亚闭环油气网络系统整体运行优化及其智能化研究

油田生产运行优化实质是受约束超大型流体网络系统极值优化问题，单一生产系统运行优化是原问题的松弛特例，本项目拟将油藏、采油、集输、污液处理、注入系统关联耦合，提出并建立油田亚闭环油气网络系统的概念和理论方法，基于分数阶微积分、贝叶斯估计和所提出的改进MCMC方法，建立非线性渗流等复杂工况下多尺度流动参数计算方程改进方法；以联结各生产系统的液流参数为决策变量，结合所构建的亚闭环系统空间有向图广义表征模型和大系统优化理论，创建多目标、多约束的油田亚闭环系统整体运行优化模型和优化子模型；基于所提出的大系统智能协调求解机制，构建测度自适应新型粒子群算法、地下注入有效模糊区和机采系统模糊相似类降维求解方法，继而形成超高维优化模型的混合智能递阶协调优化求解策略。研究成果可有效填补油田全局层面运行优化理论方法的缺失，对于促进油田集约可持续发展、实现衰减期油田节能降耗开采及油气智能生产意义重大。

The optimization of oilfield production and operation is essentially a problem of extreme value optimization for constrained super-large fluid network systems, and the operation optimization of a single production system is a relaxation special case of the original problem. In this project, correlating and coupling oil reservoir, oil production, gathering and transportation, sewage treatment, and injection systems, the concept and theoretical method of oilfield sub-closed-loop oil and gas network system are proposed and established. Based on the fractional order calculus, Bayesian estimation and the proposed improved MCMC method, an improved method for calculating multi-scale flow parameters under complex operating conditions such as nonlinear seepage is established. Taking the fluid flow parameters of each production system as decision variables, combined with the generalized representation model of the sub-closed-loop system spatial digraph and the large-scale system optimization theory, the overall operation optimization model and the optimization sub model of the oil field sub closed-loop system with multi-objective and multi constraint are established. Based on the proposed large-scale system intelligent coordination solution mechanism, a new particle swarm optimization algorithm with self-adaptive measure, an effective fuzzy region for underground injection and a dimension reduction method for fuzzy similar classes of mechanical mining system are constructed, and then a hybrid intelligent hierarchical coordination optimization solution strategy for ultra-high dimensional optimization model is formed. The research results can effectively fill the lack of operation optimization theory and method at the overall level of the oil field, which is of great significance for promoting the development of interdisciplinary frontier theory, realizing the energy-saving and efficiency-increasing production in the declining period and obtaining the intelligent oil field production decision-making.