Design and implementation of an integrated cloud platform for intelligent and data-driven oilfield development

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.