Oilfield development involves a large amount of data integration, analysis, and processing, and faces severe challenges. Traditional methods cannot integrate multiple sources of data to accurately analyze the production dynamics of oil reservoirs. To address the main production problems encountered in the development process, such as dynamic analysis, automatic historical fitting, development plan optimization, and selection of measures to improve oil recovery, the research team proposed an oilfield development technology based on machine learning and intelligent optimization theory. Its core content includes building neural networks with physical meaning to replace computationally expensive numerical simulations, training reinforcement learning models to develop optimal strategies and plans, using transfer learning techniques to transfer knowledge to accelerate the optimization of new models, and constructing proxy models and combining them with intelligent algorithms to solve complex problems quickly. By comprehensively applying the proposed intelligent oilfield development method, it is possible to reduce dependence on complex physical simulations and simulate and analyze complex reservoir systems with simpler operations and faster speeds.