Oilfield Intelligent Production Optimization and System Simulation Based on Ant Colony Algorithm

There are various forms of underground oil deposits in oil fields, complex surface processes, exploration and development, production and operation, involving many departments and complex processes. Many factors determine that the intelligent oilfield production optimization management system is a complex system engineering. The purpose of this paper is to study the intelligent oilfield production optimization and system simulation based on the ant colony algorithm. The algorithm involved in the neural network optimization model ACO-BP is briefly introduced, and the model building process is described by specific steps of model building. The system simulation environment is Python 3.7.0, and the parameters of the network model are executed. Finally, the results of the experiment are analyzed, and the prediction effect of each model is intuitively shown by the comparison diagram of the model prediction curve. From the experiment results, it can be seen that the ant colony algorithm based system is used to optimize intelligent production management in oilfield production enhancement measures. It has a good effect in effect prediction.