Chemical agents designed for oilfield development: A new paradigm empowered by artificial intelligence
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Abstract
With the ongoing rise in global energy demand, the importance of enhanced oil recovery in oilfield development is becoming increasingly prominent. However, traditional chemical flooding agents face bottlenecks such as poor adaptability to application environments, unclear coupling mechanisms regarding multiple factors, as well as long research and development cycles. This paper systematically discusses the innovative paradigm of oilfield chemical agent development driven by artificial intelligence and proposes four core technological breakthroughs. Firstly, artificial intelligence-empowered molecular simulation technology can reveal the behavior mechanisms of flooding agents under extreme conditions. Secondly, intelligent molecular design using generative adversarial networks and reinforcement learning breaks through the traditional trial-and-error model. Thirdly, the construction of a data-mechanism dual-driven multi-objective optimization model achieves the collaborative prediction of physicochemical properties, economic benefits and environ mental friendliness. Lastly, an integrated system of robotic chemist and high-throughput experimental platforms forms a closed-loop system of “artificial intelligence design - automated synthesis- online detection”, yielding a complete ecosystem. The analysis of the current technological development challenges and future development directions reveals that the artificial intelligence-empowered intelligent Research and Development system is expected to promote the transformation of chemical flooding technology toward efficiency, environmental protection and sustainable development, providing a new standard for intelligent oil and gas field development.
Document Type: Invited review
Cited as: Wei, K., Zhou, M., Huang, J., Zhang, Q., Ding, B., Lyu, W., Peng, M. Chemical agents designed for oilfield development: A new paradigm empowered by artificial intelligence. Advances in Geo-Energy Research, 2025, 17(1): 1-16. https://doi.org/10.46690/ager.2025.07.01
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DOI: https://doi.org/10.46690/ager.2025.07.01
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