A Fluid Identification Method for Buried Hill Reservoirs Based on a BP Neural Network Model Using NMR

Meng WangQuan ZhouLu Yin^*Huizhuo Xie

• China National Offshore Oil Corporation (China), Beijing, Beijing Municipality, China

The geological structure of buried hill reservoirs is highly complex. This study aims to develop a new reservoir fluid identification method for buried hill reservoirs by integrating nuclear magnetic resonance (NMR) logging techniques with a backpropagation (BP) neural network model. NMR logging data were used as input features, including parameters such as T2g (geometric mean of the transverse relaxation time), A (amplitude of the last peak in the T2 spectrum), S3/S1, and S2/S1. A BP neural network model was constructed with a single hidden layer consisting of 8 neurons. The ReLU activation function was employed to accelerate the learning process, and the Softmax function was selected as the output layer activation function to accommodate multi-class classification requirements. Results show that the BP neural network model achieves superior performance in terms of precision, recall, and F1-score across four fluid types: oil zones, oil-water bearing zones, oilbearing water zones, and water zones, outperforming other similar models. In Well HZ26-6-1 located in the ancient buried hill area, the model's predictions are largely consistent with the results from well testing. This study demonstrates that the BP neural network-based approach not only significantly enhances the accuracy of fluid identification in reservoirs but also provides a scientifically sound and effective solution for fluid discrimination under complex geological conditions. Notably, the model exhibits strong classification capability in distinguishing between oilwater bearing zones and oil-bearing water zones, which are typically difficult to differentiate.