Pressure Relief-Type Overpressure Prediction in Sand Body Based on BP Neural Network
Yanfang Gao, Yanchao Li, Hongyan Yu, Shijie Shen, Zupeng Chen, Dengke Li, Xuelin Liang, Zhi Huang · Processes · 2025
With the gradual depletion of global oil and gas resources, accurate prediction of anomaly formation pressure caused by pressure relief from other sources has become increasingly crucial in oil and gas exploration and development. The anomaly formation pressure caused by pressure relief affects the well’s stability and significantly impacts the safety and economy of drilling operations. However, traditional methods for predicting anomaly formation pressure, such as Bowers’ method, may not accurately identify the complex relationship between parameters and pore pressure. In contrast, the BP neural network (BPNN) can learn the complex relationship between input and output from data, which has a significant advantage in accurately identifying anomaly formation pressures caused by pressure relief from other sources. This study proposes a neural network-based method for accurately predicting anomaly formation pressure caused by pressure relief from other sources. The high quality of input data is ensured through meticulous preprocessing related to anomaly formation pressure caused by pressure relief from other sources, including data cleaning, standardization, and correlation analysis. Subsequently, model training was conducted to fully utilize its powerful nonlinear fitting ability and capture the complex changes in formation pressure caused by anomaly pressure relief from other sources. This method collects and organizes the parameters of the formation, including Gamma-ray (Gr), Delta-T (Dt), wave velocity (Vp), and Resistivity (R10), to train a BPNN model for predicting pressure relief type anomaly formations. The trained model has a Bayesian regularized backpropagation function, and the average absolute percentage error (AAPE) and correlation coefficient (R) of predicting pore pressure in well A are 4.22% and 0.875, respectively. To verify the proposed model’s effectiveness, it was applied to a blind dataset of adjacent B wells and successfully predicted pore pressure with AAPE of 5.44% and R of 0.864. We compare and analyze the formation pore pressure predicted by the traditional Bowers model and support vector machine (SVM) model. The prediction results of the BPNN model have more minor errors and are closer to the actual pressure coefficient. This study demonstrates the accuracy of the proposed model in predicting pressure relief type anomaly formation pressure using drilling data.