Abstract: The present disclosure relates to a parallel proxy model based machine learning method for oil reservoir production. With the proposed method, multiple optimized candidate solutions can be obtained within an iteration, and then a matrix laboratory (e.g., MATLAB) is used to call numerical simulation software Eclipse in parallel to conduct actual evaluation on the candidate solutions simultaneously, so that optimization time of complex problems can be greatly reduced. With the method of the present disclosure, the efficiency of solving an oilfield production optimization problem can be speeded up to a greater extent than in the art, and the final optimization effect can be improved. Moreover, the method of the present disclosure may further be used for well pattern optimization, history matching, and so on, apart from adjusting schedules of the producers and injectors in the oilfield.