随着油气开发难度增加和资源接替不足，传统的油气藏开发面临诸多挑战，亟须引入智能化分析手段以提高开发效益。研究聚焦常规油气藏及页岩气藏效益开发的需求和应用场景，创新性地提出了基于多模型的油气开发智能技术，实现了油藏经营效益决策、异常态势全面感知和智能均衡注采优化，有效促进了油藏资源开采的智能化，为多层复杂水驱油藏均衡注采、效益开发提供了技术支撑；构建了页岩气藏压力预测与产能因素分析技术，建立气藏异常预警机制，推送异常因素及产生原因，实现气藏由事后分析到提前预警、事前找人的转变，支撑气藏的效益开发；攻关建立了油井多模态自诊断与评价技术，实现抽油机井工况智能诊断、电泵井况自诊断与智能评价技术和油井动液面实时计算，辅助措施制定，实现对油井的精细化管理，注采调整更加及时精准，有效提高了油井生产时率。通过技术的综合应用，支持油气藏动态管控过程中的“全面感知、集成协同、预警、分析优化”新业务模式构建。此研究技术已在中国石化上游企业广泛推广，实际应用围绕多模型油气开发技术展开，为当前油气藏效益开发中的关键问题提供新的思路和技术路径，推动油气领域的数智化转型，促进了油气田的高效开发和高质量发展。

With the increasing difficulty in oil and gas development and insufficient replacement of resources, traditional development of oil and gas reservoirs faces multiple challenges, requiring intelligent analysis solutions for enhanced development efficiency. This study focused on the demand and application scenarios for efficient development in conventional oil and gas reservoirs and shale gas reservoirs and proposed an innovative intelligent technology for oil and gas development based on multi-model approaches. It enabled decision-making of production and efficiency allocation, comprehensive abnormal situation awareness, and intelligent balanced injection-production optimization. This effectively promoted the intelligent exploitation of reservoir resources, providing technical support for balanced injection-production and efficient development in multilayered complex waterflood reservoirs. A pressure prediction and capacity factor analysis technology for shale gas reservoirs was developed, along with an abnormality warning mechanism to push alerts about abnormal factors and their root causes. This achieved a transition from post-event analysis to early warning and pre-emptive intervention, thereby supporting the efficient development of gas reservoirs. Breakthroughs were made in establishing a multi-modal self-diagnosis and evaluation technology for oil wells, achieving intelligent diagnosis of pumping well operating conditions, self-diagnosis and intelligent evaluation of electric pumping well conditions, and real-time calculation of dynamic fluid levels in oil wells. These supported measure formulation, enabled refined management of oil wells, and made injection-production adjustments more timely and accurate, effectively improving the production time ratio of oil wells. The integrated technology application supported developing a new operational model featuring “comprehensive awareness, integrated coordination, early warning, and analysis and optimization” for the dynamic management and control of oil and gas reservoirs. These research technologies have been widely promoted among upstream companies of Sinopec, with practical application focusing on multi-model oil and gas development technologies. This study offers new ideas and technical approaches to address key challenges in the efficient development of oil and gas reservoirs, driving the digital and intelligent transformation of the oil and gas sector and facilitating the efficient and high-quality development of oil and gas fields.