Remediation of LWD data lag with hybrid real-time data using self-attention-based encoder-decoder model

This study aims to address the lag issue in Logging While Drilling (LWD) data, which is crucial for real-time decision-making in subsurface resource exploration. The primary objective is to enhance the accuracy of LWD measurements, which suffer from a positional discrepancy due to the tools being positioned several meters behind the drill bit. This lag can lead to delayed responses and misinterpretations during drilling operations. To achieve this, we introduce a novel Self-Attention-Based Encoder-Decoder (SABED) model that compensates for the time/depth lag by utilizing hybrid real-time data, including drilling engineering and LWD data. Our methodology involves training and validating the SABED model using data from the Volve field in the North Sea. The model's architecture is designed to effectively capture the complex relationships between drilling data and LWD measurements. Experimental results demonstrate that the SABED model can predict gamma ray values up to 45 m ahead with a Mean Relative Error (MRE) of less than 1.5% in the primary test well (F7), outperforming conventional sequential deep learning models. Further evaluations on an auxiliary test well (F10) indicate robust performance and generalization capabilities, even with noisy drilling data. The model also meets real-time operational requirements, processing predictions in approximately 4.5 s per 300-step interval (30 m) on both CPU and GPU. Notably, the SABED model maintains predictive accuracy despite data loss, using linear interpolation for missing segments. These findings underscore the SABED model's effectiveness in mitigating LWD data lag and its potential as a valuable tool for real-time geological information prediction. This research contributes novel insights to the field by providing an advanced methodology for improving data accuracy in LWD operations, thereby enhancing decision-making in the petroleum industry.