Jin Tang
Senior Engineer
Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation
China
Dr. Jin Tang is a Senior Engineer at the Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation (CNPC). His research spans energy engineering, applied artificial intelligence, and blockchain technology. He holds a Ph.D. in Petroleum Engineering from Texas A&M University.
His engineering research focuses on distributed fiber-optic sensing, large-scale hydraulic fracturing experiments, and AI-enabled fracture diagnostics. From laboratory-scale experiments to field applications, his work advances real-time monitoring and characterization of hydraulic fracture propagation and supports the development of intelligent fracturing systems that integrate fiber-optic sensing and AI for closed-loop treatment optimization. He has contributed to multiple National Science and Technology Major Projects and CNPC R&D projects. His research output includes more than 20 technical papers and over 30 patents. He is also an active contributor to ISO/TC 67/SC 3 and has participated in the development and revision of multiple international standards.
Beyond energy engineering, his research focuses on next-generation blockchain-based financial infrastructure for the energy sector, encompassing crypto assets and real-world asset (RWA) tokenization, with particular emphasis on stablecoins. His research interests include stablecoin-enabled cross-border settlement, programmable financing, and more efficient capital deployment across the global energy value chain. He also explores the broader role of stablecoins in connecting energy assets with digital capital markets and supporting national strategic priorities in energy security, international cooperation, and financial innovation.
Participates in
TECHNICAL PROGRAMME | Energy Leadership
Stablecoins offer programmable, borderless, and near-instant financial settlement, making them particularly well-suited for capital-intensive and multinational energy operations. We propose a stablecoin-enabled framework that integrates decentralized finance (DeFi) protocols, smart contracts, and tokenized assets to facilitate transparent, real-time transactions across the energy value chain. From cross-border project financing and vendor payments to carbon credit settlement and ESG-linked bond disbursement, stablecoins have the capacity to significantly reduce friction, cost, and time in financial flows.
This paper evaluates technical architectures for stablecoin implementation in upstream and midstream oil and gas projects, analyzes key risks—including regulatory, cybersecurity, and liquidity—and presents pilot case studies involving smart contract–driven payments in renewable infrastructure and hybrid energy systems. In addition, the role of private consortia and public sector alignment in fostering digital currency adoption within energy finance is discussed, alongside implications for central bank digital currencies (CBDCs) and sovereign-backed stablecoins.
The analysis demonstrates that stablecoins can serve as a digital bridge between traditional capital markets and next-generation energy infrastructure, supporting new financing models such as tokenized royalties, performance-based payment triggers, and fractional project ownership. By mitigating currency volatility and improving fund traceability, stablecoins can enhance financial inclusivity for emerging market energy projects while accelerating the global transition to sustainable energy.
In conclusion, the paper highlights stablecoins not merely as a fintech curiosity, but as a strategic enabler of resilient, transparent, and scalable energy financing. Recommendations are provided for energy companies, regulators, and institutional investors to responsibly integrate stablecoin infrastructure into their capital deployment strategies, helping secure a stable and sustainable energy future in a digitally connected world.
TECHNICAL PROGRAMME | Energy Technologies
The proposed system enhances operational efficiency by enabling precise fracture monitoring, optimizing energy extraction, and minimizing operational risks. Integrating AI-powered predictive analytics facilitates early detection of potential issues, allowing for proactive maintenance and reduced downtime. Additionally, the system offers environmental benefits by optimizing hydraulic fracturing processes, minimizing water and energy consumption, and reducing the ecological footprint of energy extraction.
Supported by large-scale physical modeling and field validation in high-pressure, high-temperature environments, the framework ensures its applicability in real-world conditions. Key innovations include an adaptive signal interpretation engine for accurate fracture characterization and an AI-driven framework that continually adapts based on real-time data inputs. A resilient cybersecurity architecture further ensures the integrity of the data across the monitoring infrastructure, protecting sensitive information throughout energy operations.
This AI-enhanced approach represents a transformative leap in real-time fracture monitoring, providing unprecedented insights into subsurface dynamics. By combining AI with state-of-the-art fiber-optic sensing technologies, the system responds dynamically to complex subsurface conditions. This integrated solution optimizes operational performance, strengthens safety protocols, and improves the overall efficiency of energy production systems.
The framework’s real-time monitoring of hydraulic fractures is essential for optimizing energy production efficiency. Its predictive capabilities enable proactive maintenance and operational adjustments, ensuring continuous optimization. Additionally, the system contributes to environmental sustainability by helping energy producers reduce waste and mitigate ecological risks typically associated with energy extraction.
Aligned with the broader goals of the energy transition, this study demonstrates how the convergence of AI, fiber-optic sensing, and real-time monitoring technologies can unlock new levels of sustainability, efficiency, and safety in energy systems. By providing a holistic solution that optimizes fracture characterization, mitigates risks, and enhances energy production efficiency, this approach represents a significant step forward in the development of sustainable and resilient energy systems for the future.
