Jin Tang
Senior Engineer
Research Institute of Petroleum Exploration and Development, PetroChina
Participates in
TECHNICAL PROGRAMME | Energy Leadership
Financing the Future Energy Supply
Forum 27 | Technical Programme Hall 5
28
April
10:00
11:30
UTC+3
As the global energy industry undergoes a profound transformation driven by decarbonization, digitalization, and decentralization, innovative financial mechanisms are urgently needed to bridge capital gaps and derisk investments across diverse geographies and technologies. This paper explores the transformative potential of stablecoins—blockchain-based digital assets pegged to fiat currencies or commodities—as a novel tool for financing the energy transition.
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.
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 Energy Transition: The Role of Digitalisation, AI, and Cybersecurity
Forum 23 | Digital Poster Plaza 4
30
April
10:00
12:00
UTC+3
The demand for intelligent systems capable of real-time decision-making is rapidly increasing as the global energy sector advances toward net-zero emissions. This study introduces an AI-augmented framework for Distributed Fiber-Optic Sensing, designed specifically to monitor the evolution of hydraulic fractures in enhanced geothermal systems (EGS) and unconventional oil and gas reservoirs. By utilizing high-resolution data from Distributed Acoustic Sensing (DAS), Distributed Temperature Sensing (DTS), and Distributed Strain Sensing (DSS), the system integrates advanced machine learning algorithms and digital twin technologies to provide continuous, real-time fracture diagnostics. This approach tracks fracture dynamics from initiation through propagation, offering valuable insights into fracture geometry and behavior over time.
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.
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.
