithm layer, and a control service layer. It incorporates back-end microservices, an Oracle database for reliable data management, and a progressive front-end framework for interactive visualization. The core technology consists of four specialized modules that perform intelligent analysis of artificial-lift operating conditions, optimize artificial-lift design, assess oilfield production performance, and run virtual simulations. By integrating these modules, the platform forms a seamless intelligent workflow from real-time condition diagnosis to production optimization and comprehensive performance evaluation.

The platform has been successfully implemented in several major international projects, most notably within the Ahdab Oilfield in Iraq. Using the first module, the platform demonstrated its ability to analyze block-wide conditions and identify specific issues, such as gas lock and supply shortages, with high accuracy. In the Ahdab field, which faces challenges like high water cut and production stability issues, the platform completed 156 well-cycle control measures. These optimizations led to a total oil increase of 110700 tons, effectively achieving objectives related to boosting oil production, controlling water production, and reducing operational costs. Additionally, the evaluation module proved effective in identifying low-performing wells by ranking them based on standardized multidimensional scores, enabling engineers to implement targeted geological and technical interventions. Moreover, the platform has served as a key tool for international collaboration, supporting 27 national-level research projects and training over 1000 petroleum professionals from partner countries such as Uganda.

This study established a robust technological foundation for smart oilfield development. By integrating big data analytics, AI, and cloud computing, the platform bridges the gap between theoretical oilfield development and practical engineering applications. The successful deployment at the Ahdab Oilfield offers a replicable and scalable model for oilfields in Belt and Road partner countries. It enhances production management and decision-making efficiency and promotes international scientific cooperation. Future updates will aim to expand the platform’s application across the entire oil and gas value chain and further explore the use of digital twins in global energy transformation.

Hydraulic fracturing technology is pivotal in the exploration and development of oil and gas fields. Among various methods, electromagnetic monitoring stands out for its nondestructive nature and high accuracy, proving to be an effective tool for evaluating hydraulic fracturing. This paper aims to advance the field by enhancing traditional fracture monitoring devices and creating an experimental teaching platform for online monitoring of hydraulic fractures using electromagnetic methods.

The proposed teaching platform integrates both numerical simulations and laboratory experiments. Initially, COMSOL software was used to conduct numerical simulations on the electromagnetic crack monitoring process, and then the laboratory experiment scheme was optimized based on the numerical simulation results. Subsequently, the teacher guided the students to conduct the laboratory fracture model monitoring experiment, and compared their results with those from the numerical simulations. This dual approach, combining virtual simulation experiments with hands-on laboratory work, allows for a deep dive into electromagnetic fracture monitoring technology. It opens avenues to explore the relationship between electromagnetic monitoring signals and fracture parameters more thoroughly. In the simulation phase, COMSOL was used to model the three-dimensional formation, wellbore, fracture, and electromagnetic detection tool. The mathematical model and boundary conditions of finite-element simulations were established based on Maxwell’s equations, and each model is meshed according to its interconnectivity. After establishing the simulation model, experiments were carried out to monitor the electromagnetic response signals of different fracture models. Numerical simulation exercises not only address students’ uncertainties and complexities in the subject matter but also enhance their understanding and feedback on the experiment. The laboratory experiments, designed to mirror field fracturing processes, feature a visual fracture simulation system and a three-dimensional electromagnetic induction probe. Through this setup, students directly observe the fracture filling and monitoring process, collecting electromagnetic response signals with the probe. Laboratory experiments can exercise students’ practical skills. Comparing these experimental results with numerical simulation results improves the comprehensive research ability of students.

The comparative analysis of electromagnetic fracture monitoring results from both numeral simulations and laboratory experiments reveals 1) a positive correlation between the signal amplitude fluctuations and the total volume of fracture; 2) the ability to infer the fracture location based on the signal fluctuation range; and 3) the similarity between numerical simulation results and experimental data, affirming the accuracy of the experiment.

The teaching platform for electromagnetic monitoring of hydraulic fractures combines virtual simulation experiments with indoor electromagnetic signal monitoring to deepen the understanding of electromagnetic fracture monitoring technology. Through a series of educational modules, including COMSOL virtual fracture simulations, monitoring of fracture filling processes, laboratory fracture monitoring, and comparative analysis of monitoring signals, students explore the relationship between fracture parameters and electromagnetic monitoring signals. This comprehensive approach not only lays a theoretical foundation for the effective inversion of fracture parameters but also enhances student’s grasp of relevant theoretical concepts, fostering their research and engineering practice skills. By engaging with this cutting-edge academic and applied experimental project, students are encouraged to develop their independent innovation and practical inquiry abilities. The platform promotes the advancement of hydraulic fracture monitoring technology. In addition, the teaching platform has been upgraded by adopting constructive and innovative ideas, positioning itself at the forefront of innovative research in the field.