Multistep-Ahead Prediction of Logging-While-Drilling Resistivity Curves Based on Seismic-Guided Seq2Seq-Long Short-Term Memory

Abstract

Summary High-temperature and high-pressure reservoirs in complex geological conditions present primary targets and significant challenges in deepsea oil and gas exploration. Limited offshore drilling operations and lack of detailed geological data hinder accurate formation pressure prediction using geoguided and offset imaging, increasing drilling hazards. Logging-while-drilling (LWD) technology provides timely and accurate subsurface information. Resistivity closely correlates with formation pressure and lithology, aiding pressure prediction. Therefore, in this study, we developed an ahead prediction workflow for LWD curves using the resistivity curve (RD) as an example. A seismic-guided sequence-to-sequence framework with the long short-term memory model (Seq2Seq-LSTM) is used to predict the RD curve at a constant depth ahead of the drill bit, utilizing the RD curve of drilled sections and seismic attributes. The network structure incorporates a direct-recursive hybrid multistep prediction strategy based on update threshold control (Dir-Rec-Update), aligning with real-time LWD data acquisition for ahead curve prediction. Using real well data cross-prediction, baseline models such as multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) were compared while also investigating the impact of different configurations on the proposed Seq2Seq-LSTM. The results demonstrate that the method outperforms conventional models, with an average performance across multiple wells under a 5-m update threshold: root mean square error (RMSE) of 0.15, correlation coefficient of 0.88, and coefficient of determination of 0.77. The Seq2Seq-LSTM model and Dir-Rec-Update strategy provide accurate LWD curves ahead of the drill bit, enabling advanced drilling decisions and preventing hazards. Advanced technologies such as empirical wavelet transform (EWT) and feature selection enhance the method’s potential for curve ahead-of-time prediction.