The role of Perforation Repair Technology in Extending the Life Cycle of Oil Fields

Abstract

Perforation repair technology plays an important role in prolonging the life cycle of oil fields. However, the traditional perforation repair method can no longer meet the needs of modern oilfield development. With the continuous growth of artificial intelligence technology, the optimization algorithm based on data analysis and model has achieved remarkable success in many fields. In this paper, an optimization algorithm based on artificial neural network (ANN) is proposed to improve the performance of perforation repair and oil recovery. The algorithm can predict the oil field output under different perforation repair schemes according to the information of geological data, production data and historical data of the oil field, and get the optimal perforation repair scheme through optimization. The results show that this algorithm has obvious advantages compared with the traditional support vector machine (SVM) algorithm, and the error is reduced by 24.58%. By comparing the reservoir state prediction results of SVM algorithm, it is found that the prediction results of ANN are closer to the actual value and can better reflect the actual situation. This method provides new ideas and methods for the optimization and improvement of oil field perforation repair technology, and shows broad prospects for the application of machine learning technology in petroleum engineering.

Keywords

• perforation repair
• oil field
• life cycle
• ann.