Combine the AGA and L-M algorithms to predict the oil-water flow profile of production wells

Abstract

Production logging is the main means of dynamic monitoring of oil wells at present. The interpretation method of production logging fluid profiles is the key to the dynamic evaluation of oil wells and is of great significance. A joint optimization technique of a AGA and L-M is proposed to deal with the profile data of oil-water two-phase flow. The method is based on the energy conservation of the fluid in the production unit space. According to the nonlinear weighted least squares principle and the error theory, the objective function of the minimum value is established by the difference between the theoretical fluid temperature and the measured value of the thermometer. According to the fluid volume conversion, the flow rate of each phase in the well and the flow log value at the bottom of the control body are constrained. In order to improve the interpretation accuracy, the global search method (AGA) of genetic algorithm and the direct local search method of L-M algorithm are combined to solve the optimal solution. Firstly, the adaptive genetic algorithm is used for heuristic global search, and then as a result of the initial parameters, the L-M method is applied to approach the optimal solution. The interpretation results from the joint inversion are stable and reliable. This method is not only suitable for vertical wells but also for inclined wells.