Deep learning prediction of waterflooding-based alteration of reservoir hydraulic flow unit

Abstract

The hydraulic flow unit (HFU) is a comprehensive representation of reservoir quality, and the type of reservoir HFU alters dramatically due to the impact of waterflooding. The accurate prediction of HFU characteristic alterations based-waterflooding is crucial but difficult because the alterations are influenced by many factors like geological conditions and production history. In this study, we propose a deep learning-based method using both static geological data and dynamic production history data to predict HFU characteristic alterations waterflooding-based. We classify six HFU types using the k-means clustering of geological variables, including the flow zone indicator (FZI), permeability, and shale volume. The deep-learning model uses the initial static geological variables, including FZI, permeability, and shale volume, and dynamic production data as inputs, whereas the HFU type after waterflooding is used as the target. Convolutional neural networks (CNNs) are adopted for the prediction of HFU characteristic alterations and also compared with support vector machines (SVM) and back propagation neural networks (BPNNs). The prediction accuracy of the CNN model is 93.3% and 87.1% for the validation and testing datasets, respectively, which performs much better than other machine learning methods. In addition, we explore the factors affecting the performance of different models. CNN model can extract useful information from complicated production history data more effectively than other machine learning methods. Finally, the trained CNN model is used to predict the spatial distribution of HFU at different development stages in the study area. In summary, the proposed method can accurately predict dynamic alterations in the reservoir HFU type after waterflooding, which is very useful for the adjustment of oilfield production measures.