Formation pressure prediction method based on machine learning

Abstract Formation pore pressure is one of the key parameters in petroleum engineering. The high-temperature and high-pressure working conditions formed under the complex mechanism of Yingqiong Basin make downhole complex events such as overflow and well leakage occur frequently. To address the problem of the large prediction error of the traditional Eaton method under high temperature and high-pressure conditions, three machine learning methods, such as GA-BP(Genetic Algorithm-Back Propagation Neural Network), PSO-LSTM(Particle Swarm Optimization-Long Short-Term Memory Neural Network), SVR(Support Vector Regression), are introduced to find the nonlinear relationship between logging parameters and formation pore pressure. Seven logging parameters such as formation depth, mechanical drilling speed, drilling pressure, and rotational speed were used as input variables, and formation pore pressure was used as an output variable through correlation analysis for constructing the machine learning model. By analyzing the experimental results of the above three machine learning models with the Eaton method, the GA-BP model with lower error and more satisfying the needs of drilling engineering is preferred, and the various regression evaluation indexes of the model are also more excellent compared with other machine learning models, which are Mean Squared Error (MSE)=0.001, Mean Absolute Error (MAE)=0.003, and R-squared (R2)=0.991, respectively. It is concluded that the machine learning method is not only more advantageous in a class of nonlinear regression problems such as formation pore pressure prediction, but also can help engineers to predict formation pore pressure more accurately under the conditions of deep high-temperature, high-pressure, and complex formations, which contributes to guaranteeing the safety and high efficiency of drilling projects.