Separate-Layer Injection Scheme Optimization Based on Integrated Injection Information With Artificial Neural Network and Residual Network

Abstract

Separate-layer injection technology is a highly significant approach for enhancing oil recovery in the later stages of oilfield production. Both separate-layer and general injection information are crucial parameters in multi-layer oilfield injection systems. However, the significance of general injection information is usually overlooked during the optimization process of separate-layer injection. Moreover, conventional optimization schemes for separate-layer injection fail to meet the immediate and dynamic demands of well production. Consequently, a separate-layer injection optimization method based on artificial neural network and residual network (ANN-Res) model was proposed. Firstly, the primary controlling factors for production were identified through grey correlation analysis and ablation experiments. Then, a data-driven model was established with an artificial neural network (ANN), in which the residual block was utilized to incorporate general injection information, eventually forming an ANN-Res model that integrates separate-layer and general injection information. Finally, a workflow for separate-layer injection optimization was designed in association with the ANN-Res model. Analysis of primary controlling factor for production shows that the combination of separate-layer and general injection information for production prediction leads to redundancy. The results of injection–production prediction demonstrate that the ANN-Res model is significantly better than that of the ANN model which only inputs separate-layer or general injection information. Furthermore, the result of optimization proves the proposed method can be successfully applied to injection optimization, realizing the purpose of increasing oil production and decreasing water cuts, thereby improving oilfield development.