TECHNICAL PROGRAMME | Energy Leadership – Future Pathways
Financing the Future Energy Supply
Forum 27 | Hall 10 - Technical Programme 5
13
October
09:30
11:00
UTC+3
Experts will discuss investment trends, risk management, and the role of public and private sectors in an evolving energy industry amidst a dynamic global transition. The panel will also address challenges in financing the energy transition, policy and market uncertainties, and adaptation to technological advancements. Join us to gain insights into innovative financing models, opportunities for growth, and how to ensure a stable and sustainable energy future.
An applied system dynamics study is presented to explore how virtual barrels, which are financial instruments representing oil exposure, influence commodity pricing. Results compare oil and LNG market behaviors.
For this study, feedback loop models through Minsky, an open-source modeling tool primarily used in economics, were done to determine both stocks (oil reserves) and flows (futures trading) and model complex interactions of oil and gas trading over time. The inclusion of market modeling, using godley tables that enable financial flow, complements this work by employing econometric models, options pricing, and time series analysis to forecast prices and market trends, particularly for commodity trading. This hybrid approach linked physical trading dynamics to speculative financial behavior.
A tale of two markets became apparent in the last 15 years as the ratio of paper barrels to physical barrels traded on the futures market went from roughly one to one to 60:1 today highlights the growing influence of speculation, making the case for virtual barrels. As the world currently consumes more than 100 million b/d of oil, the volume of trades in petroleum futures, options, and over-the-counter derivatives comes up to 6 billion b/d. For LNG markets in 2025, the presence of regional competition between northeast Europe (TTF) and northeast Asia (JKM) showed a ~US$2/BTU premium due to spot-market rerouting. The aggregation of these estimates to machine learning models like XGboost and N-HiTS that captures non-linear patterns led to creating more robust price forecasting in U.S. and China demand, achieving lower weighted Mean Absolute Percentage Error (MAPE) of 3.5% and 10.3% respectively.
Combining system dynamics with existing machine learning uniquely bridges causal feedback and nonlinear patterns, offering a holistic view that connects physical operations and financial strategies in the oil and gas industry. This integration offers a more accurate price forecast, more resilient strategies, and better decision-making that are critical for navigating volatility in interconnected markets.
For this study, feedback loop models through Minsky, an open-source modeling tool primarily used in economics, were done to determine both stocks (oil reserves) and flows (futures trading) and model complex interactions of oil and gas trading over time. The inclusion of market modeling, using godley tables that enable financial flow, complements this work by employing econometric models, options pricing, and time series analysis to forecast prices and market trends, particularly for commodity trading. This hybrid approach linked physical trading dynamics to speculative financial behavior.
A tale of two markets became apparent in the last 15 years as the ratio of paper barrels to physical barrels traded on the futures market went from roughly one to one to 60:1 today highlights the growing influence of speculation, making the case for virtual barrels. As the world currently consumes more than 100 million b/d of oil, the volume of trades in petroleum futures, options, and over-the-counter derivatives comes up to 6 billion b/d. For LNG markets in 2025, the presence of regional competition between northeast Europe (TTF) and northeast Asia (JKM) showed a ~US$2/BTU premium due to spot-market rerouting. The aggregation of these estimates to machine learning models like XGboost and N-HiTS that captures non-linear patterns led to creating more robust price forecasting in U.S. and China demand, achieving lower weighted Mean Absolute Percentage Error (MAPE) of 3.5% and 10.3% respectively.
Combining system dynamics with existing machine learning uniquely bridges causal feedback and nonlinear patterns, offering a holistic view that connects physical operations and financial strategies in the oil and gas industry. This integration offers a more accurate price forecast, more resilient strategies, and better decision-making that are critical for navigating volatility in interconnected markets.
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.
With the development of the global low-carbon economy, clean energy has gradually become an important direction for the transformation of the energy structure. As an efficient and clean energy carrier, hydrogen energy has broad application prospects. However, high capital expenditure, elongated supply chains, and a lack of market maturity are challenges the hydrogen energy industrial chain faces, which lead to complicated transfer pricing problems. This study examines the internal transfer pricing problems in the hydrogen energy industrial chain using X Company as an example. By building a transfer pricing model based on cooperative game theory and integrating it with X Company’s actual data, this study can offer theoretical justification and useful recommendations for the internal transfer pricing mechanism of developing energy sectors. The findings of the study demonstrate that the hydrogen energy industry requires substantial R&D investment, with research outcomes exerting significant long-term impacts on cost reduction. To increase business profitability, transfer pricing for new players in the hydrogen energy market should include a fair distribution of R&D expenses, combined with carefully thought-out transfer price plans and inventory control systems. This research provides novel theoretical perspectives for the transfer pricing in the hydrogen energy industrial chain, and establishes a referential paradigm for designing and optimizing transfer pricing mechanisms in other emerging industries.
Nurgul Akhmetbekova
Vice Chair
Head of Division, Budgeting & Planning Department
KazMunayGas
Kazakhstan
Jin Tang
Speaker
Senior Engineer
Research Institute of Petroleum Exploration and Development, PetroChina
China
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.
Fabian Vera
Speaker
Corporate Reserves Consultant
Mire Petroleum Consultant
United States of America
An applied system dynamics study is presented to explore how virtual barrels, which are financial instruments representing oil exposure, influence commodity pricing. Results compare oil and LNG market behaviors.
For this study, feedback loop models through Minsky, an open-source modeling tool primarily used in economics, were done to determine both stocks (oil reserves) and flows (futures trading) and model complex interactions of oil and gas trading over time. The inclusion of market modeling, using godley tables that enable financial flow, complements this work by employing econometric models, options pricing, and time series analysis to forecast prices and market trends, particularly for commodity trading. This hybrid approach linked physical trading dynamics to speculative financial behavior.
A tale of two markets became apparent in the last 15 years as the ratio of paper barrels to physical barrels traded on the futures market went from roughly one to one to 60:1 today highlights the growing influence of speculation, making the case for virtual barrels. As the world currently consumes more than 100 million b/d of oil, the volume of trades in petroleum futures, options, and over-the-counter derivatives comes up to 6 billion b/d. For LNG markets in 2025, the presence of regional competition between northeast Europe (TTF) and northeast Asia (JKM) showed a ~US$2/BTU premium due to spot-market rerouting. The aggregation of these estimates to machine learning models like XGboost and N-HiTS that captures non-linear patterns led to creating more robust price forecasting in U.S. and China demand, achieving lower weighted Mean Absolute Percentage Error (MAPE) of 3.5% and 10.3% respectively.
Combining system dynamics with existing machine learning uniquely bridges causal feedback and nonlinear patterns, offering a holistic view that connects physical operations and financial strategies in the oil and gas industry. This integration offers a more accurate price forecast, more resilient strategies, and better decision-making that are critical for navigating volatility in interconnected markets.
For this study, feedback loop models through Minsky, an open-source modeling tool primarily used in economics, were done to determine both stocks (oil reserves) and flows (futures trading) and model complex interactions of oil and gas trading over time. The inclusion of market modeling, using godley tables that enable financial flow, complements this work by employing econometric models, options pricing, and time series analysis to forecast prices and market trends, particularly for commodity trading. This hybrid approach linked physical trading dynamics to speculative financial behavior.
A tale of two markets became apparent in the last 15 years as the ratio of paper barrels to physical barrels traded on the futures market went from roughly one to one to 60:1 today highlights the growing influence of speculation, making the case for virtual barrels. As the world currently consumes more than 100 million b/d of oil, the volume of trades in petroleum futures, options, and over-the-counter derivatives comes up to 6 billion b/d. For LNG markets in 2025, the presence of regional competition between northeast Europe (TTF) and northeast Asia (JKM) showed a ~US$2/BTU premium due to spot-market rerouting. The aggregation of these estimates to machine learning models like XGboost and N-HiTS that captures non-linear patterns led to creating more robust price forecasting in U.S. and China demand, achieving lower weighted Mean Absolute Percentage Error (MAPE) of 3.5% and 10.3% respectively.
Combining system dynamics with existing machine learning uniquely bridges causal feedback and nonlinear patterns, offering a holistic view that connects physical operations and financial strategies in the oil and gas industry. This integration offers a more accurate price forecast, more resilient strategies, and better decision-making that are critical for navigating volatility in interconnected markets.
Xiangying Zhang
Speaker
Associate Consultant
Sinopec Economics & Development Research Institute Company Limited
China
With the development of the global low-carbon economy, clean energy has gradually become an important direction for the transformation of the energy structure. As an efficient and clean energy carrier, hydrogen energy has broad application prospects. However, high capital expenditure, elongated supply chains, and a lack of market maturity are challenges the hydrogen energy industrial chain faces, which lead to complicated transfer pricing problems. This study examines the internal transfer pricing problems in the hydrogen energy industrial chain using X Company as an example. By building a transfer pricing model based on cooperative game theory and integrating it with X Company’s actual data, this study can offer theoretical justification and useful recommendations for the internal transfer pricing mechanism of developing energy sectors. The findings of the study demonstrate that the hydrogen energy industry requires substantial R&D investment, with research outcomes exerting significant long-term impacts on cost reduction. To increase business profitability, transfer pricing for new players in the hydrogen energy market should include a fair distribution of R&D expenses, combined with carefully thought-out transfer price plans and inventory control systems. This research provides novel theoretical perspectives for the transfer pricing in the hydrogen energy industrial chain, and establishes a referential paradigm for designing and optimizing transfer pricing mechanisms in other emerging industries.
