In recent years,with the rapid development of Distributed Fiber Optic Temperature Sensing(DTS)technology,it has the advantages of low interference to well,convenient construction,and can provide accurate and continuous temperature data in real time.It is more and more applied in downhole dynamic monitoring.However,the application of DTS data is large and complex,and how to analyze the data and use it to interpret downhole flow profiles and production dynamics remains a major challenge.Therefore,based on the heat transfer mechanism between wellbore and formation,a multi-phase flow wellbore temperature distribution model is established in this paper,and the characteristics of wellbore temperature curve response under different influencing factors are analyzed.The measured DTS data was analyzed,the fitting evaluation objective function based on the wellbore temperature distribution model was established.L-M(Levenberg-Marquarelt algorithm)and Sqp-legacy(Sequential quadratic programming-legacy)were used.After inversion of the two algorithms,the Particle Swarm Optimization(PSO)algorithm is combined with the two algorithms,and the PSO-LM algorithm is selected after comparing the inversion errors of the two combined algorithms.PSO-LM algorithm has both the randomness of PSO algorithm and the efficiency of L-M algorithm.The error results show that the accuracy of PSO-LM algorithm is high,which meets the practical application,and verifies the correctness and reliability of the inversion interpretation model.
关键词
注入-产出剖面解释/分布式光纤温度传感(DTS)技术/多相流井筒温度分布模型/PSO-LM反演算法
Key words
Injection-output profile interpretation/Distributed Fiber Optic Temperature Sensing(DTS)technology/Multi-phase flow wellbore temperature distribution model/PSO-LM inversion algorithm
维普
万方数据