Deepwater gas field development faces complex challenges such as ultra-deep water, strong multi-field coupling, and high operational risks, including hull stability risks, difficulties in reservoir characterization, limited accessibility of monitoring data, and the complexity of integrated production management. Traditional approaches often rely on fragmented single-module simulations and manual decision-making, resulting in delayed model updates, isolated information, and the inability to achieve end-to-end collaborative optimization across reservoirs, pipeline networks, and platforms. Digital-twin technology overcomes these limitations by breaking down data silos, enhancing model coupling, and reducing decision latency. It enables real-time interaction, closed-loop control, and full-chain integrated management, thereby eliminating information barriers among reservoirs, wellbores, pipelines, and platforms and providing coordinated, efficient production control and safety assurance for deepwater gas fields. Focusing on the “Deep Sea No. 1” production platform, this study explores the construction of a production digital-twin system that spans the entire business chain of reservoir–wellbore–pipeline network–platform–operation–finance. First, the research progress of digital twins in oil and gas production is systematically reviewed, including typical modeling methods, technical frameworks, and engineering practices. Secondly, a modular hybrid modeling approach integrating physical mechanism models and data-driven models is proposed, establishing a complete modeling workflow comprising system decomposition, model construction, data integration, optimization solving, and feedback control. Third, based on the actual application scenario of the “Deep Sea No. 1” platform, digital-twin modules are developed for mooring and hull management, flow assurance management, intelligent reservoir management, and 3D visualization, enabling early warning, predictive maintenance, decision support, immersive visualization, and full-chain closed-loop control. Field application results demonstrate that the system significantly improves the automation and intelligence of the platform, reducing production allocation calculation time from 4–5 days to less than 1 hour with prediction accuracy exceeding 90%. Finally, in response to current issues such as limited model transferability and heavy manual intervention, this paper suggests establishing a linkage framework of large and small models, strengthening integration with subsea control systems, and building a full-lifecycle digital-twin system. The research results provide a feasible technical pathway and engineering reference for the intelligent and efficient development of deepwater gas fields.

The oil and gas production volume, import amount, and degree of dependence on foreign markets keep increasing annually. Meanwhile, the transaction volume of hydrocarbon futures continues to grow with time. In this regard, using intelligent technology to enhance the efficiency of oil and gas transactions, as well as to improve the trading mechanisms, has gradually become a critical consideration for the oil and gas industry. Therefore, this article first discusses the concept of intelligent finance and illustrates the history of its development. It then investigates the applications of such a composite technology that integrates intelligent techniques and financial businesses in the oil and gas industry. The investigation primarily focuses on the current status of implementation and the developmental trend of four major business fields: the capitalization of petroleum exploration and development, risk management of capital operation, insurance and reinsurance, and transaction of financial products in the oil and gas market. The intelligent techniques under consideration include cloud computing, blockchains, big data, and artificial intelligence. Moreover, the progress of intelligent technology in carbon finance is also analyzed for the oil and gas industry. Last but not least, some primary challenges that would restrain the implementation of intelligent finance technology in the industry are closely examined. The research outcome of this study reveals four discoveries. First of all, the applications of intelligent techniques have been more widely seen in the geology or engineering practices throughout the upstream, middle stream, and downstream stages. In contrast, they are sporadically used for the aforementioned scenes of financial businesses. Secondly, intelligent finance technology has demonstrated enormous value in market capitalization, hydrocarbon reserve evaluation, transaction safety, market analysis, risk control, claim settlement, and supply chain finance in the oil and gas industry. Thirdly, intelligent finance technology can efficiently boost the development of the carbon product market and the carbon derivative market. It can also continuously optimize transaction policies of the industry. Finally, such applications face several challenges, including a low degree of digitalization of the industrial businesses, a low efficiency of data management and administration, immature economic policies and market regulations, a less competitive transaction market, and an unattractive salary package for potential talents. In these respects, it is suggested to strengthen the digital transformation of the oil and gas industry, push the data governance across the related industries, refine the legislative regulation and supervision of intelligent finance practices, and raise the remunerative treatment of the professionals on this field.

The uncertainty of geological composition, the invisibility of the under-well real-time working conditions, and the complexity of the engineering simulation in the oil and gas field drilling and production process have hindered its scientific and efficient design and construction. The digital twin technology can bring up real-time, intelligent, and visualized project design and decision-making but has yet to lack a systematic method for modeling oil and gas field drilling and production. In this regard, the article first explored the current levels of investigation and implementation both domestically and abroad, based on that the level of development by applying the maturity index was quantified. It then proposed the digital twin modeling approach for drilling and production in the oil and gas field, which encompassed the modeling workflow, model division strategies, architecture for model assembly and integration, and modeling tools for constructing the digital twin. Also, two case were studied for drilling and production, using wellbore stability while drilling and offshore gas well production system as two examples, respectively. Finally, the difficulties and challenges related to the digital twin deployment in the field were analyzed, based on which the suggestions for its future development are proposed. It is found that the digital twin for drilling and production has stayed at the visualization level and at a relatively low degree of maturity compared to the manufacturing field on digital twin. The complex demand for oil and gas drilling and production systems can be divided into several clear and easy realized sub-demands. Based on requirement analysis, the modeled object can be separated to be various sub-models based on the granularity, dimension, and lifecycle. The sub-models are then assembled layer by layer across the model, function, and demand layers so that the multi-dimension and multi-field models can be integrated. Meanwhile, an improvement of their methods and an increase in efficiency for the model administration, data management, and engineering simulation ae desired. Moreover, the digital twin faces the problems such as difficulty in selection and fusion of multi-source heterogeneous data, vagueness in the sub-model definition, and ambiguity in the model validation, as well as the challenges such as the complicated kinetics processes, multi-division and multi-task collaboration, and development of domestic software tools. In summary, the digital twin modeling approach and the case studies in this article can provide a methodological guidance and practical reference for oil and gas drilling and production practices.