Intelligent method for predicting formation pore pressure in No. 5 fault zone in Shunbei oilfield based on BP and LSTM neural network

Faults are developed in the Shunbei Oilfield with complex geological structures, and the reservoirs are buried as deep as 8 000 m, which are characterized by high temperature, high pressure, and narrow drilling fluid density window, and the prediction accuracy of formation pore pressure cannot meet the engineering requirements. In order to improve the prediction accuracy of formation pore pressure, by taking advantage of artificial intelligence methods in dealing with complex nonlinear problems, two artificial intelligence algorithms, namely back-propagation neural network BP and long-short-term memory cycle neural network LSTM, were adopted. Based on the data (11 kinds of characteristic data such as acoustic time difference, spontaneous potential and natural gamma, and the formation pore pressure label data corrected by actual measurement) from 3 wells in the No. 5 fault zone in Shunbei Oilfield, an intelligent model for predicting the formation pore pressure in the No. 5 fault zone in Shunbei Oilfield was established. The prediction error of BP neural network model is 3.927%, and the prediction error of LSTM neural network model is 2.864%. The testing results show that the LSTM neural network model has a better prediction effect, and meets the prediction accuracy of the formation pore pressure on site, which can provide a data reference for ensuring the drilling safety in the No. 5 fault zone .